Analytica Docs - User contributions [en]

Example Models

2018-11-20T17:45:49Z

Jhoy: /* From Controversy to Consensus: California's offshore oil platforms */

[[Category: Models]]
[[category:Doc Status D]]

You may find these example Analytica models useful to see what Analytica can do, and as inspiration or a starting point for your own models. They cover a wide variety of topics and techniques.

These examples supplement the example models that are [[Example Models and Libraries |installed with Analytica]] into the Examples folder.

You can are also invited to contribute your own models as examples. For how to do that, see [[Uploading Example Models]].

__TOC__

==Business Examples==

=== Marginal Abatement Graph ===

:[[image:Marginal abatement heating energy.png]]

This model, along with [http://blog.lumina.com/2015/marginal-abatement/ the accompanying blog article], show how to set up a Marginal Abatement graph in Analytica.

'''Keywords''': Graph methods, carbon price, energy efficiency, climate policy, optimal allocation, budget constraint.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]

=== Solar Panel Analysis ===

:[[image:Solar Panel Analysis.png]]

'''Description:''' Would it be cost effective to install solar panels on the roof of my house? This model explores this question for my situation in San Jose, California. [https://www.youtube.com/watch?v=vhSor_fPIsI An accompanying video] documents the building of this model, and is a good example of the process one goes through when building any decision model.

The model explores how many panels I should install, and what the payoff is in terms of [[NPV|net present value]], [[IRR|Internal rate of return]] and time to recoup cost. It also looks at whether I should postpone the start of the installation to take advantage of rapidly falling PV prices, or cash in on tax credits.

'''Keywords''': Renewable energy, photovoltaics, net present value, internal rate of return, tax credits, agile modeling.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]

=== Items within Budget function ===

'''Description:''' Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget.

'''Keywords:'''

'''Author:''' Max Henrion

'''Download''': [[Media:Items_within_budget.ana|Items within budget.ana]]

=== Grant Exclusion Model ===

'''Description:''' This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant. It could be used by an organization to make decisions as to whether to fiscally-sponsor another organization that will use that fiscal sponsorship to apply for grants, by looking at the effect on the pool of grant recipients overall.

'''Keywords:''' Business analysis

'''Download''': [[Media:Grant_exclusion.ANA|Grant exclusion.ana]]

=== Project Planner ===

:[[Image: Project planner model.png |500px]]

'''Description:''' This is a demo model that shows how to:
* Evaluate a set of R&D projects, including uncertain R&D costs, and uncertain revenues if it leads to release of a commercial product.
* Use multiattribute analysis to compare projects, including a hard attribute -- expected net present value -- and soft attributes -- strategic fit, staff development, and public good will.
* Compare cost, [[NPV]], and multiattribute value for a selected portfolio of projects.
* Generate the best portfolio (ratio of NPV or multiattribute merit to cost) given a R&D budget.

The model linked here is only a test, and to an older version: [[File:Project_priorities_2007_4.0.ANA]]

'''Keywords:''' Business models, cost analysis, net present value (NPV), uncertainty analysis

'''Download:''' [[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]

=== Steel and Aluminum import tariff impact on US trade deficit ===

<center>[[Image:Steel and aluminum tariff model diagram.png|400px]]</center>

'''Description:''' On 2-March-2018, President Trump proposed new import tariffs on steel and aluminum. It seems as if the projected net impacts of these tariffs on the total US trade deficit and US economy depends largely on which news outlets you get your news from. We thought it would be helpful to put together a simple and easy to understand model to estimate of the net impact of these tariffs on the US trade deficit, assuming that no other factors change (e.g., no retaliatory tariffs are enacted by other countries). We wanted something that allows you to understand how its estimates are being derived, with assumptions that can be easily replaced with your own, so that the model itself would be impartial to any particular viewpoint. We want the uncertainties that are inherent in such a simple model to be explicit, so you can see the range of possibilities and not just a single guess. Finally, we wanted the model to be easy to understand fully for non-economists (a group to which we belong, too).

This model accompanied a current event blog post on the Lumina blog: [http://lumina.com/blog/impact-of-trumps-proposed-steel-aluminum-tariffs-on-us-trade-deficit Impact of Trump’s proposed Steel & Aluminum tariffs on US trade deficit]

'''Authors:''' Kimberley Mullins and Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]

=== Tax bracket interpolation ===

<center>[[Image:Tax bracket interpolation.png]]</center>

'''Description:''' Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media: Tax bracket interpolation.ana|Tax bracket interpolation.ana]]

==Data Analysis==

=== Sampling from only feasible points ===

'''Description:''' You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.

This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".

Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.

The instructions for how to use this are in the module description field.

'''Keywords''': Statistics, sampling, Importance sampling, feasibility, Monte Carlo simulation

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]

=== Cross-Validation / Fitting Kernel Functions to Data ===

:[[image:Cross-validated data fit.jpg]]

'''Description:''' When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data. When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.

Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set. Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set. It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.

This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.

Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].

'''Keywords:''' Cross-validation, overfitting, non-linear kernel functions

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Cross-validation example.ana|Cross-validation example.ana]]

=== Statistical Bootstrapping ===

'''Description:''' Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator. The simplest version estimates sampling error by resampling the original data. This model demonstrates how to do this in Analytica.

'''Keywords:''' Bootstrapping, sampling error, re-sampling

'''Download:''' [[media:Bootstrapping.ana|Bootstrapping.ana]]

=== Smooth PDF plots using Kernel Density Estimation ===

:{| border="0"
| [[image:Dens_Est_builtin_pdf.png|frame|Analytica's built-in PDF plot with default settings]]
|
[[image:Dens_Est_Kernel_pdf.png|frame|PDF computed from Kernel Density estimation]]
|}

'''Description:''' This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density. The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin. A kernel density estimator smooths this out, producing a less choppy PDF plot.

This smoothing is built into [[Analytica 4.4]]. You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].

'''Keywords:''' Kernel density estimation, kernel density smoothing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]

=== Output and Input Columns in Same Table ===

:[[image:Output and input columns.png]]

'''Description:''' Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select. Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column. The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.

Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column. This button is presented on the top-level panel. If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.

'''Keywords:''' Data analysis

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Output and input columns.ana|Output and input columns.ana]]

==Decision Analysis==

=== From Controversy to Consensus: California's offshore oil platforms ===

<center>[[image:oilplatform_1.jpg|300px]]</center>

'''Description:''' Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "[http://lumina.com/case-studies/energy-and-power/a-win-win-solution-for-californias-offshore-oil-rigs rigs to reefs]", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.

'''Keywords:''' decision analysis, multi-attribute, offshore platforms, oil and gas, stakeholders, rigs to reefs, decision support, sensitivity analysis

'''Author:''' Max Henrion, Lumina Decision Systems

'''Download:''' [[media:Platform 2018b.ana|Platform2018b.ana]].

=== Retirement plan type comparison ===

<center>[[image:Comparing retirement account types.png|500px]]</center>

'''Description:''' Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.

'''Keywords:''' 401(k), IRA, retirement account, decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Comparing retirement account types.ana|Comparing retirement account types.ana]].
::For a version without the sensitivity analysis part, which has fewer than 100 objects and can thus be modified using [http://lumina.com/products/free101/ Analytica Free 101], you can use this one: [[media:Comparing retirement account types without sensitivity.ana|Comparing retirement account types without sensitivity.ana]].

=== Plane Catching Decision with Expected Value of Including Uncertainty ===

'''Description:''' A simple model to assess what time I should leave my home to catch a plane, with uncertain driving time, walking from parking to gate (including security), and how long I need to be at the gate ahead of scheduled departure time. It uses a loss model based on minutes, assuming I value each extra minute snoozing in bed and set the loss if I miss the plane to 400 of those minutes.

It illustrates the EVIU (expected value of including uncertainty) i.e. the difference in expected value if I make a decision to minimize expected loss instead of decision to minimize time ignoring uncertainty (assuming each distribution is fixed at its mid value). For more details see "The Value of Knowing How Little You Know", Max Henrion, PhD Dissertation, Carnegie Mellon University, 1982.

'''Keywords''': Decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU.

'''Author:''' Max Henrion

'''Download:''' [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]]

=== Marginal Analysis for Control of SO2 emissions ===

'''Description:''' Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S. This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.

'''Keywords:''' Environmental engineering, cost-benefit analysis, marginal analysis

'''Author:''' Surya Swamy

'''Download:''' [[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]

==Dynamic Models==

=== Donor/Presenter Dashboard ===

'''Description:''' This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.

It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term. It also allows for uncertainty in the input parameters.

'''Keywords:''' Dynamic models, Markov processes

'''Download:''' [[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]

=== Regulation of Photosynthesis ===

:[[image:Photosynthesis fluorescence.jpg]]

A model of how photosynthesis is regulated inside a cyanobacteria. As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation. The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed. In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities). In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell. Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.

This model uses node shapes for a different purpose than is normally seen in decision analysis models. In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.

Two models are attached. The first is a bit cleaner, and focused on the core transport chain, as described above. The second is less developed, but is focused more on genetic regulation processes.

'''Keywords:''' Photosynthesis, dynamic models

'''Author:''' Lonnie Chrisman

'''Download:'''
* [[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways
* [[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation.

=== Time-series re-indexing ===

:[[image:Weekly_data_graph_ex.png]]

'''Description:''' This model contains some examples of time-series re-indexing. It is intended to demonstrate some of these basic techniques.

In this example, actual measurements were collected at non-uniform time increments. Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week. The mapping is done using an interpolation. The evenly-spaced data is then used to forecast future behavior. We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change. We then combine the historical data with the forecast on a common index (<code>Week</code>). A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).

'''Keywords:''' Dynamic models, forecasting, time-series re-indexing

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]

==Engineering Examples==

=== Timber Post Compression Load Capacity ===

'''Description:''' Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.

'''Keywords:'''

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:PostCompression.ana|Post Compression Model]]

=== Compression Post Load Calculator ===

'''Description:''' Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.

'''Keywords:''' Compression analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]

=== Daylighting Options in Building Design ===

:[[File:Chapter_9.7-updated.png]]

'''Description:''' A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.

Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.

'''Keywords:''' Engineering, cost-benefits analysis

'''Author:''' Max Henrion

'''Download:''' [[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]

=== California Power Plants ===

'''Description:''' An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table. This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables. The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.

'''Keywords:''' Edit table, Choice menu, pulldown menu, checkbox, Power plants.

'''Download:''' [[Media:California_Power_Plants.ANA|California Power Plants.ana ]]

=== Electrical Generation and Transmission ===

'''Description:''' This model of an electrical network minimizes total cost of generation and transmission. Each node in the network has power generators and consumers (demand). Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero). Each generator has a min and max power and a marginal cost in $/KWh. The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.

''Requires Analytica Optimizer''

'''Keywords:''' Electrical engineering, power generation and transmission

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Electrical Transmission.ana|Electrical Transmission.ana]]

==Fun and Games==

=== Color Map ===

:[[File:Color_map.gif]]

'''Description:''' A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.

'''Keywords:''' Computed cell formatting

'''Author:''' Kimberley Mullins, Lumina Decision Systems

'''Download:''' [[media:color_map.ana|Color map.ana]]

=== 2018 World Cup Soccer final ===

'''Download:''' [[Media:World cup.ana|World cup.ana]]

'''Description:''' On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

=== Image recognition ===

'''Download: ''' [http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]

'''Description:''' Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems. (Analytica implementation). Residual network developed by
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", https://arxiv.org/abs/1512.03385 rXiv:1512.03385]

==Function Examples==

=== Transforming Dimensions by transform matrix, month to quarter ===

'''Description:''' The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as [[Aggregate|aggregation]]. The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.

'''Webinar:''' [[Analytica_User_Group/Past_Topics#The_Aggregate_Function|the Aggregate function]].

'''Keywords''': Aggregation, level of detail, days, weeks, months, quarters, years.

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:Month to quarter.ana|Month to quarter.ana]]

=== Convolution ===

'''Description:''' Convolution is used mostly for signal and systems analysis. It is a way to combine two time series. This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series. The model contains several examples of convolved functions.

A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.

The mathematical definition of the convolution of two time series is the function given by:

:<math>h(t) = \int y(u) z(t-u) dt</math>

'''Keywords:''' Signal analysis, systems analysis

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Convolution.ana|Convolution.ana]]

=== Dependency Tracker Module ===

:[[image:Dependency tracker.jpg]]

'''Description:''' This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another. You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.

The module contains button scripts that change the bevel appearance of nodes in your model. To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>. Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.

In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes. (The result of pressing the "Bevel all from Ancestor to Descendant" button)

'''Keywords:''' Dependency analysis

'''Download:''' [[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]

=== Multi-lingual Influence Diagram ===

:{| border="0"
| [[Image:English-view.png]]
| [[Image:French-view.png]]
|}

'''Description:''' Maintains a single influence diagram with Title and Description attributes in both English and French. With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.

If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description. Similarly if you make a change while viewing French.

'''Keywords:''' Multi-lingual models

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:French-English.ana|French-English.ana]]

=== Extracting Data from an XML file ===

'''Description:''' Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).

'''Keywords:''' Data extraction, xml, DOM parsing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Parsing XML example.ana|Parsing XML example.ana]]

=== Vector Math ===

'''Description:'''
Functions used for computing geospatial coordinates and distances. Includes:
* A cross product of vectors function
* Functions to conversion between spherical and Cartesian coordinates in 3-D
* Functions to compute bearings from one latitude-longitude point to another
* Functions for finding distance between two latitude-longitude points along the great circle.
* Functions for finding the intersection of two great circles

'''Keywords:''' Geospatial analysis, GIS, vector analysis

'''Author:''' Robert D. Brown III, Incite Decision Technologies, LLC

'''Download:''' [[media:Vector Math.ana|Vector Math.ana]]

==Optimizer Examples==

=== Total Allowable Harvest ===

'''Description:''' The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:

:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>

where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.

The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).

Two models are available for download. One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.

'''Keywords:''' Population analysis, dynamic models, optimization analysis

'''Author:''' Models contributed by Pierre Richard

'''Download:'''
* [[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]]
* [[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]]

=== Linearizing a discrete NSP ===

'''Description:''' A cereal formulation model

A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
1) Decision variable is constructed as a constrained Boolean array
2) Prices are defined as piecewise linear curves

'''Keywords:''' product formulation, cereal formulation

'''Author:''' P. Sanford, Lumina Decision Systems

'''Download:''' [[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]

=== Neural Network ===

'''Description:''' A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer. This is essentially an example of non-linear regression. This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.

Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].

'''Keywords:''' Feed-forward neural networks, optimization analysis

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Neural-Network.ana|Neural Network.ana]]

==Risk Analysis==

=== Earthquake Expenses ===

'''Description:''' An example of risk analysis with time-dependence and costs shifted over time.

Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes. This simplified demo model can be used to answer questions such as:
* What is the probability of more than one quake in a specific 10 year period.
* What is the probability that in my time window my costs exceed $X?

Assumptions in this model:
* Earthquakes are Poisson events with mean rate of once every 10 years.
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time: 20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.

'''Keywords:''' Risk analysis, cost analysis

'''Download''': [[media:Earthquake expenses.ana|Earthquake expenses.ana]]

=== Loan Policy Selection ===

'''Description:''' A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge. The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating. The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution. Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.

'''Best used with Analytica Optimizer'''

'''Keywords:''' Creditworthiness, credit rating, default risk, risk analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Loan policy selection.ANA|Loan policy selection.ana]]

=== Inherent and Residual Risk Simulation ===

:[[File:Prob of Exceeding Loss.png]]

'''Description:''' The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].

'''Keywords:''' Cybersecurity risk, loss exceedance curve, simulation

'''Author:''' Kim Mullins

'''Download:''' [[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]

== Graphing examples ==
=== Red or blue state ===
[[image:Red_or_blue_state.png|600px]]

'''Download:''' [[media:Red State Blue State plot.ana]]

'''Description:''' This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

==See Also==
* [[New examples]]
* [[Additional libraries]]
* [[Uploading Example Models]]
* [[Example Models and Libraries]]
* [[Using Add Module... to import a Model file]]
* [[Import a module or library]]
* [[Tutorial: Sharing a model with ACP]]
* [[Obfuscated and Browse-Only Models]]
* [[Filed modules and libraries]]
* [[Working with Models, Modules, and Files in ADE]]
* [[Combining models into an integrated model]]
* [[Model file formats]]
* [[Model Licensing]]

Example Models

2018-11-16T21:47:16Z

Jhoy:

[[Category: Models]]
[[category:Doc Status D]]

You may find these example Analytica models useful to see what Analytica can do, and as inspiration or a starting point for your own models. They cover a wide variety of topics and techniques.

These examples supplement the example models that are [[Example Models and Libraries |installed with Analytica]] into the Examples folder.

You can are also invited to contribute your own models as examples. For how to do that, see [[Uploading Example Models]].

__TOC__

==Business Examples==

=== Marginal Abatement Graph ===

:[[image:Marginal abatement heating energy.png]]

This model, along with [http://blog.lumina.com/2015/marginal-abatement/ the accompanying blog article], show how to set up a Marginal Abatement graph in Analytica.

'''Keywords''': Graph methods, carbon price, energy efficiency, climate policy, optimal allocation, budget constraint.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]

=== Solar Panel Analysis ===

:[[image:Solar Panel Analysis.png]]

'''Description:''' Would it be cost effective to install solar panels on the roof of my house? This model explores this question for my situation in San Jose, California. [https://www.youtube.com/watch?v=vhSor_fPIsI An accompanying video] documents the building of this model, and is a good example of the process one goes through when building any decision model.

The model explores how many panels I should install, and what the payoff is in terms of [[NPV|net present value]], [[IRR|Internal rate of return]] and time to recoup cost. It also looks at whether I should postpone the start of the installation to take advantage of rapidly falling PV prices, or cash in on tax credits.

'''Keywords''': Renewable energy, photovoltaics, net present value, internal rate of return, tax credits, agile modeling.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]

=== Items within Budget function ===

'''Description:''' Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget.

'''Keywords:'''

'''Author:''' Max Henrion

'''Download''': [[Media:Items_within_budget.ana|Items within budget.ana]]

=== Grant Exclusion Model ===

'''Description:''' This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant. It could be used by an organization to make decisions as to whether to fiscally-sponsor another organization that will use that fiscal sponsorship to apply for grants, by looking at the effect on the pool of grant recipients overall.

'''Keywords:''' Business analysis

'''Download''': [[Media:Grant_exclusion.ANA|Grant exclusion.ana]]

=== Project Planner ===

:[[Image: Project planner model.png |500px]]

'''Description:''' This is a demo model that shows how to:
* Evaluate a set of R&D projects, including uncertain R&D costs, and uncertain revenues if it leads to release of a commercial product.
* Use multiattribute analysis to compare projects, including a hard attribute -- expected net present value -- and soft attributes -- strategic fit, staff development, and public good will.
* Compare cost, [[NPV]], and multiattribute value for a selected portfolio of projects.
* Generate the best portfolio (ratio of NPV or multiattribute merit to cost) given a R&D budget.

The model linked here is only a test, and to an older version: [[File:Project_priorities_2007_4.0.ANA]]

'''Keywords:''' Business models, cost analysis, net present value (NPV), uncertainty analysis

'''Download:''' [[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]

=== Steel and Aluminum import tariff impact on US trade deficit ===

<center>[[Image:Steel and aluminum tariff model diagram.png|400px]]</center>

'''Description:''' On 2-March-2018, President Trump proposed new import tariffs on steel and aluminum. It seems as if the projected net impacts of these tariffs on the total US trade deficit and US economy depends largely on which news outlets you get your news from. We thought it would be helpful to put together a simple and easy to understand model to estimate of the net impact of these tariffs on the US trade deficit, assuming that no other factors change (e.g., no retaliatory tariffs are enacted by other countries). We wanted something that allows you to understand how its estimates are being derived, with assumptions that can be easily replaced with your own, so that the model itself would be impartial to any particular viewpoint. We want the uncertainties that are inherent in such a simple model to be explicit, so you can see the range of possibilities and not just a single guess. Finally, we wanted the model to be easy to understand fully for non-economists (a group to which we belong, too).

This model accompanied a current event blog post on the Lumina blog: [http://lumina.com/blog/impact-of-trumps-proposed-steel-aluminum-tariffs-on-us-trade-deficit Impact of Trump’s proposed Steel & Aluminum tariffs on US trade deficit]

'''Authors:''' Kimberley Mullins and Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]

=== Tax bracket interpolation ===

<center>[[Image:Tax bracket interpolation.png]]</center>

'''Description:''' Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media: Tax bracket interpolation.ana|Tax bracket interpolation.ana]]

==Data Analysis==

=== Sampling from only feasible points ===

'''Description:''' You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.

This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".

Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.

The instructions for how to use this are in the module description field.

'''Keywords''': Statistics, sampling, Importance sampling, feasibility, Monte Carlo simulation

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]

=== Cross-Validation / Fitting Kernel Functions to Data ===

:[[image:Cross-validated data fit.jpg]]

'''Description:''' When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data. When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.

Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set. Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set. It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.

This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.

Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].

'''Keywords:''' Cross-validation, overfitting, non-linear kernel functions

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Cross-validation example.ana|Cross-validation example.ana]]

=== Statistical Bootstrapping ===

'''Description:''' Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator. The simplest version estimates sampling error by resampling the original data. This model demonstrates how to do this in Analytica.

'''Keywords:''' Bootstrapping, sampling error, re-sampling

'''Download:''' [[media:Bootstrapping.ana|Bootstrapping.ana]]

=== Smooth PDF plots using Kernel Density Estimation ===

:{| border="0"
| [[image:Dens_Est_builtin_pdf.png|frame|Analytica's built-in PDF plot with default settings]]
|
[[image:Dens_Est_Kernel_pdf.png|frame|PDF computed from Kernel Density estimation]]
|}

'''Description:''' This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density. The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin. A kernel density estimator smooths this out, producing a less choppy PDF plot.

This smoothing is built into [[Analytica 4.4]]. You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].

'''Keywords:''' Kernel density estimation, kernel density smoothing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]

=== Output and Input Columns in Same Table ===

:[[image:Output and input columns.png]]

'''Description:''' Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select. Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column. The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.

Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column. This button is presented on the top-level panel. If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.

'''Keywords:''' Data analysis

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Output and input columns.ana|Output and input columns.ana]]

==Decision Analysis==

=== From Controversy to Consensus: California's offshore oil platforms ===

<center>[[image:oilplatform_1.jpg|300px]]</center>

'''Description:''' Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "rigs to reefs", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.

'''Keywords:''' decision analysis, uncertainty, sensitivity analysis,

'''Author:''' Max Henrion, Lumina Decision Systems

'''Download:''' [[media:Platform 2018b.ana|Platform2018b.ana]].

=== Retirement plan type comparison ===

<center>[[image:Comparing retirement account types.png|500px]]</center>

'''Description:''' Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.

'''Keywords:''' 401(k), IRA, retirement account, decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Comparing retirement account types.ana|Comparing retirement account types.ana]].
::For a version without the sensitivity analysis part, which has fewer than 100 objects and can thus be modified using [http://lumina.com/products/free101/ Analytica Free 101], you can use this one: [[media:Comparing retirement account types without sensitivity.ana|Comparing retirement account types without sensitivity.ana]].

=== Plane Catching Decision with Expected Value of Including Uncertainty ===

'''Description:''' A simple model to assess what time I should leave my home to catch a plane, with uncertain driving time, walking from parking to gate (including security), and how long I need to be at the gate ahead of scheduled departure time. It uses a loss model based on minutes, assuming I value each extra minute snoozing in bed and set the loss if I miss the plane to 400 of those minutes.

It illustrates the EVIU (expected value of including uncertainty) i.e. the difference in expected value if I make a decision to minimize expected loss instead of decision to minimize time ignoring uncertainty (assuming each distribution is fixed at its mid value). For more details see "The Value of Knowing How Little You Know", Max Henrion, PhD Dissertation, Carnegie Mellon University, 1982.

'''Keywords''': Decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU.

'''Author:''' Max Henrion

'''Download:''' [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]]

=== Marginal Analysis for Control of SO2 emissions ===

'''Description:''' Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S. This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.

'''Keywords:''' Environmental engineering, cost-benefit analysis, marginal analysis

'''Author:''' Surya Swamy

'''Download:''' [[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]

==Dynamic Models==

=== Donor/Presenter Dashboard ===

'''Description:''' This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.

It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term. It also allows for uncertainty in the input parameters.

'''Keywords:''' Dynamic models, Markov processes

'''Download:''' [[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]

=== Regulation of Photosynthesis ===

:[[image:Photosynthesis fluorescence.jpg]]

A model of how photosynthesis is regulated inside a cyanobacteria. As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation. The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed. In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities). In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell. Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.

This model uses node shapes for a different purpose than is normally seen in decision analysis models. In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.

Two models are attached. The first is a bit cleaner, and focused on the core transport chain, as described above. The second is less developed, but is focused more on genetic regulation processes.

'''Keywords:''' Photosynthesis, dynamic models

'''Author:''' Lonnie Chrisman

'''Download:'''
* [[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways
* [[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation.

=== Time-series re-indexing ===

:[[image:Weekly_data_graph_ex.png]]

'''Description:''' This model contains some examples of time-series re-indexing. It is intended to demonstrate some of these basic techniques.

In this example, actual measurements were collected at non-uniform time increments. Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week. The mapping is done using an interpolation. The evenly-spaced data is then used to forecast future behavior. We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change. We then combine the historical data with the forecast on a common index (<code>Week</code>). A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).

'''Keywords:''' Dynamic models, forecasting, time-series re-indexing

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]

==Engineering Examples==

=== Timber Post Compression Load Capacity ===

'''Description:''' Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.

'''Keywords:'''

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:PostCompression.ana|Post Compression Model]]

=== Compression Post Load Calculator ===

'''Description:''' Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.

'''Keywords:''' Compression analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]

=== Daylighting Options in Building Design ===

:[[File:Chapter_9.7-updated.png]]

'''Description:''' A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.

Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.

'''Keywords:''' Engineering, cost-benefits analysis

'''Author:''' Max Henrion

'''Download:''' [[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]

=== California Power Plants ===

'''Description:''' An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table. This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables. The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.

'''Keywords:''' Edit table, Choice menu, pulldown menu, checkbox, Power plants.

'''Download:''' [[Media:California_Power_Plants.ANA|California Power Plants.ana ]]

=== Electrical Generation and Transmission ===

'''Description:''' This model of an electrical network minimizes total cost of generation and transmission. Each node in the network has power generators and consumers (demand). Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero). Each generator has a min and max power and a marginal cost in $/KWh. The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.

''Requires Analytica Optimizer''

'''Keywords:''' Electrical engineering, power generation and transmission

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Electrical Transmission.ana|Electrical Transmission.ana]]

==Fun and Games==

=== Color Map ===

:[[File:Color_map.gif]]

'''Description:''' A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.

'''Keywords:''' Computed cell formatting

'''Author:''' Kimberley Mullins, Lumina Decision Systems

'''Download:''' [[media:color_map.ana|Color map.ana]]

=== 2018 World Cup Soccer final ===

'''Download:''' [[Media:World cup.ana|World cup.ana]]

'''Description:''' On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

=== Image recognition ===

'''Download: ''' [http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]

'''Description:''' Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems. (Analytica implementation). Residual network developed by
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", https://arxiv.org/abs/1512.03385 rXiv:1512.03385]

==Function Examples==

=== Transforming Dimensions by transform matrix, month to quarter ===

'''Description:''' The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as [[Aggregate|aggregation]]. The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.

'''Webinar:''' [[Analytica_User_Group/Past_Topics#The_Aggregate_Function|the Aggregate function]].

'''Keywords''': Aggregation, level of detail, days, weeks, months, quarters, years.

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:Month to quarter.ana|Month to quarter.ana]]

=== Convolution ===

'''Description:''' Convolution is used mostly for signal and systems analysis. It is a way to combine two time series. This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series. The model contains several examples of convolved functions.

A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.

The mathematical definition of the convolution of two time series is the function given by:

:<math>h(t) = \int y(u) z(t-u) dt</math>

'''Keywords:''' Signal analysis, systems analysis

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Convolution.ana|Convolution.ana]]

=== Dependency Tracker Module ===

:[[image:Dependency tracker.jpg]]

'''Description:''' This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another. You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.

The module contains button scripts that change the bevel appearance of nodes in your model. To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>. Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.

In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes. (The result of pressing the "Bevel all from Ancestor to Descendant" button)

'''Keywords:''' Dependency analysis

'''Download:''' [[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]

=== Multi-lingual Influence Diagram ===

:{| border="0"
| [[Image:English-view.png]]
| [[Image:French-view.png]]
|}

'''Description:''' Maintains a single influence diagram with Title and Description attributes in both English and French. With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.

If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description. Similarly if you make a change while viewing French.

'''Keywords:''' Multi-lingual models

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:French-English.ana|French-English.ana]]

=== Extracting Data from an XML file ===

'''Description:''' Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).

'''Keywords:''' Data extraction, xml, DOM parsing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Parsing XML example.ana|Parsing XML example.ana]]

=== Vector Math ===

'''Description:'''
Functions used for computing geospatial coordinates and distances. Includes:
* A cross product of vectors function
* Functions to conversion between spherical and Cartesian coordinates in 3-D
* Functions to compute bearings from one latitude-longitude point to another
* Functions for finding distance between two latitude-longitude points along the great circle.
* Functions for finding the intersection of two great circles

'''Keywords:''' Geospatial analysis, GIS, vector analysis

'''Author:''' Robert D. Brown III, Incite Decision Technologies, LLC

'''Download:''' [[media:Vector Math.ana|Vector Math.ana]]

==Optimizer Examples==

=== Total Allowable Harvest ===

'''Description:''' The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:

:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>

where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.

The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).

Two models are available for download. One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.

'''Keywords:''' Population analysis, dynamic models, optimization analysis

'''Author:''' Models contributed by Pierre Richard

'''Download:'''
* [[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]]
* [[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]]

=== Linearizing a discrete NSP ===

'''Description:''' A cereal formulation model

A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
1) Decision variable is constructed as a constrained Boolean array
2) Prices are defined as piecewise linear curves

'''Keywords:''' product formulation, cereal formulation

'''Author:''' P. Sanford, Lumina Decision Systems

'''Download:''' [[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]

=== Neural Network ===

'''Description:''' A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer. This is essentially an example of non-linear regression. This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.

Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].

'''Keywords:''' Feed-forward neural networks, optimization analysis

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Neural-Network.ana|Neural Network.ana]]

==Risk Analysis==

=== Earthquake Expenses ===

'''Description:''' An example of risk analysis with time-dependence and costs shifted over time.

Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes. This simplified demo model can be used to answer questions such as:
* What is the probability of more than one quake in a specific 10 year period.
* What is the probability that in my time window my costs exceed $X?

Assumptions in this model:
* Earthquakes are Poisson events with mean rate of once every 10 years.
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time: 20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.

'''Keywords:''' Risk analysis, cost analysis

'''Download''': [[media:Earthquake expenses.ana|Earthquake expenses.ana]]

=== Loan Policy Selection ===

'''Description:''' A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge. The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating. The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution. Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.

'''Best used with Analytica Optimizer'''

'''Keywords:''' Creditworthiness, credit rating, default risk, risk analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Loan policy selection.ANA|Loan policy selection.ana]]

=== Inherent and Residual Risk Simulation ===

:[[File:Prob of Exceeding Loss.png]]

'''Description:''' The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].

'''Keywords:''' Cybersecurity risk, loss exceedance curve, simulation

'''Author:''' Kim Mullins

'''Download:''' [[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]

== Graphing examples ==
=== Red or blue state ===
[[image:Red_or_blue_state.png|600px]]

'''Download:''' [[media:Red State Blue State plot.ana]]

'''Description:''' This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

==See Also==
* [[New examples]]
* [[Additional libraries]]
* [[Uploading Example Models]]
* [[Example Models and Libraries]]
* [[Using Add Module... to import a Model file]]
* [[Import a module or library]]
* [[Tutorial: Sharing a model with ACP]]
* [[Obfuscated and Browse-Only Models]]
* [[Filed modules and libraries]]
* [[Working with Models, Modules, and Files in ADE]]
* [[Combining models into an integrated model]]
* [[Model file formats]]
* [[Model Licensing]]

Example Models

2018-11-16T19:00:35Z

Jhoy:

[[Category: Models]]
[[category:Doc Status D]]

You may find these example Analytica models useful to see what Analytica can do, and as inspiration or a starting point for your own models. They cover a wide variety of topics and techniques.

These examples supplement the example models that are [[Example Models and Libraries |installed with Analytica]] into the Examples folder.

You can are also invited to contribute your own models as examples. For how to do that, see [[Uploading Example Models]].

__TOC__

==Business Examples==

=== Marginal Abatement Graph ===

:[[image:Marginal abatement heating energy.png]]

This model, along with [http://blog.lumina.com/2015/marginal-abatement/ the accompanying blog article], show how to set up a Marginal Abatement graph in Analytica.

'''Keywords''': Graph methods, carbon price, energy efficiency, climate policy, optimal allocation, budget constraint.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]

=== Solar Panel Analysis ===

:[[image:Solar Panel Analysis.png]]

'''Description:''' Would it be cost effective to install solar panels on the roof of my house? This model explores this question for my situation in San Jose, California. [https://www.youtube.com/watch?v=vhSor_fPIsI An accompanying video] documents the building of this model, and is a good example of the process one goes through when building any decision model.

The model explores how many panels I should install, and what the payoff is in terms of [[NPV|net present value]], [[IRR|Internal rate of return]] and time to recoup cost. It also looks at whether I should postpone the start of the installation to take advantage of rapidly falling PV prices, or cash in on tax credits.

'''Keywords''': Renewable energy, photovoltaics, net present value, internal rate of return, tax credits, agile modeling.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]

=== Items within Budget function ===

'''Description:''' Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget.

'''Keywords:'''

'''Author:''' Max Henrion

'''Download''': [[Media:Items_within_budget.ana|Items within budget.ana]]

=== Grant Exclusion Model ===

'''Description:''' This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant. It could be used by an organization to make decisions as to whether to fiscally-sponsor another organization that will use that fiscal sponsorship to apply for grants, by looking at the effect on the pool of grant recipients overall.

'''Keywords:''' Business analysis

'''Download''': [[Media:Grant_exclusion.ANA|Grant exclusion.ana]]

=== Project Planner ===

:[[Image: Project planner model.png |500px]]

'''Description:''' This is a demo model that shows how to:
* Evaluate a set of R&D projects, including uncertain R&D costs, and uncertain revenues if it leads to release of a commercial product.
* Use multiattribute analysis to compare projects, including a hard attribute -- expected net present value -- and soft attributes -- strategic fit, staff development, and public good will.
* Compare cost, [[NPV]], and multiattribute value for a selected portfolio of projects.
* Generate the best portfolio (ratio of NPV or multiattribute merit to cost) given a R&D budget.

The model linked here is only a test, and to an older version: [[File:Project_priorities_2007_4.0.ANA]]

'''Keywords:''' Business models, cost analysis, net present value (NPV), uncertainty analysis

'''Download:''' [[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]

=== Steel and Aluminum import tariff impact on US trade deficit ===

<center>[[Image:Steel and aluminum tariff model diagram.png|400px]]</center>

'''Description:''' On 2-March-2018, President Trump proposed new import tariffs on steel and aluminum. It seems as if the projected net impacts of these tariffs on the total US trade deficit and US economy depends largely on which news outlets you get your news from. We thought it would be helpful to put together a simple and easy to understand model to estimate of the net impact of these tariffs on the US trade deficit, assuming that no other factors change (e.g., no retaliatory tariffs are enacted by other countries). We wanted something that allows you to understand how its estimates are being derived, with assumptions that can be easily replaced with your own, so that the model itself would be impartial to any particular viewpoint. We want the uncertainties that are inherent in such a simple model to be explicit, so you can see the range of possibilities and not just a single guess. Finally, we wanted the model to be easy to understand fully for non-economists (a group to which we belong, too).

This model accompanied a current event blog post on the Lumina blog: [http://lumina.com/blog/impact-of-trumps-proposed-steel-aluminum-tariffs-on-us-trade-deficit Impact of Trump’s proposed Steel & Aluminum tariffs on US trade deficit]

'''Authors:''' Kimberley Mullins and Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]

=== Tax bracket interpolation ===

<center>[[Image:Tax bracket interpolation.png]]</center>

'''Description:''' Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media: Tax bracket interpolation.ana|Tax bracket interpolation.ana]]

==Data Analysis==

=== Sampling from only feasible points ===

'''Description:''' You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.

This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".

Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.

The instructions for how to use this are in the module description field.

'''Keywords''': Statistics, sampling, Importance sampling, feasibility, Monte Carlo simulation

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]

=== Cross-Validation / Fitting Kernel Functions to Data ===

:[[image:Cross-validated data fit.jpg]]

'''Description:''' When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data. When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.

Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set. Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set. It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.

This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.

Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].

'''Keywords:''' Cross-validation, overfitting, non-linear kernel functions

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Cross-validation example.ana|Cross-validation example.ana]]

=== Statistical Bootstrapping ===

'''Description:''' Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator. The simplest version estimates sampling error by resampling the original data. This model demonstrates how to do this in Analytica.

'''Keywords:''' Bootstrapping, sampling error, re-sampling

'''Download:''' [[media:Bootstrapping.ana|Bootstrapping.ana]]

=== Smooth PDF plots using Kernel Density Estimation ===

:{| border="0"
| [[image:Dens_Est_builtin_pdf.png|frame|Analytica's built-in PDF plot with default settings]]
|
[[image:Dens_Est_Kernel_pdf.png|frame|PDF computed from Kernel Density estimation]]
|}

'''Description:''' This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density. The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin. A kernel density estimator smooths this out, producing a less choppy PDF plot.

This smoothing is built into [[Analytica 4.4]]. You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].

'''Keywords:''' Kernel density estimation, kernel density smoothing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]

=== Output and Input Columns in Same Table ===

:[[image:Output and input columns.png]]

'''Description:''' Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select. Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column. The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.

Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column. This button is presented on the top-level panel. If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.

'''Keywords:''' Data analysis

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Output and input columns.ana|Output and input columns.ana]]

==Decision Analysis==

=== From Controversy to Consensus: California's offshore oil platforms ===

<center>[[image:oilplatform_1.jpg|500px]]</center>

'''Description:''' Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "rigs to reefs", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.

'''Keywords:''' decision analysis, uncertainty, sensitivity analysis,

'''Author:''' Max Henrion, Lumina Decision Systems

'''Download:''' [[media:Platform 2018b.ana|Platform2018b.ana]].

=== Retirement plan type comparison ===

<center>[[image:Comparing retirement account types.png|500px]]</center>

'''Description:''' Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.

'''Keywords:''' 401(k), IRA, retirement account, decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Comparing retirement account types.ana|Comparing retirement account types.ana]].
::For a version without the sensitivity analysis part, which has fewer than 100 objects and can thus be modified using [http://lumina.com/products/free101/ Analytica Free 101], you can use this one: [[media:Comparing retirement account types without sensitivity.ana|Comparing retirement account types without sensitivity.ana]].

=== Plane Catching Decision with Expected Value of Including Uncertainty ===

'''Description:''' A simple model to assess what time I should leave my home to catch a plane, with uncertain driving time, walking from parking to gate (including security), and how long I need to be at the gate ahead of scheduled departure time. It uses a loss model based on minutes, assuming I value each extra minute snoozing in bed and set the loss if I miss the plane to 400 of those minutes.

It illustrates the EVIU (expected value of including uncertainty) i.e. the difference in expected value if I make a decision to minimize expected loss instead of decision to minimize time ignoring uncertainty (assuming each distribution is fixed at its mid value). For more details see "The Value of Knowing How Little You Know", Max Henrion, PhD Dissertation, Carnegie Mellon University, 1982.

'''Keywords''': Decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU.

'''Author:''' Max Henrion

'''Download:''' [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]]

=== Marginal Analysis for Control of SO2 emissions ===

'''Description:''' Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S. This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.

'''Keywords:''' Environmental engineering, cost-benefit analysis, marginal analysis

'''Author:''' Surya Swamy

'''Download:''' [[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]

==Dynamic Models==

=== Donor/Presenter Dashboard ===

'''Description:''' This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.

It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term. It also allows for uncertainty in the input parameters.

'''Keywords:''' Dynamic models, Markov processes

'''Download:''' [[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]

=== Regulation of Photosynthesis ===

:[[image:Photosynthesis fluorescence.jpg]]

A model of how photosynthesis is regulated inside a cyanobacteria. As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation. The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed. In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities). In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell. Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.

This model uses node shapes for a different purpose than is normally seen in decision analysis models. In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.

Two models are attached. The first is a bit cleaner, and focused on the core transport chain, as described above. The second is less developed, but is focused more on genetic regulation processes.

'''Keywords:''' Photosynthesis, dynamic models

'''Author:''' Lonnie Chrisman

'''Download:'''
* [[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways
* [[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation.

=== Time-series re-indexing ===

:[[image:Weekly_data_graph_ex.png]]

'''Description:''' This model contains some examples of time-series re-indexing. It is intended to demonstrate some of these basic techniques.

In this example, actual measurements were collected at non-uniform time increments. Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week. The mapping is done using an interpolation. The evenly-spaced data is then used to forecast future behavior. We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change. We then combine the historical data with the forecast on a common index (<code>Week</code>). A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).

'''Keywords:''' Dynamic models, forecasting, time-series re-indexing

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]

==Engineering Examples==

=== Timber Post Compression Load Capacity ===

'''Description:''' Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.

'''Keywords:'''

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:PostCompression.ana|Post Compression Model]]

=== Compression Post Load Calculator ===

'''Description:''' Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.

'''Keywords:''' Compression analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]

=== Daylighting Options in Building Design ===

:[[File:Chapter_9.7-updated.png]]

'''Description:''' A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.

Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.

'''Keywords:''' Engineering, cost-benefits analysis

'''Author:''' Max Henrion

'''Download:''' [[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]

=== California Power Plants ===

'''Description:''' An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table. This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables. The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.

'''Keywords:''' Edit table, Choice menu, pulldown menu, checkbox, Power plants.

'''Download:''' [[Media:California_Power_Plants.ANA|California Power Plants.ana ]]

=== Electrical Generation and Transmission ===

'''Description:''' This model of an electrical network minimizes total cost of generation and transmission. Each node in the network has power generators and consumers (demand). Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero). Each generator has a min and max power and a marginal cost in $/KWh. The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.

''Requires Analytica Optimizer''

'''Keywords:''' Electrical engineering, power generation and transmission

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Electrical Transmission.ana|Electrical Transmission.ana]]

==Fun and Games==

=== Color Map ===

:[[File:Color_map.gif]]

'''Description:''' A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.

'''Keywords:''' Computed cell formatting

'''Author:''' Kimberley Mullins, Lumina Decision Systems

'''Download:''' [[media:color_map.ana|Color map.ana]]

=== 2018 World Cup Soccer final ===

'''Download:''' [[Media:World cup.ana|World cup.ana]]

'''Description:''' On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

=== Image recognition ===

'''Download: ''' [http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]

'''Description:''' Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems. (Analytica implementation). Residual network developed by
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", https://arxiv.org/abs/1512.03385 rXiv:1512.03385]

==Function Examples==

=== Transforming Dimensions by transform matrix, month to quarter ===

'''Description:''' The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as [[Aggregate|aggregation]]. The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.

'''Webinar:''' [[Analytica_User_Group/Past_Topics#The_Aggregate_Function|the Aggregate function]].

'''Keywords''': Aggregation, level of detail, days, weeks, months, quarters, years.

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:Month to quarter.ana|Month to quarter.ana]]

=== Convolution ===

'''Description:''' Convolution is used mostly for signal and systems analysis. It is a way to combine two time series. This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series. The model contains several examples of convolved functions.

A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.

The mathematical definition of the convolution of two time series is the function given by:

:<math>h(t) = \int y(u) z(t-u) dt</math>

'''Keywords:''' Signal analysis, systems analysis

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Convolution.ana|Convolution.ana]]

=== Dependency Tracker Module ===

:[[image:Dependency tracker.jpg]]

'''Description:''' This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another. You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.

The module contains button scripts that change the bevel appearance of nodes in your model. To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>. Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.

In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes. (The result of pressing the "Bevel all from Ancestor to Descendant" button)

'''Keywords:''' Dependency analysis

'''Download:''' [[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]

=== Multi-lingual Influence Diagram ===

:{| border="0"
| [[Image:English-view.png]]
| [[Image:French-view.png]]
|}

'''Description:''' Maintains a single influence diagram with Title and Description attributes in both English and French. With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.

If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description. Similarly if you make a change while viewing French.

'''Keywords:''' Multi-lingual models

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:French-English.ana|French-English.ana]]

=== Extracting Data from an XML file ===

'''Description:''' Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).

'''Keywords:''' Data extraction, xml, DOM parsing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Parsing XML example.ana|Parsing XML example.ana]]

=== Vector Math ===

'''Description:'''
Functions used for computing geospatial coordinates and distances. Includes:
* A cross product of vectors function
* Functions to conversion between spherical and Cartesian coordinates in 3-D
* Functions to compute bearings from one latitude-longitude point to another
* Functions for finding distance between two latitude-longitude points along the great circle.
* Functions for finding the intersection of two great circles

'''Keywords:''' Geospatial analysis, GIS, vector analysis

'''Author:''' Robert D. Brown III, Incite Decision Technologies, LLC

'''Download:''' [[media:Vector Math.ana|Vector Math.ana]]

==Optimizer Examples==

=== Total Allowable Harvest ===

'''Description:''' The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:

:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>

where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.

The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).

Two models are available for download. One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.

'''Keywords:''' Population analysis, dynamic models, optimization analysis

'''Author:''' Models contributed by Pierre Richard

'''Download:'''
* [[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]]
* [[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]]

=== Linearizing a discrete NSP ===

'''Description:''' A cereal formulation model

A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
1) Decision variable is constructed as a constrained Boolean array
2) Prices are defined as piecewise linear curves

'''Keywords:''' product formulation, cereal formulation

'''Author:''' P. Sanford, Lumina Decision Systems

'''Download:''' [[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]

=== Neural Network ===

'''Description:''' A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer. This is essentially an example of non-linear regression. This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.

Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].

'''Keywords:''' Feed-forward neural networks, optimization analysis

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Neural-Network.ana|Neural Network.ana]]

==Risk Analysis==

=== Earthquake Expenses ===

'''Description:''' An example of risk analysis with time-dependence and costs shifted over time.

Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes. This simplified demo model can be used to answer questions such as:
* What is the probability of more than one quake in a specific 10 year period.
* What is the probability that in my time window my costs exceed $X?

Assumptions in this model:
* Earthquakes are Poisson events with mean rate of once every 10 years.
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time: 20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.

'''Keywords:''' Risk analysis, cost analysis

'''Download''': [[media:Earthquake expenses.ana|Earthquake expenses.ana]]

=== Loan Policy Selection ===

'''Description:''' A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge. The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating. The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution. Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.

'''Best used with Analytica Optimizer'''

'''Keywords:''' Creditworthiness, credit rating, default risk, risk analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Loan policy selection.ANA|Loan policy selection.ana]]

=== Inherent and Residual Risk Simulation ===

:[[File:Prob of Exceeding Loss.png]]

'''Description:''' The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].

'''Keywords:''' Cybersecurity risk, loss exceedance curve, simulation

'''Author:''' Kim Mullins

'''Download:''' [[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]

== Graphing examples ==
=== Red or blue state ===
[[image:Red_or_blue_state.png|600px]]

'''Download:''' [[media:Red State Blue State plot.ana]]

'''Description:''' This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

==See Also==
* [[New examples]]
* [[Additional libraries]]
* [[Uploading Example Models]]
* [[Example Models and Libraries]]
* [[Using Add Module... to import a Model file]]
* [[Import a module or library]]
* [[Tutorial: Sharing a model with ACP]]
* [[Obfuscated and Browse-Only Models]]
* [[Filed modules and libraries]]
* [[Working with Models, Modules, and Files in ADE]]
* [[Combining models into an integrated model]]
* [[Model file formats]]
* [[Model Licensing]]

File:Platform 2018b.ana

2018-11-16T18:58:13Z

Jhoy:

Example Models

2018-11-16T18:57:36Z

Jhoy:

[[Category: Models]]
[[category:Doc Status D]]

You may find these example Analytica models useful to see what Analytica can do, and as inspiration or a starting point for your own models. They cover a wide variety of topics and techniques.

These examples supplement the example models that are [[Example Models and Libraries |installed with Analytica]] into the Examples folder.

You can are also invited to contribute your own models as examples. For how to do that, see [[Uploading Example Models]].

__TOC__

==Business Examples==

=== Marginal Abatement Graph ===

:[[image:Marginal abatement heating energy.png]]

This model, along with [http://blog.lumina.com/2015/marginal-abatement/ the accompanying blog article], show how to set up a Marginal Abatement graph in Analytica.

'''Keywords''': Graph methods, carbon price, energy efficiency, climate policy, optimal allocation, budget constraint.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]

=== Solar Panel Analysis ===

:[[image:Solar Panel Analysis.png]]

'''Description:''' Would it be cost effective to install solar panels on the roof of my house? This model explores this question for my situation in San Jose, California. [https://www.youtube.com/watch?v=vhSor_fPIsI An accompanying video] documents the building of this model, and is a good example of the process one goes through when building any decision model.

The model explores how many panels I should install, and what the payoff is in terms of [[NPV|net present value]], [[IRR|Internal rate of return]] and time to recoup cost. It also looks at whether I should postpone the start of the installation to take advantage of rapidly falling PV prices, or cash in on tax credits.

'''Keywords''': Renewable energy, photovoltaics, net present value, internal rate of return, tax credits, agile modeling.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]

=== Items within Budget function ===

'''Description:''' Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget.

'''Keywords:'''

'''Author:''' Max Henrion

'''Download''': [[Media:Items_within_budget.ana|Items within budget.ana]]

=== Grant Exclusion Model ===

'''Description:''' This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant. It could be used by an organization to make decisions as to whether to fiscally-sponsor another organization that will use that fiscal sponsorship to apply for grants, by looking at the effect on the pool of grant recipients overall.

'''Keywords:''' Business analysis

'''Download''': [[Media:Grant_exclusion.ANA|Grant exclusion.ana]]

=== Project Planner ===

:[[Image: Project planner model.png |500px]]

'''Description:''' This is a demo model that shows how to:
* Evaluate a set of R&D projects, including uncertain R&D costs, and uncertain revenues if it leads to release of a commercial product.
* Use multiattribute analysis to compare projects, including a hard attribute -- expected net present value -- and soft attributes -- strategic fit, staff development, and public good will.
* Compare cost, [[NPV]], and multiattribute value for a selected portfolio of projects.
* Generate the best portfolio (ratio of NPV or multiattribute merit to cost) given a R&D budget.

The model linked here is only a test, and to an older version: [[File:Project_priorities_2007_4.0.ANA]]

'''Keywords:''' Business models, cost analysis, net present value (NPV), uncertainty analysis

'''Download:''' [[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]

=== Steel and Aluminum import tariff impact on US trade deficit ===

<center>[[Image:Steel and aluminum tariff model diagram.png|400px]]</center>

'''Description:''' On 2-March-2018, President Trump proposed new import tariffs on steel and aluminum. It seems as if the projected net impacts of these tariffs on the total US trade deficit and US economy depends largely on which news outlets you get your news from. We thought it would be helpful to put together a simple and easy to understand model to estimate of the net impact of these tariffs on the US trade deficit, assuming that no other factors change (e.g., no retaliatory tariffs are enacted by other countries). We wanted something that allows you to understand how its estimates are being derived, with assumptions that can be easily replaced with your own, so that the model itself would be impartial to any particular viewpoint. We want the uncertainties that are inherent in such a simple model to be explicit, so you can see the range of possibilities and not just a single guess. Finally, we wanted the model to be easy to understand fully for non-economists (a group to which we belong, too).

This model accompanied a current event blog post on the Lumina blog: [http://lumina.com/blog/impact-of-trumps-proposed-steel-aluminum-tariffs-on-us-trade-deficit Impact of Trump’s proposed Steel & Aluminum tariffs on US trade deficit]

'''Authors:''' Kimberley Mullins and Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]

=== Tax bracket interpolation ===

<center>[[Image:Tax bracket interpolation.png]]</center>

'''Description:''' Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media: Tax bracket interpolation.ana|Tax bracket interpolation.ana]]

==Data Analysis==

=== Sampling from only feasible points ===

'''Description:''' You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.

This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".

Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.

The instructions for how to use this are in the module description field.

'''Keywords''': Statistics, sampling, Importance sampling, feasibility, Monte Carlo simulation

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]

=== Cross-Validation / Fitting Kernel Functions to Data ===

:[[image:Cross-validated data fit.jpg]]

'''Description:''' When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data. When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.

Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set. Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set. It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.

This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.

Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].

'''Keywords:''' Cross-validation, overfitting, non-linear kernel functions

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Cross-validation example.ana|Cross-validation example.ana]]

=== Statistical Bootstrapping ===

'''Description:''' Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator. The simplest version estimates sampling error by resampling the original data. This model demonstrates how to do this in Analytica.

'''Keywords:''' Bootstrapping, sampling error, re-sampling

'''Download:''' [[media:Bootstrapping.ana|Bootstrapping.ana]]

=== Smooth PDF plots using Kernel Density Estimation ===

:{| border="0"
| [[image:Dens_Est_builtin_pdf.png|frame|Analytica's built-in PDF plot with default settings]]
|
[[image:Dens_Est_Kernel_pdf.png|frame|PDF computed from Kernel Density estimation]]
|}

'''Description:''' This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density. The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin. A kernel density estimator smooths this out, producing a less choppy PDF plot.

This smoothing is built into [[Analytica 4.4]]. You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].

'''Keywords:''' Kernel density estimation, kernel density smoothing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]

=== Output and Input Columns in Same Table ===

:[[image:Output and input columns.png]]

'''Description:''' Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select. Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column. The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.

Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column. This button is presented on the top-level panel. If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.

'''Keywords:''' Data analysis

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Output and input columns.ana|Output and input columns.ana]]

==Decision Analysis==

=== From Controversy to Consensus: California's offshore oil platforms ===

<center>[[image:oilplatform_1.jpg|500px]]</center>

'''Description:''' Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "rigs to reefs", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.

'''Keywords:'''decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Max Henrion, Lumina Decision Systems

'''Download:''' [[media:Platform 2018b.ana|Platform2018b.ana]].

=== Retirement plan type comparison ===

<center>[[image:Comparing retirement account types.png|500px]]</center>

'''Description:''' Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.

'''Keywords:''' 401(k), IRA, retirement account, decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Comparing retirement account types.ana|Comparing retirement account types.ana]].
::For a version without the sensitivity analysis part, which has fewer than 100 objects and can thus be modified using [http://lumina.com/products/free101/ Analytica Free 101], you can use this one: [[media:Comparing retirement account types without sensitivity.ana|Comparing retirement account types without sensitivity.ana]].

=== Plane Catching Decision with Expected Value of Including Uncertainty ===

'''Description:''' A simple model to assess what time I should leave my home to catch a plane, with uncertain driving time, walking from parking to gate (including security), and how long I need to be at the gate ahead of scheduled departure time. It uses a loss model based on minutes, assuming I value each extra minute snoozing in bed and set the loss if I miss the plane to 400 of those minutes.

It illustrates the EVIU (expected value of including uncertainty) i.e. the difference in expected value if I make a decision to minimize expected loss instead of decision to minimize time ignoring uncertainty (assuming each distribution is fixed at its mid value). For more details see "The Value of Knowing How Little You Know", Max Henrion, PhD Dissertation, Carnegie Mellon University, 1982.

'''Keywords''': Decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU.

'''Author:''' Max Henrion

'''Download:''' [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]]

=== Marginal Analysis for Control of SO2 emissions ===

'''Description:''' Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S. This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.

'''Keywords:''' Environmental engineering, cost-benefit analysis, marginal analysis

'''Author:''' Surya Swamy

'''Download:''' [[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]

==Dynamic Models==

=== Donor/Presenter Dashboard ===

'''Description:''' This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.

It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term. It also allows for uncertainty in the input parameters.

'''Keywords:''' Dynamic models, Markov processes

'''Download:''' [[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]

=== Regulation of Photosynthesis ===

:[[image:Photosynthesis fluorescence.jpg]]

A model of how photosynthesis is regulated inside a cyanobacteria. As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation. The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed. In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities). In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell. Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.

This model uses node shapes for a different purpose than is normally seen in decision analysis models. In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.

Two models are attached. The first is a bit cleaner, and focused on the core transport chain, as described above. The second is less developed, but is focused more on genetic regulation processes.

'''Keywords:''' Photosynthesis, dynamic models

'''Author:''' Lonnie Chrisman

'''Download:'''
* [[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways
* [[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation.

=== Time-series re-indexing ===

:[[image:Weekly_data_graph_ex.png]]

'''Description:''' This model contains some examples of time-series re-indexing. It is intended to demonstrate some of these basic techniques.

In this example, actual measurements were collected at non-uniform time increments. Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week. The mapping is done using an interpolation. The evenly-spaced data is then used to forecast future behavior. We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change. We then combine the historical data with the forecast on a common index (<code>Week</code>). A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).

'''Keywords:''' Dynamic models, forecasting, time-series re-indexing

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]

==Engineering Examples==

=== Timber Post Compression Load Capacity ===

'''Description:''' Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.

'''Keywords:'''

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:PostCompression.ana|Post Compression Model]]

=== Compression Post Load Calculator ===

'''Description:''' Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.

'''Keywords:''' Compression analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]

=== Daylighting Options in Building Design ===

:[[File:Chapter_9.7-updated.png]]

'''Description:''' A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.

Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.

'''Keywords:''' Engineering, cost-benefits analysis

'''Author:''' Max Henrion

'''Download:''' [[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]

=== California Power Plants ===

'''Description:''' An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table. This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables. The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.

'''Keywords:''' Edit table, Choice menu, pulldown menu, checkbox, Power plants.

'''Download:''' [[Media:California_Power_Plants.ANA|California Power Plants.ana ]]

=== Electrical Generation and Transmission ===

'''Description:''' This model of an electrical network minimizes total cost of generation and transmission. Each node in the network has power generators and consumers (demand). Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero). Each generator has a min and max power and a marginal cost in $/KWh. The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.

''Requires Analytica Optimizer''

'''Keywords:''' Electrical engineering, power generation and transmission

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Electrical Transmission.ana|Electrical Transmission.ana]]

==Fun and Games==

=== Color Map ===

:[[File:Color_map.gif]]

'''Description:''' A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.

'''Keywords:''' Computed cell formatting

'''Author:''' Kimberley Mullins, Lumina Decision Systems

'''Download:''' [[media:color_map.ana|Color map.ana]]

=== 2018 World Cup Soccer final ===

'''Download:''' [[Media:World cup.ana|World cup.ana]]

'''Description:''' On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

=== Image recognition ===

'''Download: ''' [http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]

'''Description:''' Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems. (Analytica implementation). Residual network developed by
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", https://arxiv.org/abs/1512.03385 rXiv:1512.03385]

==Function Examples==

=== Transforming Dimensions by transform matrix, month to quarter ===

'''Description:''' The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as [[Aggregate|aggregation]]. The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.

'''Webinar:''' [[Analytica_User_Group/Past_Topics#The_Aggregate_Function|the Aggregate function]].

'''Keywords''': Aggregation, level of detail, days, weeks, months, quarters, years.

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:Month to quarter.ana|Month to quarter.ana]]

=== Convolution ===

'''Description:''' Convolution is used mostly for signal and systems analysis. It is a way to combine two time series. This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series. The model contains several examples of convolved functions.

A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.

The mathematical definition of the convolution of two time series is the function given by:

:<math>h(t) = \int y(u) z(t-u) dt</math>

'''Keywords:''' Signal analysis, systems analysis

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Convolution.ana|Convolution.ana]]

=== Dependency Tracker Module ===

:[[image:Dependency tracker.jpg]]

'''Description:''' This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another. You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.

The module contains button scripts that change the bevel appearance of nodes in your model. To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>. Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.

In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes. (The result of pressing the "Bevel all from Ancestor to Descendant" button)

'''Keywords:''' Dependency analysis

'''Download:''' [[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]

=== Multi-lingual Influence Diagram ===

:{| border="0"
| [[Image:English-view.png]]
| [[Image:French-view.png]]
|}

'''Description:''' Maintains a single influence diagram with Title and Description attributes in both English and French. With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.

If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description. Similarly if you make a change while viewing French.

'''Keywords:''' Multi-lingual models

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:French-English.ana|French-English.ana]]

=== Extracting Data from an XML file ===

'''Description:''' Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).

'''Keywords:''' Data extraction, xml, DOM parsing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Parsing XML example.ana|Parsing XML example.ana]]

=== Vector Math ===

'''Description:'''
Functions used for computing geospatial coordinates and distances. Includes:
* A cross product of vectors function
* Functions to conversion between spherical and Cartesian coordinates in 3-D
* Functions to compute bearings from one latitude-longitude point to another
* Functions for finding distance between two latitude-longitude points along the great circle.
* Functions for finding the intersection of two great circles

'''Keywords:''' Geospatial analysis, GIS, vector analysis

'''Author:''' Robert D. Brown III, Incite Decision Technologies, LLC

'''Download:''' [[media:Vector Math.ana|Vector Math.ana]]

==Optimizer Examples==

=== Total Allowable Harvest ===

'''Description:''' The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:

:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>

where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.

The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).

Two models are available for download. One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.

'''Keywords:''' Population analysis, dynamic models, optimization analysis

'''Author:''' Models contributed by Pierre Richard

'''Download:'''
* [[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]]
* [[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]]

=== Linearizing a discrete NSP ===

'''Description:''' A cereal formulation model

A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
1) Decision variable is constructed as a constrained Boolean array
2) Prices are defined as piecewise linear curves

'''Keywords:''' product formulation, cereal formulation

'''Author:''' P. Sanford, Lumina Decision Systems

'''Download:''' [[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]

=== Neural Network ===

'''Description:''' A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer. This is essentially an example of non-linear regression. This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.

Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].

'''Keywords:''' Feed-forward neural networks, optimization analysis

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Neural-Network.ana|Neural Network.ana]]

==Risk Analysis==

=== Earthquake Expenses ===

'''Description:''' An example of risk analysis with time-dependence and costs shifted over time.

Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes. This simplified demo model can be used to answer questions such as:
* What is the probability of more than one quake in a specific 10 year period.
* What is the probability that in my time window my costs exceed $X?

Assumptions in this model:
* Earthquakes are Poisson events with mean rate of once every 10 years.
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time: 20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.

'''Keywords:''' Risk analysis, cost analysis

'''Download''': [[media:Earthquake expenses.ana|Earthquake expenses.ana]]

=== Loan Policy Selection ===

'''Description:''' A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge. The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating. The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution. Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.

'''Best used with Analytica Optimizer'''

'''Keywords:''' Creditworthiness, credit rating, default risk, risk analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Loan policy selection.ANA|Loan policy selection.ana]]

=== Inherent and Residual Risk Simulation ===

:[[File:Prob of Exceeding Loss.png]]

'''Description:''' The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].

'''Keywords:''' Cybersecurity risk, loss exceedance curve, simulation

'''Author:''' Kim Mullins

'''Download:''' [[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]

== Graphing examples ==
=== Red or blue state ===
[[image:Red_or_blue_state.png|600px]]

'''Download:''' [[media:Red State Blue State plot.ana]]

'''Description:''' This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

==See Also==
* [[New examples]]
* [[Additional libraries]]
* [[Uploading Example Models]]
* [[Example Models and Libraries]]
* [[Using Add Module... to import a Model file]]
* [[Import a module or library]]
* [[Tutorial: Sharing a model with ACP]]
* [[Obfuscated and Browse-Only Models]]
* [[Filed modules and libraries]]
* [[Working with Models, Modules, and Files in ADE]]
* [[Combining models into an integrated model]]
* [[Model file formats]]
* [[Model Licensing]]

File:Oilplatform 1.jpg

2018-11-16T18:55:47Z

Jhoy:

Example Models

2018-11-16T18:53:56Z

Jhoy:

[[Category: Models]]
[[category:Doc Status D]]

You may find these example Analytica models useful to see what Analytica can do, and as inspiration or a starting point for your own models. They cover a wide variety of topics and techniques.

These examples supplement the example models that are [[Example Models and Libraries |installed with Analytica]] into the Examples folder.

You can are also invited to contribute your own models as examples. For how to do that, see [[Uploading Example Models]].

__TOC__

==Business Examples==

=== Marginal Abatement Graph ===

:[[image:Marginal abatement heating energy.png]]

This model, along with [http://blog.lumina.com/2015/marginal-abatement/ the accompanying blog article], show how to set up a Marginal Abatement graph in Analytica.

'''Keywords''': Graph methods, carbon price, energy efficiency, climate policy, optimal allocation, budget constraint.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Marginal abatement home heating.ana|Marginal abatement home heating.ana]]

=== Solar Panel Analysis ===

:[[image:Solar Panel Analysis.png]]

'''Description:''' Would it be cost effective to install solar panels on the roof of my house? This model explores this question for my situation in San Jose, California. [https://www.youtube.com/watch?v=vhSor_fPIsI An accompanying video] documents the building of this model, and is a good example of the process one goes through when building any decision model.

The model explores how many panels I should install, and what the payoff is in terms of [[NPV|net present value]], [[IRR|Internal rate of return]] and time to recoup cost. It also looks at whether I should postpone the start of the installation to take advantage of rapidly falling PV prices, or cash in on tax credits.

'''Keywords''': Renewable energy, photovoltaics, net present value, internal rate of return, tax credits, agile modeling.

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Solar Panel Analysis.ana|Solar Panel Analysis.ana]]

=== Items within Budget function ===

'''Description:''' Given a set of items, with a priority and a cost for each, the function Items_within_budget function selects out the highest priority items that fit within the fixed budget.

'''Keywords:'''

'''Author:''' Max Henrion

'''Download''': [[Media:Items_within_budget.ana|Items within budget.ana]]

=== Grant Exclusion Model ===

'''Description:''' This model tests a hypothesis about the distribution of an attribute of the marginal rejectee of a grant program, given the relevance of that attribute to award of the grant. It could be used by an organization to make decisions as to whether to fiscally-sponsor another organization that will use that fiscal sponsorship to apply for grants, by looking at the effect on the pool of grant recipients overall.

'''Keywords:''' Business analysis

'''Download''': [[Media:Grant_exclusion.ANA|Grant exclusion.ana]]

=== Project Planner ===

:[[Image: Project planner model.png |500px]]

'''Description:''' This is a demo model that shows how to:
* Evaluate a set of R&D projects, including uncertain R&D costs, and uncertain revenues if it leads to release of a commercial product.
* Use multiattribute analysis to compare projects, including a hard attribute -- expected net present value -- and soft attributes -- strategic fit, staff development, and public good will.
* Compare cost, [[NPV]], and multiattribute value for a selected portfolio of projects.
* Generate the best portfolio (ratio of NPV or multiattribute merit to cost) given a R&D budget.

The model linked here is only a test, and to an older version: [[File:Project_priorities_2007_4.0.ANA]]

'''Keywords:''' Business models, cost analysis, net present value (NPV), uncertainty analysis

'''Download:''' [[Media:Project Priorities 5 0.ana|Project Priorities 5 0.ana]]

=== Steel and Aluminum import tariff impact on US trade deficit ===

<center>[[Image:Steel and aluminum tariff model diagram.png|400px]]</center>

'''Description:''' On 2-March-2018, President Trump proposed new import tariffs on steel and aluminum. It seems as if the projected net impacts of these tariffs on the total US trade deficit and US economy depends largely on which news outlets you get your news from. We thought it would be helpful to put together a simple and easy to understand model to estimate of the net impact of these tariffs on the US trade deficit, assuming that no other factors change (e.g., no retaliatory tariffs are enacted by other countries). We wanted something that allows you to understand how its estimates are being derived, with assumptions that can be easily replaced with your own, so that the model itself would be impartial to any particular viewpoint. We want the uncertainties that are inherent in such a simple model to be explicit, so you can see the range of possibilities and not just a single guess. Finally, we wanted the model to be easy to understand fully for non-economists (a group to which we belong, too).

This model accompanied a current event blog post on the Lumina blog: [http://lumina.com/blog/impact-of-trumps-proposed-steel-aluminum-tariffs-on-us-trade-deficit Impact of Trump’s proposed Steel & Aluminum tariffs on US trade deficit]

'''Authors:''' Kimberley Mullins and Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media:Steel and aluminum tariff model.ana|Steel and aluminum tariff model.ana]]

=== Tax bracket interpolation ===

<center>[[Image:Tax bracket interpolation.png]]</center>

'''Description:''' Computes amount of tax due from taxable income for a 2017 US Federal tax return. To match the IRS's numbers exactly, it is necessary to process tax brackets correctly as well as implementation a complex mix of rounding rules that reproduce the 12 pages of table lookups from the Form 1040 instructions. This model is showcased in a blog article, [http://Lumina.com/blog/how-to-simplify-the-irs-tax-tables How to simplify the IRS Tax Tables].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[Media: Tax bracket interpolation.ana|Tax bracket interpolation.ana]]

==Data Analysis==

=== Sampling from only feasible points ===

'''Description:''' You have a bunch of chance variables, each with a probability distribution. Their joint sample, however, contains some combinations of points that are (for one reason or another) physically impossible. We'll call those infeasible points. You'd like to eliminate those points from the sample and keep only the feasible points.

This module implements a button that will sample a collection of chance variables, then reset the sample size and keep only those sample points that are "feasible".

Obviously, this approach will work best when most of your samples are feasible. If you can handle the "infeasible" points in your model directly, by conditioning certain chance variables on others, that is far preferable. But there are some cases where this solution (although a bit of a kludge) is more convenient.

The instructions for how to use this are in the module description field.

'''Keywords''': Statistics, sampling, Importance sampling, feasibility, Monte Carlo simulation

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Feasible_Sampler.ana|Feasible Sampler.ana]]

=== Cross-Validation / Fitting Kernel Functions to Data ===

:[[image:Cross-validated data fit.jpg]]

'''Description:''' When fitting a function to data, if you have too many free parameters relative to the number of points in your data set, you may "overfit" the data. When this happens, the fit to your training data may be very good, but the fit to new data points (beyond those used for training) may be very poor.

Cross-validation is a common technique to deal with this problem: We set aside a fraction of the available data as a cross-validation set. Then we begin by fitting very simple functions to the data (with few free parameters), successively increasing the number of free parameters, and seeing how the predictive performance changes on the cross-validation set. It is typical to see improvement on the cross-validation set for a while, followed by a deterioration of predictive performance on the cross-validation set once overfitting starts occurring.

This example model successively fits a non-linear kernel function to the residual error, and uses cross-validation to determine how many kernel functions should be used.

Requires Analytica Optimizer: The kernel fitting function (Kern_Fit) uses [[NlpDefine]].

'''Keywords:''' Cross-validation, overfitting, non-linear kernel functions

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Cross-validation example.ana|Cross-validation example.ana]]

=== Statistical Bootstrapping ===

'''Description:''' Bootstrapping is a technique from statistics for estimating the sampling error present in a statistical estimator. The simplest version estimates sampling error by resampling the original data. This model demonstrates how to do this in Analytica.

'''Keywords:''' Bootstrapping, sampling error, re-sampling

'''Download:''' [[media:Bootstrapping.ana|Bootstrapping.ana]]

=== Smooth PDF plots using Kernel Density Estimation ===

:{| border="0"
| [[image:Dens_Est_builtin_pdf.png|frame|Analytica's built-in PDF plot with default settings]]
|
[[image:Dens_Est_Kernel_pdf.png|frame|PDF computed from Kernel Density estimation]]
|}

'''Description:''' This example demonstrates a very simple fixed-width kernel density estimator to estimate a "smooth" probability density. The built-in PDF function in Analytica often has a choppy appearance due to the nature of histogramming -- it sets up a set of bins and counts how many points land in each bin. A kernel density estimator smooths this out, producing a less choppy PDF plot.

This smoothing is built into [[Analytica 4.4]]. You can select [[Kernel Density Smoothing|smoothing]] from the [[Uncertainty Setup dialog]].

'''Keywords:''' Kernel density estimation, kernel density smoothing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Kernel_Density_Estimation.ana|Kernel Density Estimation.ana]]

=== Output and Input Columns in Same Table ===

:[[image:Output and input columns.png]]

'''Description:''' Presents an input table to a user, where one column is populated with computed output data, the other column with checkboxes for the user to select. Although the '''Output Data''' column isn't read only, as would be desired, a [[Check Attribute]] has been configured to complain if he does try to change values in that column. The model that uses these inputs would ignore any changes he makes to data in the '''Output Data''' column.

Populating the '''Output Data''' column requires the user to press a button, which runs a button script to populate that column. This button is presented on the top-level panel. If you change the input value, the output data will change, and then the button needs to be pressed to refresh the output data column.

'''Keywords:''' Data analysis

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Output and input columns.ana|Output and input columns.ana]]

==Decision Analysis==

=== From Controversy to Consensus: California's offshore oil platforms ===

<center>[[image:oilplatform_1.jpg|500px]]</center>

'''Description:''' Too many environmental issues cause bitter public controversy. The question of how to decommission California's 27 offshore oil platforms started out as a typical example. But remarkably, after careful analysis a single option, "rigs to reefs", obtained the support of almost all stakeholders, including oil companies and environmentalists. A law to enable this option was passed by the California State house almost unanimously, and signed by Governor Arnold Schwarzenegger.

'''Keywords:'''decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Max Henrion, Lumina Decision Systems

'''Download:''' [[media:Platform 2018b.ana|Platform2018b.ana]].

=== Retirement plan type comparison ===

<center>[[image:Comparing retirement account types.png|500px]]</center>

'''Description:''' Will you end up with a bigger nest egg at retirement with a 401(k), traditional IRA, Roth IRA or a normal non-tax-advantaged brokerage account? For example, comparing a Roth IRA to a normal brokerage, intermediate capital gains compound in the Roth, but eventually you pay taxes on those gains at your income tax rate at retirement, whereas in the brokerage you pay capital gains taxes on the gains, which is likely a lower tax rate. So does the compounding outweigh the tax rate difference? What effect do the higher account maintenance fees in a 401(k) account have? How sensitive are these conclusions to the various input estimates? The answers to all these questions depend on your own situation, and may different for someone else. Explore these questions with this model.

'''Keywords:''' 401(k), IRA, retirement account, decision analysis, uncertainty, sensitivity analysis, [[MultiTable]]s.

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Comparing retirement account types.ana|Comparing retirement account types.ana]].
::For a version without the sensitivity analysis part, which has fewer than 100 objects and can thus be modified using [http://lumina.com/products/free101/ Analytica Free 101], you can use this one: [[media:Comparing retirement account types without sensitivity.ana|Comparing retirement account types without sensitivity.ana]].

=== Plane Catching Decision with Expected Value of Including Uncertainty ===

'''Description:''' A simple model to assess what time I should leave my home to catch a plane, with uncertain driving time, walking from parking to gate (including security), and how long I need to be at the gate ahead of scheduled departure time. It uses a loss model based on minutes, assuming I value each extra minute snoozing in bed and set the loss if I miss the plane to 400 of those minutes.

It illustrates the EVIU (expected value of including uncertainty) i.e. the difference in expected value if I make a decision to minimize expected loss instead of decision to minimize time ignoring uncertainty (assuming each distribution is fixed at its mid value). For more details see "The Value of Knowing How Little You Know", Max Henrion, PhD Dissertation, Carnegie Mellon University, 1982.

'''Keywords''': Decision theory, decision analysis, uncertainty, Monte Carlo simulation, value of information, EVPI, EVIU.

'''Author:''' Max Henrion

'''Download:''' [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]]

=== Marginal Analysis for Control of SO2 emissions ===

'''Description:''' Acid rain in eastern US and Canada caused by sulfur dioxide is emitted primarily by coal-burning electric-generating plants in the Midwestern U.S. This model demonstrates a marginal analysis a.k.a. benefit/cost analysis to determine the policy alternative that leads us to the most economically efficient level of cleanup.

'''Keywords:''' Environmental engineering, cost-benefit analysis, marginal analysis

'''Author:''' Surya Swamy

'''Download:''' [[media:Marginal Analysis for Control of SO2 Emissions.ana|Marginal Analysis for Control of SO2 Emissions.ana]]

==Dynamic Models==

=== Donor/Presenter Dashboard ===

'''Description:''' This model implements a continuous-time Markov chain in Analytica's discrete-time dynamic simulation environment. It supports immigration to, and emigration from, every node.

It can be used by an arts organization to probabilistically forecast future audience evolution, in both the short and the long (steady state) term. It also allows for uncertainty in the input parameters.

'''Keywords:''' Dynamic models, Markov processes

'''Download:''' [[Media:Donor_Presenter_Dashboard_II.ANA|Donor-Presenter Dashboard.ana]]

=== Regulation of Photosynthesis ===

:[[image:Photosynthesis fluorescence.jpg]]

A model of how photosynthesis is regulated inside a cyanobacteria. As light exposure varies over time (and you can experiment with various light intensity waveforms), it simulates the concentration levels of key transport molecules along the chain, through the PSII complex, plasto-quinone pool, PSI complex, down to metabolic oxidation. The dynamic response to light levels, or changes in light levels, over time becomes evident, and the impact of changes to metabolic demand can also be observed. In the graph of fluorescence above, we can see an indicator of how much energy is being absorbed, in three different cases (different light intensities). In the two higher intensity cases, photoinhibition is observed -- a protective mechanism of the cell that engages when more energy is coming in than can be utilized by the cell. Excess incoming energy, in the absence of photoinhibition, causes damage, particularly to the PSII complex.

This model uses node shapes for a different purpose than is normally seen in decision analysis models. In this model, ovals, instead of depicting chance variables, depict chemical reactions, where the value depicts the reaction rate, and rounded rectangles depict chemical concentrations.

Two models are attached. The first is a bit cleaner, and focused on the core transport chain, as described above. The second is less developed, but is focused more on genetic regulation processes.

'''Keywords:''' Photosynthesis, dynamic models

'''Author:''' Lonnie Chrisman

'''Download:'''
* [[media:Photosynthesis regulation.ANA|Photosynthesis Regulation.ana]] - main regulation pathways
* [[media:Photosystem.ana | Photosystem.ana]] - rough sketch of genetic regulation.

=== Time-series re-indexing ===

:[[image:Weekly_data_graph_ex.png]]

'''Description:''' This model contains some examples of time-series re-indexing. It is intended to demonstrate some of these basic techniques.

In this example, actual measurements were collected at non-uniform time increments. Before analyzing these, we map these to a uniformly spaced time index (<code>Week</code>), occurring on Monday of each week. The mapping is done using an interpolation. The evenly-spaced data is then used to forecast future behavior. We first forecast over an index containing only future time points (<code>Future_weeks</code>), using a log-normal process model based on the historical weekly change. We then combine the historical data with the forecast on a common index (<code>Week</code>). A prob-bands graph of the weekly_data result shows the range of uncertainty projected by the process model (you'll notice the uncertainty exists only for future forecasted values, not historical ones).

'''Keywords:''' Dynamic models, forecasting, time-series re-indexing

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Time-series-reindexing.ana|Time-series-reindexing.ana]]

==Engineering Examples==

=== Timber Post Compression Load Capacity ===

'''Description:''' Here is a calculator for computing the maximum load that can be handled by a Douglas Fir - Larch post of a given size, grade, and composition in a construction setting.

'''Keywords:'''

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:PostCompression.ana|Post Compression Model]]

=== Compression Post Load Calculator ===

'''Description:''' Computes the load that a Douglas-Fir Larch post can support in compression. Works for different timber types and grades and post sizes.

'''Keywords:''' Compression analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Compression_Post_Load_Capacity.ana|Compression Post Load Capacity.ana]]

=== Daylighting Options in Building Design ===

:[[File:Chapter_9.7-updated.png]]

'''Description:''' A demonstration showing how to analyze lifecycle costs and savings from daylighting options in building design.

Analysis based on Nomograph Cost/Benefit Tool for Daylighting. adapted from S.E. Selkowitz and M. Gabel. 1984. "LBL Daylighting Nomographs," LBL-13534, Lawrence Berkeley Laboratory, Berkeley CA, 94704. (510) 486-6845.

'''Keywords:''' Engineering, cost-benefits analysis

'''Author:''' Max Henrion

'''Download:''' [[media:Daylighting analyzer.ana|Daylighting analyzer.ana]]

=== California Power Plants ===

'''Description:''' An example showing how to use Choice menus and Checkbox inside an Edit table. It also shows how to use the Cell default attribute to specify default values (including Choice menu and Checkbox with default selections) specified in "Default Plant Data" to be used when user creates a new row in the Edit table. This model shows how to demonstrates the use of [[Choice|choice pulldowns]] in edit tables. The model is created during a mini-tutorial on [[Inserting Choice Controls in Edit Table Cells]] elsewhere on this Wiki.

'''Keywords:''' Edit table, Choice menu, pulldown menu, checkbox, Power plants.

'''Download:''' [[Media:California_Power_Plants.ANA|California Power Plants.ana ]]

=== Electrical Generation and Transmission ===

'''Description:''' This model of an electrical network minimizes total cost of generation and transmission. Each node in the network has power generators and consumers (demand). Nodes are connected by transmission links. Each link has a maximum capacity in Watts and an admittance (the real part of impedance is assumed to be zero). Each generator has a min and max power and a marginal cost in $/KWh. The model uses a linear program to determine how much power each generator should produce so as to minimize total cost of generation and transmission, while satisfying demand and remaining within link constraints.

''Requires Analytica Optimizer''

'''Keywords:''' Electrical engineering, power generation and transmission

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Electrical Transmission.ana|Electrical Transmission.ana]]

==Fun and Games==

=== Color Map ===

:[[File:Color_map.gif]]

'''Description:''' A model which highlights [[Cell Format Expression|Cell Formatting]] and [[Computed cell formats|Computed Cell Formats]]. Model result is a 'color map' wherein the cell fill color is computed based on three input variables (R, G, and B), the computed color is displayed in hexadecimal, and the font color of the hexadecimal color is determined by the cell fill color.

'''Keywords:''' Computed cell formatting

'''Author:''' Kimberley Mullins, Lumina Decision Systems

'''Download:''' [[media:color_map.ana|Color map.ana]]

=== 2018 World Cup Soccer final ===

'''Download:''' [[Media:World cup.ana|World cup.ana]]

'''Description:''' On July 15, 2018, France beat Croatia 4-2 in the final game of the World Cup to become world champions. But how much of that is can be attributed to France being the better team versus to the random chance? This model accompanies my blog article, [http://lumina.com/blog/world-cup-soccer.-how-much-does-randomness-determine-the-winner World Cup Soccer. How much does randomness determine the winner?], where I explore this question and use the example to demonstrate the [[Poisson|Poisson distribution]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

=== Image recognition ===

'''Download: ''' [http://AnalyticaOnline.com/Lonnie/resnet18.zip resnet18.zip]

'''Description:''' Show it an image, and it tries to recognize what it is an image of, classifying it among 1000 possible categories. It uses an 18-layer residual network. This model is described and demonstrated in a video in the blog article [http://lumina.com/blog/an-analytica-model-that-recognizes-images An Analytica model that recognizes images].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems. (Analytica implementation). Residual network developed by
* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, "Deep Residual Learning for Image Recognition", https://arxiv.org/abs/1512.03385 rXiv:1512.03385]

==Function Examples==

=== Transforming Dimensions by transform matrix, month to quarter ===

'''Description:''' The model shows how to transform an array from a finer-grain index (e.g., Month) onto a coarser index (e.g., Quarter). We generally refer to this as [[Aggregate|aggregation]]. The model illustrates the direct use of [[Aggregate]], as well as a method to do this used before Aggregate was added to Analytica in release 4.2.

'''Webinar:''' [[Analytica_User_Group/Past_Topics#The_Aggregate_Function|the Aggregate function]].

'''Keywords''': Aggregation, level of detail, days, weeks, months, quarters, years.

'''Author:''' Lonnie Chrisman

'''Download:''' [[Media:Month to quarter.ana|Month to quarter.ana]]

=== Convolution ===

'''Description:''' Convolution is used mostly for signal and systems analysis. It is a way to combine two time series. This model contains function Convolve(Y, Z, T, I), that computes the convolution of two time series. The model contains several examples of convolved functions.

A time series is a set of points, <code>(Y, T)</code>, where <code>T</code> is the ascending X-axis, and the set of points is indexed by <code>I</code>. The values of <code>T</code> do not have to be equally spaced. The function treats <code>Y</code> and <code>Z</code> as being equal to 0 outside the range of <code>T</code>. The two time series here are the set of points <code>(Y, T)</code> and the set of points <code>(Z, T)</code>, where both sets of points are indexed by <code>I</code>.

The mathematical definition of the convolution of two time series is the function given by:

:<math>h(t) = \int y(u) z(t-u) dt</math>

'''Keywords:''' Signal analysis, systems analysis

'''Author:''' Lonnie Chrisman

'''Download''': [[Media:Convolution.ana|Convolution.ana]]

=== Dependency Tracker Module ===

:[[image:Dependency tracker.jpg]]

'''Description:''' This module tracks dependencies through your model, updating the visual appearance of nodes so that you can quickly visualize the paths by which one variable influences another. You can also use it to provide a visual indication of which nodes are downstream (or upstream) from an indicated variable.

The module contains button scripts that change the bevel appearance of nodes in your model. To see how Variable <code>X</code> influences Variable <code>Y</code>, the script will bevel the nodes for all variables that are influenced by <code>X</code> and influence <code>Y</code>. Alternatively, you can bevel all nodes that are influenced by <code>X</code>, or you can bevel all nodes that influence <code>Y</code>.

In the image above, the path from <code>dp_ex_2</code> through <code>dp_ex_4</code> has been highlighted using the bevel style of the nodes. (The result of pressing the "Bevel all from Ancestor to Descendant" button)

'''Keywords:''' Dependency analysis

'''Download:''' [[media:Dependency_Tracker.ANA | Dependency Tracker.ana]]

=== Multi-lingual Influence Diagram ===

:{| border="0"
| [[Image:English-view.png]]
| [[Image:French-view.png]]
|}

'''Description:''' Maintains a single influence diagram with Title and Description attributes in both English and French. With the change of a pull-down, the influence diagram and all object descriptions are instantly reflected in the language of choice.

If you change a title or description while viewing English, and then change to French, the change you made will become the English-language version of the description. Similarly if you make a change while viewing French.

'''Keywords:''' Multi-lingual models

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:French-English.ana|French-English.ana]]

=== Extracting Data from an XML file ===

'''Description:''' Suppose you receive data in an XML format that you want to read into your model. This example demonstrates two methods for extracting data: Using a full XML DOM parser, or using regular expressions. The first method fully parses the XML structure, the second simply finds the data of interest by matching patterns, which can be easier for very simple data structures (as is often the case).

'''Keywords:''' Data extraction, xml, DOM parsing

'''Author:''' D. Rice, Lumina Decision Systems

'''Download:''' [[media:Parsing XML example.ana|Parsing XML example.ana]]

=== Vector Math ===

'''Description:'''
Functions used for computing geospatial coordinates and distances. Includes:
* A cross product of vectors function
* Functions to conversion between spherical and Cartesian coordinates in 3-D
* Functions to compute bearings from one latitude-longitude point to another
* Functions for finding distance between two latitude-longitude points along the great circle.
* Functions for finding the intersection of two great circles

'''Keywords:''' Geospatial analysis, GIS, vector analysis

'''Author:''' Robert D. Brown III, Incite Decision Technologies, LLC

'''Download:''' [[media:Vector Math.ana|Vector Math.ana]]

==Optimizer Examples==

=== Total Allowable Harvest ===

'''Description:''' The problem applies to any population of fish or animal whose dynamics are poorly known but can be summarized in a simple model:

:<code>N_t + 1 = N_t*Lambda - landed catch*(1 + loss rate)</code>

where «N_t» is the population size (number of individuals) at time ''t'', «N_t+1» is the population size at time ''t + 1'', «Lambda» is the intrinsic rate of increase and the «loss rate» is the percentage of fish or animals killed but not retrieved relative to the «landed catch», or catch secured.

The question here is to determine how many fish or animals can be caught (landed) annually so that the probability of the population declining X% in Y years (decline threshold) is less than Z% (risk tolerance).

Two models are available for download. One uses the Optimizer ([[NlpDefine]]) to find the maximum landed catch at the risk tolerance level for the given decline threshold. The other (for those using a version of Analytica without Optimizer) uses [[StepInterp]] in an iterative way to get that maximum landed catch.

'''Keywords:''' Population analysis, dynamic models, optimization analysis

'''Author:''' Models contributed by Pierre Richard

'''Download:'''
* [[media:Total Allowable Removal model with Optimizer.ana | Total Allowable w Optimizer.ana]]
* [[media:Total Allowable Removal model w StepInterp.ana|Total Allowable w StepInterp.ana]]

=== Linearizing a discrete NSP ===

'''Description:''' A cereal formulation model

A discrete mixed integer model that chooses product formulations to minimize total ingredient costs. This could be an NSP but it uses two methods to linearize:
1) Decision variable is constructed as a constrained Boolean array
2) Prices are defined as piecewise linear curves

'''Keywords:''' product formulation, cereal formulation

'''Author:''' P. Sanford, Lumina Decision Systems

'''Download:''' [[media:Cereal Formulation1.ana|Cereal Formulation1.ana]]

=== Neural Network ===

'''Description:''' A feed-forward neural network can be trained (fit to training data) using the Analytica Optimizer. This is essentially an example of non-linear regression. This model contains four sample data sets, and is set up to train a 2-layer feedforward sigmoid network to "learn" the concept represented by the data set(s), and then test how well it does across examples not appearing in the training set.

Developed during the Analytica User Group Webinar of 21-Apr-2011 -- see the [[Analytica_User_Group/Past_Topics#Neural_Networks|webinar recording]].

'''Keywords:''' Feed-forward neural networks, optimization analysis

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

'''Download:''' [[media:Neural-Network.ana|Neural Network.ana]]

==Risk Analysis==

=== Earthquake Expenses ===

'''Description:''' An example of risk analysis with time-dependence and costs shifted over time.

Certain organizations (insurance companies, large companies, governments) incur expenses following earthquakes. This simplified demo model can be used to answer questions such as:
* What is the probability of more than one quake in a specific 10 year period.
* What is the probability that in my time window my costs exceed $X?

Assumptions in this model:
* Earthquakes are Poisson events with mean rate of once every 10 years.
* Damage caused by such quake is lognormally distributed, with mean $10M adn stddev of $6M.
* Cost of damage gets incurred over the period of a year from the date of the quake as equipment is replaced and buildings are repaired over time: 20% in 1st quarter after quake, 50% in 2nd quarter, 20% in 3rd quarter, 10% in 4th quarter.

'''Keywords:''' Risk analysis, cost analysis

'''Download''': [[media:Earthquake expenses.ana|Earthquake expenses.ana]]

=== Loan Policy Selection ===

'''Description:''' A lender has a large pool of money to loan, but needs to decide what credit rating threshold to require and what interest rate (above prime) to charge. The optimal value is determined by market forces (competing lenders) and by the probability that the borrower defaults on the loan, which is a function of the economy and borrower's credit rating. The model can be used without the Analytica optimizer, in which case you can explore the decision space manually or use a parametric analysis to find the near optimal solution. Those with Analytica Optimizer can find the optimal solution (more quickly) using an [[NlpDefine|NLP]] search.

'''Best used with Analytica Optimizer'''

'''Keywords:''' Creditworthiness, credit rating, default risk, risk analysis

'''Author:''' Lonnie Chrisman

'''Download:''' [[media:Loan policy selection.ANA|Loan policy selection.ana]]

=== Inherent and Residual Risk Simulation ===

:[[File:Prob of Exceeding Loss.png]]

'''Description:''' The model simulates loss exceedance curves for a set of cybersecurity events, the likelihood and probabilistic monetary impact of which have been characterized by system experts. The goal of the model is assess the impact of mitigation measures, by comparing the residual risk curve to the inherent risk curve (defined as risk without any mitigation measures) and to the risk tolerance curve. This is a translation of a model built by Douglas Hubbard and Richard Seiersen which they describe in their book [https://www.howtomeasureanything.com/cybersecurity/about-the-book/ How to Measure Anything in Cybersecurity Risk], and which they make available [https://www.howtomeasureanything.com/cybersecurity/ here].

'''Keywords:''' Cybersecurity risk, loss exceedance curve, simulation

'''Author:''' Kim Mullins

'''Download:''' [[media:Hubbard and Seiersen cyberrisk.ana|Hubbard_and_Seiersen_cyberrisk.ana]]

== Graphing examples ==
=== Red or blue state ===
[[image:Red_or_blue_state.png|600px]]

'''Download:''' [[media:Red State Blue State plot.ana]]

'''Description:''' This example contains the shape outlines for each of the 50 US states, along with a graph that uses color to depict something that varies by state (historical political party leaning). You may find the shape data useful for your own plots. In addition, it demonstrates the polygon fill feature that is new in [[Analytica 5.2]].

'''Author:''' Lonnie Chrisman, Lumina Decision Systems

==See Also==
* [[New examples]]
* [[Additional libraries]]
* [[Uploading Example Models]]
* [[Example Models and Libraries]]
* [[Using Add Module... to import a Model file]]
* [[Import a module or library]]
* [[Tutorial: Sharing a model with ACP]]
* [[Obfuscated and Browse-Only Models]]
* [[Filed modules and libraries]]
* [[Working with Models, Modules, and Files in ADE]]
* [[Combining models into an integrated model]]
* [[Model file formats]]
* [[Model Licensing]]

FAQs on Software Licensing and Activation

2018-06-22T20:56:31Z

Jhoy:

This is a collection of frequently asked questions about activating your Analytica software products. If you have questions that aren't answered here, please let us know at [mailto:sales@lumina.com sales@lumina.com].

__TOC__

==License Server==
===Where do I find my software key?===
Your activation key will be in the email sent from sales@lumina.com. If you no longer have it, please contact sales@lumina.com.
===Can I transfer a license to another end user? ===
===How do I transfer a license to another end user?===
===Where do I find my software key?===
===What is the difference between an Activation Key and a License Name?===
===Can I move my license to another computer?===
===Can I install on two machines?===
===How do I reinstall or move my license to another computer?===
===Why does the software say "you have used up your activations" when I input my key?===
===What should I do when during installation I get the message "no activations available”?===
===Where can I download an old release?===
===Where can I find my Host ID and User ID?===
===Can I use my 5.0 activation key for previous versions?===
===Can I transfer a license to another end user? How do I transfer a license to another end user?===

Analytica User FAQs/Editions, licenses, and installation

2017-01-17T19:36:56Z

Jhoy: /* Can I install Analytica on more than one computer? */

[[Category: FAQ]]
[[Category:Analytica installation and licenses]]

== Do you have resellers or distributors? Who sells Analytica? ==
We have a network of resellers located all over the world including Canada, Germany, Brazil, Italy, Australia, New Zealand, and Japan. We partner with the best organizations that are committed to decision support software and understand the requirements of the industries we serve. To contact a reseller in your area, see our [http://www.lumina.com/services/analytica-resellers/ list of resellers] for contact information.

== How do I know what version of Analytica I need? ==
The [http://www.lumina.com/products/analytica-editions/ Product Description] page on the Lumina website will help you determine which edition of Analytica is right for you. If you require advice after reviewing these description, [http://www.lumina.com/company/contact-us/ contact us] and we would be glad to assist you.

== When will the new version of Analytica be released? ==
Analytica 5.0, is planned for release in November 2016. Check our [http://www.lumina.com/company/news-events/ News & Events] for more details.

== How long can a license be used before it expires? ==
Analytica licenses are permanent. When a license is purchased, 12 months of maintenance and support is included. We recommend renewing support annually to continue receiving technical support and free upgrades. See a full list of maintenance and support benefits [http://www.lumina.com/support/technical-support/ here].

== What does support and maintenance include? ==
Maintenance and Support (MTS) includes:
• Email response to your [http://www.lumina.com/support/technical-support/ technical questions]. Technical support covers all issues in installation, configuration, functions and features, linking to Excel and ODBC databases.
• Free downloads of patch releases
• Free upgrades to minor releases (for example, 4.5 to 4.6)
• Discounts on major upgrades (for example, 4.x to 5.0)
• Access to beta releases of major upgrades
• Edit access to the [[Analytica_Wiki|Analytica Wiki]], an online technical resource
• 25 free credits for [http://www.lumina.com/products/analytica-cloud-player/ Analytica Cloud Player] per month

== How much does it cost to renew support and maintenance on my license? ==
We recommend that the support and maintenance be renewed annually. The cost is 25% of the price of the license or 20% if purchased before expiration. Renew support [http://www.lumina.com/shoppingcart/support here].

== Do you offer one license for multiple users? ==
A [http://www.lumina.com/shoppingcart/rlmproductorder floating license] lets anyone in your organization use Analytica, but only one person at a time per Floating License. It also supports Roaming: A user can check a Floating License out onto their laptop for use out of the office and away from the network. On return to the office, the user checks the license back in again so that it is available for others.

== Can I use Analytica on my Mac? ==
Analytica does not run on Macintosh OS. But, it does run on a Macintosh, using Parallels or VMWare to run Windows.
Many Mac users run Analytica for Windows under VMWare Fusion or Bootcamp.
Analytica runs fine under Bootcamp also. Note that for either Bootcamp or VMWare Fusion, you will need a copy of the Windows Operating System.
FYI: Bootcamp comes free with the Mac OS, and lets you boot up the Mac to run Windows. The difference with VMWare Fusion is that it lets you run the Mac OS X and Windows simultaneously. Mac and Windows programs can each run in their own Windows.

== Can I install Analytica on more than one computer? ==
In the case of an individual license, you can install on more than one computer. For example, you may install on a laptop for use away from your office in addition to your desktop computer; as long as you are the only person who uses those installed copies. One person - one license. See the [http://www.lumina.com/images/uploads/main_images/Analytica%20EULA.pdf End User License Agreement].

== Can I transfer my license to another user? ==
Yes, you can transfer your license to another end user. Just [http://www.lumina.com/company/contact-us/ email us] with your request. We will send the new end user an email with license information and email you, the previous end user, instructions to uninstall your license.

== I tried to install Analytica on my new computer, but the error message says I need a 64-bit license. How do I get one? ==
As long as your maintenance and support is current, simply [http://www.lumina.com/company/contact-us/ email us] with your request and we will provide you with an updated license activation key. If your support and maintenance has expired, there is an upgrade fee for your 64-bit license. Send us your request and we will provide you a quote for upgrading.

== I tried installing Analytica but I get an error message saying my activation code has been used too many times. Can you help? ==
When you purchase your license, it is set up for 3 activations. Once those activations have been used, you will need to notify us and we will provide you with more activations.

== I lost the activation key for my Analytica license. Can you tell me what it is? ==
[http://www.lumina.com/company/contact-us/ Email us] with your company or organization name and we will send you your activation key.

== Where do I find my License Id and activation code? ==
You can find your License ID from inside Analytica. Select the Update License... dialog from the Help Menu. You can copy/paste it from the License field. The activation code is sent to you when you purchased the license. Search your email first and if you still can’t find it, [http://www.lumina.com/company/contact-us/ email us] with your company name and we will send it to you.

== How do I know what version of Analytica I am using? ==
From inside Analytica, select the About Analytica from the Help menu. The window will display your Analytica edition and release. This dialog box shows your Edition, Release number, and the date of the release.

== What is the best way to learn Analytica? ==
Always start with the [[Analytica_Tutorial|Tutorial]].
After that, browse the [[Analytica_User_Guide|User Guide]]. Early chapters are introductory.
Watch the [http://www.lumina.com/why-analytica/video-introduction/#other Solar Panel model video].
Browse the [[Example_Models|example models]].
Browse the [[Analytica_Wiki|wiki]], and [[Tutorial_videos|videos]] there, for ideas.
Through all of it, experiment, try stuff on your own.

== Do you have sample models? ==
From inside Analytica, select Open from the File menu. Click the Example Analytica Models icon. Example models are organized in several categories. In addition to the models that come with your Analytica software, you can find [[Example_Models|more examples]] on the [[Analytica_Wiki|Analytica wiki]]. These examples supplement the example models that are installed with Analytica into the Examples folder.

==See Also==
* [[Editions of Analytica]]
* [[Installation and licenses]]

File:Mega-Millions-ROI.ana

2016-04-14T15:21:41Z

Jhoy:

File:Show Indexes In Help Balloons.ana

2016-04-14T15:20:32Z

Jhoy:

File:Marginal vs Effective Tax Rates.ana

2016-04-14T15:08:13Z

Jhoy: Jhoy uploaded a new version of File:Marginal vs Effective Tax Rates.ana

File:Marginal vs Effective Tax Rates.ana

2016-04-14T15:05:36Z

Jhoy: Jhoy uploaded a new version of File:Marginal vs Effective Tax Rates.ana

File:Marginal vs Effective Tax Rates.ana

2016-04-14T15:01:42Z

Jhoy:

Analytica Cloud Platform

2016-02-19T23:53:47Z

Jhoy: /* What is ACP? */

[[Category: Analytica Cloud Player]]

==What is ACP?==

* The Analytica Cloud Player (ACP) lets you view, run, and share Analytica models via the web.
* ACP users don’t need to install any software. They just need a web browser.
* ACP is similar to Analytica in browse mode. It lets you view and run models, and change inputs, but it doesn’t let you edit model expressions or add variables.
* Analytica modelers can instantly upload models to ACP from Analytica by selecting '''Publish to Web...''' from the '''File''' menu.
* You can be sure that all users of your model via ACP have the latest version of a model and its data -- not so easy if you are distributing a model to end users to run with desktop Analytica.
*ACP offers some user-interface and navigation styles not available in Analytica to help you can adapt your model’s user interface for convenient use on the web:
**Tab-based navigation: Top level modules become tabs
** You can [[ACP Rendering tables and graphs on the diagram | embed tables and graphs]] in a diagram using Frames.
** You can easily select user-interface options using the [[ACP Style Library]].

Learn how to use Analytica Cloud Player, for better collaboration and productivity in the recorded [https://youtu.be/OH3mYa_m0xE Analytica Cloud Player (ACP) Webinar]

===How do you use ACP?===
*You can upload your model to ACP immediately from inside Analytica. Just select '''Publish to Web''' from the '''File''' menu.
*The first time, it will prompt you to start up an ACP account.
*It gives you a directory for managing and starting your models in ACP.
*Once your ACP account is set up, you can [https://www.analyticacloud.com/acp/Client/AnalyticaCloudPlayer.aspx log in to ACP] to access your account from a web browser.
*You can also upload your model to ACP from the web browser. Just log in to ACP from a browser and press the '''Upload''' button at the bottom of the model directory.
*You can email anyone a link to your ACP model, or embed the link in a web page. These users can review model, change inputs, and view outputs, but not save changes. Just log in to ACP from a browser and press the '''Invite''' button at the bottom of the model directory to generate an Invite email. The link shown in the email can be used to access the model as a guest.

== Features and price by subscription type ==
===Comparison Table===
{| class="wikitable"
|-
!
! Individual Account
without Analytica Support
! Individual Account with
Analytica Support
! Basic Group Account
! Premium Group Account
! [[ACP Server License]]
|-
| Publish Models
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Email Invites & Anonymous Views
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Named Users & Roles
| ✗
|✗
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Access Control
| ✗
| ✗
|'''✓'''
|'''✓'''
|'''✓'''
|-
| [[Save Snapshots in ACP |Save Snapshots]]
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Shared Project Folders
| ✗
| ✗
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Customizable Logo
| ✗
| ✗
|'''✓'''
|'''✓'''
|'''✓'''
|-
| Sessions
| 25 total
| 25 / month
| 500 / month
| 1200 / month
| Unlimited
|-
| Maximum Number of Users
| 1
| 1
| 10
| 25
| Unlimited
|-
| Additional Sessions
| $25 for 25 sessions
| $25 for 25 sessions
| $25 for 25 sessions
| $25 for 25 sessions
| Unlimited
|-
| Maximum File Size
| 25 MB
| 25 MB
| 50MB
| 200MB
| 200 MB
|-
| CPU minutes per calculation
| 1 minute
| 1 minute
| 2 minutes
| 5 minutes
| Unlimited
|-
| Optimizer
|✗
|✗
|✗
|'''✓'''
| Available add-on
|-
| Price
| Free
| Free, included
with support
| $250/month or
$2,000/year
| $5,000/year
| Call (+1) 650-212-1212 or Sales@Lumina.com
|}
*A '''''session''''' is one or model runs between log ins.
**A session starts when you first run a model after logging into ACP.
**Viewing model listings does not use a session.
**A session ends after a run of 20 minutes of inactivity, when switching to a different browser is used, or when you log out.
*You can buy '''extra sessions''' at $25 per 25 sessions at [https://www.lumina.com/shoppingcart/acpcredits/ Buy ACP Session Credits].
*You can buy a Basic Group Plan at [http://www.lumina.com/shoppingcart/acpgrouplan/ Buy/Renew ACP Group Plan]. To buy a Premium Group Plan, call (+1) 650-212-1212 or Sales@Lumina.com.
*The initial 25 sessions for an Individual Account without support never expire. Monthly sessions expire after one month.
*'''Maximum model file size''' is 25MB for individual accounts, 50MB for the Basic Group Plan, and 200MB for the Premium Group Plan or ACP Server License.
*'''''CPU Minutes per calculation: ''' ''For large calculations (e.g. when you click a variable to display), ACP will time out after 1 minute of CPU time for Individual Accounts, 2 minutes for the Basic Group Plan, 5 minutes for the Premium Group Plan, and no limit for ACP on your server. The server is shared, so 2 CPU minutes may take quite a bit more than 2 actual minutes.

===A Basic or Premium Group Plan===
*Multiple users can collaborate on a Group plan.
*Each user has their own account and password.
*Users can be Reviewers (run and read only), Authors (can also upload and delete models), or Administrators (can also set up people as Reviewers and Authors).
*A group plan can have multiple Projects containing a directory of models.
*Each user can be a member of selected Projects. You can’t see models in a Project unless you are member.
*Lumina logo at upper left corner of ACP window can be changed to your organization's logo.

===ACP Server Licenser===
You may purchase an ACP license to install [[ACP Server License|on your own server]]. This lets your users run more or larger models without having to share server resources with other customers. It also lets you access it via a corporate intranet and apply extra security for confidential or proprietary models and data. See [[ACP Server License]]

==Security==
ACP is a secure way to share models:
*ACP uses a https for data transfer
*Data input is not saved unless the user explicitly saves a snapshot of the model
*Snapshots are private by default (although the user can change them to public)
*When a model is uploaded or saved, it can be encrypted using the "lock and encrypt model feature"

==See also==
* [[Differences between ACP and Desktop Analytica]]
* [[ACP Style Library]]
* [[CloudPlayerStyles Attribute Values]]
* [[ACP miscellaneous | ACP: Explanation of some features not mentioned elsewhere]]
* [[ACP Group Accounts | Miscellaneous ACP Group account features]]
* [[ACP Rendering tables and graphs on the diagram]]

* [[Embed an ACP model in a Web Page]]

* [[Save Snapshots in ACP]]
* [[Transferring Data Between ACP and Desktop Analytica]]
*[[Spreadsheets in ACP]]
* [[What's new in ACP?]]
* [[Special ACP features]]
* [[Future ACP Features]]
* Example models:
** [https://www.analyticacloud.com/acp/Client/AcpClient.aspx?inviteId=3&inviteCode=221703&subName=acp%20demos Play the Rent vs. Buy model in ACP.] The Rent vs. Buy model is an example model used in the Analytica Tutorial. It compares the Net Present Value of renting vs. buying a house. Model navigation is fairly intuitive. You can switch diagrams by clicking on modules or selecting different modules in the outline tree located on the left side of the diagram. Clicking on a variable displays its result graph or table. Move the mouse over a node to see its description. Right click on a node and select 'Object view' from the popup menu to view the node's attributes in the object tab, notably its definition.
** [https://www.analyticacloud.com/acp/Client/AcpClient.aspx?inviteId=5&inviteCode=730213&subName=acp%20demos Play the Foxes and Hares model in ACP.] The Foxes and Hares model is a predator-prey example model also used in the Tutorial. When playing this model ACP adds beveled gradients and drop shadows to nodes on the influence diagram. Also, the default tabbed UI has been removed. You can see node descriptions and definitions when you move the cursor over a node. It displays model results directly on the diagram.

ACP Style Library

2016-02-19T23:48:24Z

Jhoy: /* ACP Style Library */

[[Category: Analytica Cloud Player]]

[[CloudPlayerStyles Attribute Values|<<Cloud player styles attribute values]]

=ACP Style Library=

The ACP Style Library helps you select user-interface styles and options available for the Analytica Cloud Player (ACP) useful for web applications, and not available in desktop Analytica. These options include the navigation styles with tabs, outline, or hierarchy views, node and balloon display styles, and using Frame nodes to embed tables and graphs in a Diagram. You select these options in Analytica on the desktop after loading the ACP Style library. The library is in the folder of standard libraries distributed with Analytica. Or you can download it here: [[Media:ACP style library.ana]]

To see the ACP Style library in use, you can watch the recording of the [https://youtu.be/OH3mYa_m0xE Analytica Cloud Player (ACP) Webinar].

== How to use the ACP Style library ==

* Start your model in Analytica in the usual way -- e.g. double-click on the .ana model file.
* Make sure you are in edit mode
* Click '''File''' menu > '''Add Library...'''
* Select the '''ACP Style Library''' from the distributed libraries and click '''Open'''

[[File:Add library01.PNG]]

The 'ACP Style library' will be added to the diagram.

[[File:Add styles library01.PNG]]

Double-click on the '''ACP Style library''' library node to view its main control panel.

[[File:Open styles library01.PNG]]

Then go through the three control panels for Navigation styles, Node styles and Frame nodes. After selecting the options you want, save your model and upload it into ACP to see how they look.

These ACP styles and options put settings in the [[CloudPlayerStyles]] attribute of the model or selected variables or other objects in the model. You could enter these settings directly into the [[CloudPlayerStyles]] attribute, but it's much easier just to use the ACP Style library. That way you don't have to learn about all the names and parameter settings for the [[CloudPlayerStyles]] attribute.

You can only use this library from Analytica, not in ACP. The library is invisible in ACP. Once you have selected the ACP styles you want, you can delete the ACP Styles Library from your model. Your selections model will remain. Since the library file size is over 1 MB, removing it from your model saves time when uploading and running the model in ACP.

=Navigation styles=

In this part of the style library you configure how your model is navigated either using toolbar tabs or the hierarchy header as in Analytica. Or using tabs. You can also control the display of some elements such as those in the banner shown above the diagram. Click the 'Navigation styles' button to get started.

[[File:Styles library 03.png]]

The Navigation styles dialogue window opens...

[[File:ACP Nav Styles Panel01.PNG]]

==Main style==

Set the 'Main style' for playing the model in ACP. Use the 'Main style' pulldown menu depicted here. The preview pane beneath the Pulldown shows a preview of the style as you change the pulldown options.

[[File:Main style01.PNG]]

Currently, we have two styles. The first is 'Analytica model review' which is depicted above. This style is generally used when you want to share your model with another modeler who knows Analytica. The diagrams are basically the same as in Analytica.

The second 'Main style' is 'Web Application', and is depicted below. This is the style you use for creating an application for people who are not familiar with Analytica. In this style, you use a 'tabbed module' UI and the toolbar tabs are removed. Usually you will build a few control panels of input/output nodes which your model users can access via the tabs along the top or tabs along the side. Results are displayed directly on the diagram using 'tall' output nodes or using frame nodes.

[[File:Show as tab noa01.png]]

==Navigation style==

Choose the style for navigation of your model in ACP.

If your 'Main style' is 'Analytica model review' then you can choose to use the 'Outline on Left' or 'Hierarchy headers' navigation styles.

[[File:ACP Outline on left01.PNG]]
[[File:Styles library 12.png]]

If your 'Main style' is 'Web Application' then you choose where you place the tabs used for navigating your models. The tabs can either be located across the top of the diagram or down the left side.

[[File:ACP Tabs down left01.PNG]]

[[File:ACP Tabs across top01.PNG]]

New Styles library feature - download the latest library version here: [[media:ACP style library.ana]]

If your 'Navigation style' is Tabs across top or Tabs down left, you have the option to exclude {Default} the top level diagram and just show the submodules of the top level diagram as tabs, or to show the top level diagram;

This option is set with the checkbox - present when 'Web application' Main style is selected.

[[File:Show as tab noa02.png]]

==ACP Navigation options==

[[File: Styles library 15.png]]

In the ACP Navigation options you can set attributes which control how the banner area above your diagram appears. If your navigation sytle is 'Web application', then only the 'Banner logo and tabs', 'Model title', and 'Auto calc' checkboxes are enabled.

===Banner logo and tabs===
Unchecking the 'Banner logo and tabs' hides the banner space usually present at the top of ACP. The banner typically contains the Lumina Logo, the Parent Diagram button, tabs, Close Model button, and Save button.

[[File:Styles library 16.png]]

===Parent button===
Flag to control the display of the 'Go into Parent' button. Currently the button is shown by default.

[[File:Styles library 17.png]]

===Toolbar tabs===

By clearing this checkbox, you can remove the default ACP tabs that appear at the top of ACP. These are the tabs with titles like "Diagram", "Object", "Edit Table", "Table", "Graph"... Toolbar tabs are not compatible with using 'Tabs across the top' or 'Tabs down the side' navigation styles.

[[File:Styles library 18.png]]

===Use Top diagram size for all windows===
Sets the size of all diagrams based on the size of the diagram window of the top level when the model was last viewed in Desktop Analytica (in non-maximized mode). This is the default in 'Web application' Main style, so this flag is only relevant for the Analytica model review' Main style.

===Model title===
Shows the title of the model at the top to the right of the Lumina (or other) logo. Note this flag only works properly when you also clear Toolbar tabs because the tabs and title will overlap.

[[file:Styles library 19.png]]

===Diagram title===
You can control whether or not to display the diagram's title at the top of the diagram.

[[file:styles library 20.png]]

===Auto calc===
Checking this causes ACP to calculate any result (table or graph) as it displays a diagram window containing the result, and to immediately recalculate any result when the user changes an input on that diagram that affects the result. (It combines Calculate on open and Auto recalc results.) This behavior is unlike Analytica which does not usually calculate results until the user asks for them by clicking the Calc button.

'''Once you have the navigation styles set the way you want, click 'Done'. This will close the Navigation panel and take yo back to the Main ACP Style Library diagram. '''

[[File:ACP style nav Click done.PNG]]

=Node styles=

So now click the Node styles button.

[[File:Styles library 04.png]]

In this part of the style library you configure how nodes are displayed including using beveling and shadows. As well as what happens when one places the mouse over a node in terms of the mouse over effect, and if balloon help will be shown.

[[File:ACP Node Styles Panel01.PNG]]

==Node effect on mouse-over==
Select a highlighting effect for nodes when you move the cursor over the node. The default setting is 'Outline', with 'Glow' and 'None' as the other choices. As you select an effect from the pulldown menu a preview is shown.

[[File:ACP Effect on mouseover01.PNG]]

==Node edge appearance==
In this pane, the 2 checkboxes set flags controlling the appearance of the edge of the nodes. Bevels adds a 3 d bevel to the node. Shadows adds a drop shadow effect. You can select either of these effects or both.

[[File:ACP Node edge appearance01.PNG]]

==Balloon help==
This pane has 2 checkboxes to set whether the identifier and/or the definition is also shown in the balloon, and a pulldown menu which sets the delay before a balloon appears.

By default, ACP displays the description - if any - the units, and the title of an object in a balloon as you place the mouse over it. If there is no description the balloon will not appear.

[[File:Default Balloon wiki071212.PNG]]

'''Identifier in balloon.''' This can be useful when a node is titled 'Net present value' and has an identifier 'Npv' for example.

[[File:Id in balloon wiki071212.PNG]]

'''Definition in balloon.''' When checked, it shows the Definition of a variable in the balloon when you move the mouse cursor over its node.

[[File:Def in Balloon wiki071212.PNG]]

'''Both Definition and Identifier boxes checked'''

[[File:Def id in balloon wiki071212.PNG]]

'''Balloon delay''' When you mouse over a node, there's a short delay of about half a second before it displays the balloon (to prevent wild balloon appearance when you move the cursor rapidly over a diagram.) You can tweak this delay time measured in seconds.

[[File:Balloon delay wiki 071212.PNG]]

==Uncertainty icon in outputs==
In Analytica, normally just to the right of an output node is a little icon indicating the uncertainty view last displayed e.g. mid, mean, prob bands, pdf ...

You can suppress the display of these icons. This might be desirable, for instance, if your model is not probabilistic or if the end users will not know what these icons represent.

[[file:styles library 27.png]]

==Flash buttons==
For button nodes, instead of drawing a button that looks like a button node in desktop analytica, use a Flash button. A Flash button component looks and feels a little bit more like a GUI button used in software applications.

If you are building a web application which uses 'Submit' or other buttons, you might find it looks better using the Flash button component rather than a traditional Analytica button node.

However, if you are using multi-line text or have images embedded in any of the button nodes, then you should not use Flash Buttons since they don't support these features.

[[File:Styles library 28.png]]

'''*Once you have the Node styles set remember to click 'done'. This will apply the node style attributes you have selected to the model and return you to the ACP Style Library diagram'''

[[File:Done node styles.PNG]]

=Frame nodes=

[[File:Styles library 05.png]]

[[File:Styles library 08.png]]

WHAT IS A FRAME NODE?
If you are creating a Web Application which does not use the toolbar tabs, then you probably will want to display tables and graphs on the influence diagram. You can specify the location and size of the tables and graphs on the diagram using a Frame node. A module frame node is just a text node in a module that one tells ACP to use as the location for displaying tables and graphs in that module.

These are used with Web Application navigation styles where the toolbar tabs are not present. If you are not using one of the Web Application navigation styles, then you can skip this step.

HOW TO MAKE A TEXT NODE BE A FRAME NODE

First, create a text node in the module you want to display tables and graphs. Here is a Model with a text node on the top diagram.

[[file:Styles library 30.png]]

==Select frame==

*In the Frame Nodes control panel, select the module where the text node is located from the 'Select module' pulldown menu.

*Select the frame node's identifier from the 'Select frame node' pulldown menu.
(If the text node is newly added to the model, it may not be listed if this variable has not been 'dirtied'. If it is not listed, you need to click the refresh button.)

[[File:Styles library 29.png]]

*Once you have the correct node selected, then check "Use as a module frame node".

[[File:Use as frame node 071212.PNG]]

Once you check this checkbox the options in the field below will be displayed and enabled..

[[File:Set Frame node01 071212.PNG]]

==Select frame node styles==

'''Index Menus''' Controls the display of the Index pulldown menus on the diagram. Unchecked by default, since this saves diagram space, and it is assumed that a modeller will usually choose how he wants people to view the orientation and dimensions of a table/graph in his model on the web. If you want people to be able to view and use these menus in ACP, check this box.

[[File:Index menu no 0071212.PNG]][[File:Index menu yes 0071212.PNG]]

'''Title''' This checkbox controls whether or not the title of the node is shown above the table or graph on the diagram.

'''Description''' This checkbox controls whether or not the description of the node is shown above the table or graph on the diagram.

'''Description length''' Length of description of variable as a percent of Frame height. Not enabled if the 'description' check box is unchecked. If the Description is too long to fit the allotted space, it will be truncated and a scroll bar will be used to view the entire description. (This feature will only display correctly if you have made the frame node large enough to accommodate the scroll bar within the allotted space).

[[File:Descr 50 071212.PNG]]

'''Table and or graph''' When the results of a node are shown, you have the following options, to show just the graph, just the table, show both the table and the graph. If you want the table or graph to be shown based on what was viewed last in Analytica, you can choose 'As saved in Analytica'.

For edit tables, the table will always be displayed regardless of this setting.

[[File:Table over graph 071212.PNG]]

Once you have chosen the settings for your frame node, you can make another frame node, either in a different module or in the same module. If there is more than one Frame, each time you click on a node, it will show its table or graph in the Frame whose contents was displayed the longest ago. Thus, as you click on different nodes, it cycles through the Frames. In this way, you can see and compare edit tables or results from multiple nodes -- all in the same diagram window.

'''Click 'Done' when your settings are complete.'''

Tutorial videos

2016-02-19T23:40:25Z

Jhoy:

New: I've attempted to impose some categorization on the past user group webinar topics.

= General =

=== Analytica Cloud Player (ACP) Webinar ===

'''Date and Time:''' 18 Feb 2016 10:00am Pacific Standard Time

'''Presenter''': Max Henrion, Lumina Decision Systems

'''Abstract'''

In this webinar learn how to use Analytica Cloud Player, for better collaboration and productivity!

Analytica Cloud Player (ACP) lets people collaborate via the web in building and using Analytica models. ACP lets you view and run a model using a web browser, without downloading or installing software.

•Share your models - You can review and run models or web applications.
•Increase Collaboration - You can explore influence diagrams, change inputs, and view result tables and graphs, just like having Analytica on your computer. You can also assign specific roles for more dynamic collaboration.
•Easy to Use - By selecting Publish to Web... from the File menu, you can instantly upload models to ACP from Analytica, ensuring that all users of your model have the latest version.

Watch a recording of this webinar from [https://youtu.be/OH3mYa_m0xE Analytica Cloud Player (ACP) Webinar]

=== New Features in [[Analytica 4.4]] ===

'''Date and Time:''' 19 Jan 2012 10:00am Pacific Standard Time

'''Presenter''': Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

I will give a rapid overview of enhancements and changes in [[Analytica 4.4]] and what these mean for people upgrading from Analytica 4.3.
I'll give quick demonstrations of Expression Assist, Publish to [[ACP|Cloud]], Kernel density smoothing, adjusting legacy models for Clear Type font, Help balloons, and dozens of small improvements that you'll find listed at [[What's new in Analytica 4.4?]]. I'll include a few hidden gems. In following weeks,
we will follow up with more in-depth webinars on specific items.

Watch a recording of this webinar from [http://AnalyticaOnline.com/WebinarArchive/2012-01-19-New-In-Analytica-4.4.wmv New In Analytica 4.4.wmv]

=== Expression Assist ===

'''Date and Time:''' Thursday, 9 Feb 2012 10:00 am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Expression Assist is a new feature in Analytica 4.4 that makes suggestions as you type definitions. This assistance is extremely helpful for beginning modelers and advanced experts alike. It can dramatically speed up the task of writing Analytica expressions, and often provide help, saving you from having to consult a reference elsewhere.

In this webinar, I will demonstrate this new feature, plus show some tricks for getting the most out of it.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2012-02-09-Expression-Assist.wmv New Expression-Assist.wmv].

=== Analytica Web Player ===

'''Date and Time:''' Thursday, August 7, 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Max Henrion, Lumina Decision Systems

'''Abstract'''

The Analytica Web Player (AWP) is scheduled to launch on July 31, 2008. AWP is a subscription service hosted on Lumina's servers. As a subscriber, you can upload your models to the server and send your colleagues a URL so that they can view your model. To view your models, they need only a Flash-enabled web browser. They can browser your model, change inputs, and evaluate results, all from within their web browser.

In this talk we'll cover the available subscription plans, pricing, limitations, and how you sign up. We'll also demonstrate the process of uploading models and sharing these with colleagues.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-08-07-AWP.wmv AWP.wmv].

= Table and Array Topics =

=== The Basics of Analytica Arrays and Indexes ===

''This webinar is continued across two sessions.''

Date and Time (Part 1): January 10, 2008, 10:00 - 11:00 Pacific Standard Time

Date and Time (Part 1, repeat): January 11, 2008, 10:00 - 11:00 Pacific Standard Time

Date and Time (Part 2): January 17, 2008, 10:00 - 11:00 Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

This introductory talk introduces the basic concepts of Analytica indexes and multi-dimensional arrays, as well as the basics of Intelligent Array Abstraction. There are several important differences between Analytica arrays compared to multi-dimensional arrays found in other modeling, database, and programming environments. For example, each dimension of an array is associated with an index object, and there is no inherent ordering to the dimensions of a multi-D array. Intelligent array abstraction is perhaps the most powerful feature in Analytica. The session will include a brief description of what array abstraction does, and how you should take advantage of it.

Part 1 focuses on indexes, 1-D arrays and the uses of the [[Subscript/Slice Operator]].

Part 2 focuses on [[:Category:Array Functions|array functions]], multi-D arrays, and the principles and philosphy of arrays in Analytica.

This talk is intended for beginning Analytica modelers, and for people who have been using Analytica without making substantial use of its array features.

A recording of the two sessions can be viewed at (requires Windows Media Player):
* [http://AnalyticaOnline.com/WebinarArchive/2008-01-11-Intro-to-arrays.wmv Intro-to-arrays (Part 1).wmv]
* [http://AnalyticaOnline.com/WebinarArchive/2008-01-17-Intro-to-arrays2.wmv Intro-to-arrays (Part 2).wmv]

An Analytica model containing the examples created during the webinar can be downloaded from [[media:Intro to intelligent arrays.ana|Intro to intelligent arrays.ana]]. During part 1, the [[media:Plane catching decision with EVIU.ana|Plane catching decision with EVIU.ana]] was also used briefly during the webinar.

=== Manipulating Indexes and Arrays in Analytica Expressions ===

Date and Time: Thursday, Aug 9, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this webinar, I will review many of the common operations applied to indexes and arrays from within Analytica expressions, with a particular emphasis on enhancements in this area that are new to Analytica 4.0. I'll review the often used and very powerful [[Subscript]] and [[Slice]] operations, along with the [[Associative_vs._Positional_Indexing|duality of associational and positional indexing]]. I'll introduce newly introduced extensions for positional indexes, such as the @I, A[@I=n], and @[I=n] operations, and extensions that expose positional duals to various previously-existing associational array functions. I will describe the distinction between index and value contexts in Analytica expressions, along with the distinction between a variable's index value, mid value and sample value, how these may differ ([[Self-Indexed Arrays]]), and how we may access each context-value explicitly. I will also introduce slice assignment -- the ability to assign values to individual slices of an array within an algorithm.

The content of this webinar is most appropriate for users with moderate to advanced Analyica model-building experience.

Here is the Analytica model that was created during this talk: [[media:Indexes and Arrays UG2.ANA | "Indexes and Arrays UG2.ANA"]]. (This wouldn't be very interesting for someone who didn't attend, but it contains the examples we tried).

=== Local Variables ===

'''Date and Time:''' Thursday, 23 July 2009, 10:00-11:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

I'll explain distinctions between different types of local variables that can be used within expressions. These distinctions are of primary interest for people implementing [[Meta-Inference]] algorithms, since they have a lot to do with how [[Handle]]s are treated. Analytica 4.2 introduces some new distinctions to the types of local variables, designed to make the behavior of local variables cleaner and more understandable. One type of local variable is the [[LocalAlias]], in which the local variable identifier serves as an alias to another existing object. In contrast, there is the [[MetaVar]], which may hold a [[Handle]] to another object, but does not act as an alias. The only local variable option that existed previously, declared using [[Var..Do]], is a hybrid of these two, which leads to confusion when manipulating [[handle]]s. Since [[LocalAlias..Do]] and [[MetaVar..Do]] have very clean semantics, the use of these when writing [[Meta-Inference]] algorithm should help to reduce that confusion considerably. Inside a [[User-Defined Function]], parameters are also instances of local variables, and depending on how they are declared, may behave as a [[MetaVar]] or [[LocalAlias]], so I'll discuss how these fit into the picture, as well as local indexes and local indexes.

This is appropriate for advanced Analytica modelers.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2009-07-23-Local-Variables.wmv Local-Variables.wmv]. The analytica file from the webinar is at [[media:Local Variables.ana|Local Variables.ana]], where I've also implemented the exercises that I had suggested at the end of the webinar, so you can look in the model for the solutions.

=== Array Concatenation ===

'''Date and Time:''' Thursday, 25 June 2009 10:00am-11:00 Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Array concatenation combines two (or more) arrays by joining them side-by-side, creating an array having all the elements of both arrays. The special case of list-concatenation joins 1-D arrays or lists to create a list of elements that can function as an index. Array concatenation is a basic, and common, form of array manipulation.

The [[Concat]] function has been improved in Analytica 4.2, so that array concatenation is quite a bit easier in many cases, and the [[ConcatRows]] function is now built-in (formerly it was available as a library function).

I'll take you through examples of array concatenation, including cases that have been simplified with the 4.2 enhancements, to help develop your skills at using [[Concat]] and [[ConcatRows]].

This webinar is appropriate for all levels of Analytica modelers.

You can view a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2009-06-25-Array-Concatenation.wmv Array_Concatenation.wmv]. The model file created during the webinar is: [[media:Array_Concatenation.ana|Array_Concatenation.ana]].

=== Flattening and Unflattening of Arrays ===

Date and Time: January 31, 2008, 10:00 - 11:00 Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

On occassion you may need to flatten a multi-dimensional array into a 2-D table. The table could be called a ''relational representation'' of the data. In some circles it is also refered to as a ''fact table''. Or, you may need to convert in the other direction -- expanding, or unflattening a relational/fact table into a multi-dimensional array. In Analytica, the [[MdTable]] and [[MdArrayToTable]] functions are the primary tools for unflattening and flattening. In this session, I'll introduce these functions and how to use them, several examples, and many variations.

The model developed during this talk is at [[media:Flattening_and_Unflatting_Arrays.ana | Flattening_and_Unflatting_Arrays.ana]]. A recording of the webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-01-31-Array-Flattening.wmv Array-Flattening.wmv]

=== The [[Aggregate]] Function ===

'''Date and Time:''' Thursday, 2 July 2009, 10:00am - 11:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Aggregation is the process of transforming an array based on a fine-grain index into a smaller array based on a coarser-grain index. For example, you might map a daily cash stream into monthly revenue (i.e., reindexing from days to months).

This has always been a pretty common operation in Analytica models, with a variety of techniques for accomplishing it, but it has just become more convenient with the [[Aggregate]] function, new to Analytica 4.2.

In the webinar, I'll be demonstrating the use and generality of the [[Aggregate]] function. In the process, it will also be a chance to review a number of other basic intelligent array concepts, including array abstraction, subscripting, re-indexing, etc.

This webinar is appropriate for all levels of Analytica modelers.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2009-07-02-Aggregate.wmv Aggregate.wmv]. The model file created during this webinar is: [[media:Aggregate_Function.ana|Aggregate Function.ana]].

=== Sorting ===

'''Date and Time:''' Thursday, 6 Aug 2009, 10:00am-11:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This webinar will demonstrate the functions in Analytica that are used to sort (i.e. re-order) data -- the functions [[sortIndex]], [[Rank]], and the new to 4.2 [[Sort]]. I'll cover the basics of using these functions, including how they interact with indexes, how to apply them to arrays of data, and their use with array abstraction. I'll then introduce several new 4.2 extensions for handling multi-key sorts, descending options, and case insensitivity.

This webinar is appropriate for all levels of Analytica modelers.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2009-08-06-Sorting.wmv Sorting.wmv]. The model file created during the webinar is at [[media:Sorting.ana|Sorting.ana]].

=== [[Self-Indexed Arrays|Self-Indexes]], Lists and [[Implicit Dimensions]] ===

Date and Time: January 24, 2008, 10:00 - 11:00 Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

Every dimension of an Analytica array is associated with an index object. [[Array Abstraction]] recognizes when two arrays passed as parametes to an operator or function contain the same indexes. These indexes are more commonly defined by a global index object, i.e., an index object that appears on a diagram as a parallelogram node. However, variable and decision nodes can serve as indexes, and can even have a multi-dimensional value in addition to being an index itself. This is refered to as a [[Self-Indexed Arrays|self index]]. If a variable identifier is used in an expression, the context in which it appears always makes it clear whether the identifier is being used as an index, or as a variable with a value. Self-indexes can arise in several ways, which I will cover. In rare cases, when writing an expression, you may need to be aware of whether you intend to use the index value or the context value of a self-indexed variable. I'll discuss these cases, for example in [[For..Do]] loops, and the use of the [[IndexValue]] function.

In some cases, lists may be used in expressions, and when combined with other results, lists can end up serving as an [[Implicit Dimensions|implicit dimension]] of an array. An implicit dimension is a bit different from a full-fledged index since it has not name, and hence no way to refer to it in an expression where an index parameter is expected. Yet most built-in Analytica functions can still be employed to operate over an implicit index. When an implicit index reaches the top level of an expression, it is promoted to be a self-index. I will explain and demonstrate these concepts.

The model developed during this talk is at [[media:Self-Indexes_Lists_and_Implicit_dimensions.ana|Self-Indexes_Lists_and_Implicit_dimensions.ana]]. A recording of the webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-01-24-Self-Indexes-Implicit-Dims.wmv Self-Indexes-Implicit-Dims.wmv]

=== Introduction to [[DetermTable]]s ===

'''Date and Time:''' 18 September 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

A [[DetermTable]] provides an input view like that of an [[Table|edit table]], allowing you to specify values or expressions in each cell for all index combinations; however, unlike a [[Table|table]], the evaluation of a [[DetermTable|determtable]] conditionally returns only selected values from the table. It is called a determtable because it acts as a deterministic function of one or more discrete-valued variables.
You can conceptualize a determtable as a multi-dimensional generalization of a select-case statement found in many programming languages, or as a value that varies with the path down a decision tree.

[[DetermTable]]s can be used to encode a table of utilities (or costs) for each outcome in a probabilistic model. In this usage, they combine very naturally with [[ProbTable]]s (probability tables) for discrete probabilistic models. They are also extremely useful in combination with [[Choice]] pulldowns, allowing you to keep lots of data in your model, but using only a selected part of that for your analysis. This leads to [[Selective Parametric Analysis]], which is often an effective way of coping with memory capacity limitation in high dimensional models.

In this talk, I'll introduce the [[DetermTable]], show how you create one and describe the requirements for the table indexes. The actual "selection" of slices occurs in the table indexes. Not all indexes have to be selectors, but I'll explain the difference and how the domain attribute is used to establish the table index, while the value is used to select the slice. When you define the domain of a variable that will serve as a [[DetermTable]] index, you have the option of defining the domain as an ''index domain''. This can be extremely useful in combination with a [[DetermTable]], so I will cover that feature as well. It is helpful to understand how the functionality a [[DetermTable]] can be replicated using two nodes -- the first containing an [[Table|Edit Table]] and the second using [[Subscript]]. Despite this equivalence, [[DetermTable]] can be especially convenient, both because it simplifies things by requiring one less node, but also because an [[Table|Edit Table]] can be easily converted into a [[DetermTable]].

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-09-18-DetermTables.wmv DetermTables.wmv]. The examples created while demonstrating the mechanics of [[DetermTable]]s is saved here: [[media:DetermTable intro.ana|DetermTable intro.ana]]. Other example models used were the ''2-branch party problem.ana'' and the ''Compression post load calculator.ana'', both distributed in the Example models folder with Analytica, and the [[media:Loan_policy_selection.ANA|Loan policy selection.ana]] model.

=== [[Table Splicing]] ===

'''Date and Time''': Thursday, August 14, 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Edit tables, probability tables and determ tables automatically adjust when their index's values are altered. When new elements are inserted into an index, rows (or columns or slices) are automatically inserted, and when elements are deleted, rows (or columns or slices) are deleted from the tables. This process of adjusting tables is referred to as ''[[Table Splicing|splicing]]''.

Some indexes in Analytica may be computed, so that changes to some input variables could result in dramatic changes to the index value, both in terms of the elements that appear and the order of the elements in the index. This creates a correspondence problem for Analytica -- how do the rows after the change correspond to the rows before the change. Analytica can utilize three different methods for determining the correspondence: associative, positional, or flexible correspondence. I'll discuss what these are and show you how you can control which method is used for each index.

When slices (rows or columns) are inserted in a table, Analytica will usually insert 0 (zero) as the default value for the new cells. It is possible, however, to explicit set a default value, and even to set a different default for each column of the table. Doing so requires some typescripting, but I'll take you through the steps.

Using blank cells as a default value, rather than zero, has some advantages. It becomes quickly apparent which cells need to be filled in after index items are inserted, and Analytica will issue a warning message if blank cells exist that you haven't yet filled in. I'll take you through the steps of enabling blank cells by default.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-08-14-Edit-Table-Splicing.wmv Edit-Table-Splicing.wmv]. (Note: There is a gap in the recording's audio from 18:43-27:35).

=== [[StepInterp|Step Interpolation]] ===

'''Date and Time:''' Thursday, 8 April 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[StepInterp]] function is useful in a number of scenarios, including:
* Discretizing a continuous quantity into a set of finite buckets.
* Looking up a value from a "schedule table" (e.g., tax-rate table, depreciation table)
* Mapping from a date to its fiscal year, when the fiscal year starts on an arbitrary mid-year date.
* Mapping from a cumulated value back to the index element/position.
* Performing a "nearest" or "robust" [[Subscript]] or [[Slice]] operation.
* Interpolating value for a relationship that changes in discrete steps

In this webinar, I'll demonstrate how to use the [[StepInterp]] function on several simple examples.

This webinar is appropriate for beginning Analytica modelers and up.

You can watch a recording of this webinar at: [http://analyticaonline.com/WebinarArchive/2010-04-08-StepInterp.wmv StepInterp.wmv]. You can download the model created during this webinar from [[media:Step Interp Intro.ana|Step Interp Intro.ana]].

=== [[SubTable|SubTables]] ===

'''Date and Time:''' Thursday, 31 July 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[SubTable]] function allows a subset of another edit table to be edited by the user as a different view. To the user, it appears as if he is editing any other edit table; however, the changes are stored in the original edit table. The rows and columns can be transformed to other dimensions in the Subtable, with different index element orders, based on [[Subset]] indexes, and with different number formats.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-07-31-SubTables.wmv SubTables.wmv]. The model file from this webinar is at [[media:SubTable_webinar.ana]].

=== Edit Table Enhancements in Analytica 4.0 ===

Date and Time: Thursday, Aug 2, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this webinar, I will demonstrate several new edit table functionalities in Analytica 4.0, including:
* Insert [[Choice]] drop-down controls in table cells.
* [[Table Splicing|Splicing]] tables based on computed indexes.
* Customizing the [[TableCellDefault|default cell value(s)]].
* Blank cells to catch entries that need to be filled in.
* [[SubTable]]s
* Using different number formats for each column.

This talk is oriented for model builders with Analytica model-building experience.

The Analytica session that existed by the end of the talk is stored in the following model file: [[Media:Edit_Table_Features.ANA | "Edit Table Features.ana"]].

= Modeling Time =

=== Manipulating Dates in Analytica ===

Date and Time: Thursday, Sept. 13, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this talk, I'll cover numerous aspects relating to the manipulation of dates in Analytica. I'll introduce the encoding of dates as integers and the date origin preference. I'll review how to configure input variables, edit tables, or even individual columns of edit tables to accept (and parse) dates as input. I'll cover date number format capabilities in depth, including how to create your own custom date formats, understanding how date formats interact with your computer's regional settings, and how to restrict a date format to a single column only. We'll also see how axis scaling in graphs is date-aware.

Next, we'll examine various ways to manipulate dates in Analytica expressions. This includes use of the new and powerful functions [[MakeDate]], [[DatePart]], and [[DateAdd]], and some interesting ways in which these can be used, for example, to define date sequences. Finally, we'll practice our array mastery by aggregating results to and from different date granularities, such aggregating from a month sequence to a years, or interpolating from years to months.

The model file resulting by the end of the session is available here: [[media:Manipulating Dates in Analytica.ana|Manipulating Dates in Analytica.ana]].

You can watch a recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-13-Manipulating-Dates.wmv Manipulating Dates.wmv] (Windows Media Player required) Unfortunately, this one seems to have recorded poorly -- the video size is too small. If you magnify it in your media player, it does become readable. Sorry -- I don't know why it recorded like this.

=== The [[Dynamic]] Function ===

'''Date and Time:''' Thursday, 12 June 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Dynamic]] function is used for modeling or simulating changes over time, in which values of variables at time t depend on the values of those variables at earlier time points. Analytica provides a special system index named Time that can be used like any other index, but which also has the additional property that it is used by the [[Dynamic]] function for dynamic simulation.

This webinar is a brief introduction to the use of the Dynamic function and to the creation of dynamic models. I'll cover the basic syntax of the [[Dynamic]] function, as well as various ways in which you can refer to values at earlier time points within an expression. Dynamic models result in influence diagrams that have directed cycles (i.e., where you can start at a node, follow the arrows forward and return to where you started), called dynamic loops. Similar ''cyclic dependencies'' are disallowed in non-dynamic influence diagrams.

During the webinar, we'll loop at several simple examples of Dynamic, oriented especially for those of you with little or no experience with using [[Dynamic]] in models. I'll provide some helpful hints for keeping things straight when building dynamic models. For the more seasoned modelers, I'll also try to fold in a few more detailed tidbits, such as some explanation about how dynamic loops are evaluated, and how variable identifiers are interpreted somewhat differently from within dynamic loops.

The model developed (extension of Fibonacci's rabbit growth model) can be downloaded here: [[media:The Dynamic Function.ana|The Dynamic Function.ana]]. A recording of the webinar can be viewed at
[http://AnalyticaOnline.com/WebinarArchive/2008-06-12-Dynamic-Function.wmv Dynamic-Function.wmv].

=== Modeling Markov Processes in Analytica ===

Date and Time: Thursday, Sept. 20, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Matthew Bingham, Principal Economist, Veritas Economic Consulting

Abstract

The class of mathematical processes characterized by dynamic dependencies between successive random variables is called Markov chains. The rich behavior and wide applicability of Markov chains make them important in a variety of applied mathematical applications including population and demographics, health outcomes, marketing, genetics, and renewable resources. Analytica’s dynamic modeling capabilities, robust array handling, and flexible uncertainty capabilities support sophisticated Markov modeling. In this webinar, a Markov modeling application is demonstrated. The model develops age-structured population simulations using a Leslie matrix structure and dynamic simulation in Analytica.

A recording of this session can be viewed at: [http://AnalyticaOnline.com/WebinarArchive/2007-09-20-Markov-Processes.wmv Markov-Processes.wmv] (requires Windows Media Player)

An article about the model presented here: [[media:AnalyticaMarkovtext.pdf|AnalyticaMarkovtext.pdf]]

= Analytica Language Features =

=== [[Local Indexes]] ===

Date and Time: Thursday, Dec. 13, 2007 at 10:00 - 11:00am Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

A [[Local Indexes|local index]] is an index object created during the evaluation of an expression using either the [[Index..Do]] or [[MetaIndex..Do]] construction. Local indexes may exist only temporarily, being reclaimed when they are no longer used, or they may live on after the evaluation of the expression has completed, as an index of the result. Some operations require the use of local indexes, or otherwise could not be expressed.

In this talk, I'll introduce simple uses of local indexes, covering how they are declared using [[Index..Do]], with several examples. We'll see how to access a local index using the [[Dot operator::A.I|A.I]] operator. I'll discuss the distinctions between local indexes and local variables. I'll show how the name of a local index can be computed dynamically, and I'll briefly cover the [[IndexNames]] and [[IndexesOf]] functions.

The model created during this talk is here: [[media:Webinar_Local_Indexes.ana|Webinar_Local_Indexes.ana]].

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-12-13-Local-Indexes.wmv Local-Indexes.wmv] (Requires Windows Media Player)

=== [[Handle]]s and [[Meta-Inference]] ===

Date and Time: Thursday, Dec. 6, 2007 at 10:00 - 11:00am Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

Meta-inference refers to computations that reason about your model itself, or that actually alter your model. For example, if you were to write an expression that counted how many variables are in your model, you would be reasoning about your model. Other examples of meta inference include changing visual appearance of nodes to communicate some property, re-arranging nodes, finding objects with given properties, or even creating a transformed model based on portion of your model's structure.

The ability to implement meta-inferential algorithms in Analytica has been greatly enhanced in Analytica 4.0. The key to implementation of meta-inference is the manipulation of [[Handle]]s to objects (formerly refered to as ''varTerms''). This webinar will provide a very brief introduction to handles and using them from within expressions. I will assume you are pretty familiar with creating models and writing expressions in Analyica, but I will not assume that have previous seen or used Handles. This topic is oriented towards more advanced Analytica users.

The model used/created during this webinar as at: [[media:Handle and MetaInference Webinar.ANA|Handle and MetaInference Webinar.ANA]].

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-12-06-Handles.wmv Handles.wmv] (Requires Windows Media Player)

=== The [[Iterate]] Function ===

Date and Time: Thursday, Nov. 29, 2007 at 10:00 - 11:00am Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

With [[Iterate]], you can create a recurrent loop around a large model, which can be useful for iterating until a convergence condition is reached, for example. For complex iterations, where many variables are being updated at each iteration, requires you to structure your model appropriate, bundling and unbundling values within the single iterative loop. With some work, [[Iterate]] can be used to simulate the functionality [[Dynamic]], and thus provides one option when a second [[Time]]-like index is needed (although not nearly as convenient as [[Dynamic]]).

In this session, we'll explore how [[Iterate]] can be used.

Here is the model file developed during the webinar: [[media:Iterate Demonstration.ANA|Iterate Demonstration.ANA]]

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-11-29-Iterate.wmv Iterate.wmv] (Requires Windows Media Player)

=== The [[Using_References|Reference and Dereference Operators]] ===

Date and Time: Thursday, Nov. 15, 2007 at 10:00 - 11:00am Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract
The reference operators make it possible to represent complex data structures like trees or non-rectangular arrays, bundle heterogenous data into records, maintain arrays of local indexes, and seize control of array abstraction in a variety of scenarios. Using a reference, an array can be made to look like an atomic element to array abstraction, so that arrays of differing dimensionality can be bundled into a single array without an explosion of dimensions. The flexibilities afforded by references are generally for the advanced modeler or programmer, but once mastered, they come in useful fairly often.

Here is the model used during the webinar: [[media:Webinar Reference and Dereference Operators.ANA|Reference and Dereference Operators.ana]].
Near the end of the webinar, I encountered a glitch that I was not able to resolve until after the webinar was over. This has been fixed in the attached model. For an explanation of what was occurring, see:
[[Analytica_User_Group/Reference_Webinar_Glitch]].

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-11-15-Reference-And-Dereference.wmv Reference-And-Dereference.wmv] (Requires Windows Media Player)

=== Writing [[User-Defined Functions]] ===

Date and Time: Thursday, Sept. 27, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

When you need a specialized function that is not already built into Analytica, never fear -- you can create your own [[User-Defined Functions|User-Defined Function (UDF)]]. Creating UDFs in Analytica is very easy. I'll introduce this convenient capability, and demonstrate how UDFs can be organized into libraries and re-used in other models. I'll also review the libraries of functions that come with Analytica, providing dozens of additional functions.

After this introduction to the basics of UDFs, I'll dive into an in-depth look at [[Function Parameter Qualifiers]]. There is a deep richness to function parameter qualifiers, mastery of which can be used to great benefit. One of the main objectives for a UDF author, and certainly a hallmark of good modeling style, should be to ensure that the function fully array abstracts. Although this usually comes for free with simple algorithms, it is sometimes necessary to worry about this explicitly. I will demonstrate how this objective can often be achieved through appropriate function parameter qualification.

Finally, I will cover how to write a custom distribution function, and how to ensure it works with [[Mid]], [[Sample]] and [[Random]].

This talk is appropriate for Analytica modelers from beginning through expert level. At least some experience building Analytica models and writing Analytica expressions is assumed.

The model created during this webinar, complete with the UDFs written during that webinar, can be downloaded here: [[media:Writing User Defined Functions.ana|Writing User Defined Functions.ana]].

You can watch this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-27-Writing-UDFs.wmv Writing-UDFs.wmv] (Windows Media Player required)

=== Custom Distribution Functions ===

'''Date and Time:''' Thursday, 24 July 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Analytica comes with most of the commonly seen [[:category:Distribution Functions|distributions]] built-in, and many additions [[:category:Distribution Functions|distribution functions]] available in the standard libraries. However, in specific application areas, you may encounter distribution types that aren't already provided, or you may wish to create a variation on an existing distribution based on a different set of parameters. In these cases, you can create your own ''User-Defined Distribution Function'' (UDDF). Once you've created your function, you can utilize it within your model like you would any other distribution function.

User-defined distribution functions are really just instances of [[User-Defined Functions]] (UDFs) that behave in certain special ways. This webinar discusses the various functionalities that a user-defined distribution function should exhibit and various related considerations. Most fundamentally, the defining feature of a UDDF is that it returns a median value when evaluated in Mid mode, but a sample indexed by Run when evaluated from Sample mode. This contrasts with non-distribution functions whose behavior does not depend on the Mid/Sample evaluation mode. Custom distributions are most often implemented in terms of existing distributions (which includes Inverse CDF methods for implementing distributions), so that this property is achieved automatically since the existing distributions already have this property. But in less common cases, UDDFs may treat the two evaluation modes differently.

When you create a UDDF, you may also want to ensure that it works with [[Random]]() to generate a single random variate, and supports the [[Distribution Functions#The Over parameter|Over parameter]] for generating independent distributions. You may also want to create a companion function for computing the density (or probability for discrete distributions) at a point, which may be useful in a number of contexts including, for example, during importance sampling. I'll show you how these features are obtained.

There are several techniques that are often used to implement distribution functions. The two most common, especially in Analytica, are the ''Inverse CDF'' technique and the ''transformation from existing distributions'' method. I'll explain and show examples of both of these. The Inverse CDF is particularly convenient in that it supports all sampling methods (Median Latin Hypercube, Random Latin Hypercube, and Monte Carlo).

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-07-24-Custom-Distribution-Functions.wmv Custom-Distribution-Functions.wmv]. The model file created during the webinar is [[media:Custom Distribution Functions.ana|Custom Distribution Functions.ana]].

=== [[Regular Expressions]] ===

'''Date and Time:''' Thursday, 9 July 2009, 10:00am - 11:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Analytica 4.2 exposes a new and powerful ability to utilize Perl-Compatible regular expressions for text expression analysis. This feature has particular applicability for parsing application when importing data. Long known as the feature that makes Perl and Python popular as data file processing languages, that same power is now readily available within Analytica's [[FindInText]], [[SplitText]], and [[TextReplace]] functions.

This talk will only touch on the regular expression language itself (information on which is readily available elsewhere), but instead focuses on the use of these expressions from the Analytica expressions, especially the extracting of text that matches to subpatterns and finding repeated matches.

One relatively complex example that I plan to work through is the parsing of census population data from datafiles downloaded from the U.S. census web site. The task includes parsing highly variable HTML, as well as multiple CSV files with formatting variations that occur from element to element. These variations, which are typical in many sources of data, demonstrate why the flexibility of regular expressions can be extremely helpful when parsing data files.

Regular expressions themselves are extremely powerful, but when overused, can be very cryptic. So even though it is possible to get carried away with this power, it is good to know how to balance the temptation.

This talk is appropriate for moderate to advanced level modelers.

A recording of this webinar can be watched at [http://AnalyticaOnline.com/WebinarArchive/2009-07-09-Regular-Expressions.wmv Regular-Expressions.wmv]. If you are new to regular expressions, I've included a slides on the regular expression patterns that I made use of in [[media:Regular_Expressions.pps|this power point show]] (these were not shown during the webinar). The model file developed during the webinar is [[media:Regular expressions.ana|Regular expressions.ana]].

=== Using the [[Check Attribute]] to validate inputs and results ===

''' Date and Time:''' Thursday, 17 July 2008 10:00 Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Check Attribute|check attribute]] provides a way to validate inputs and computed results. When users of your model are entering data, this can provide immediate feedback when they enter values that are out of range or inconsistent. When applied to computed results, it can help catch inconsistencies, which can help reduce error rates and accidental introduction of errors later.

In this talk, I'll demonstrate how to define a check validation for a variable, and how to turn on the check attribute visible so that it is visible in the object window. I'll demonstrate how the failed check alert messages can be customized. And perhaps most interestingly, how the check can be used in edit tables for cell-by-cell validation, so that out-of-range inputs are flagged with a red background, and alert balloons pop-up when out-of-range inputs are entered. Cell-by-cell validation when certain restrictions on the check expression are followed, which I'll discuss.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-07-17-Check-Attribute.wmv Check-Attribute.wmv] (Note: There is audio, but screen is black, for first 50 seconds). The model used during this webinar, with the check attributes inserted, is at [[media:Check attribute -- car costs.ana|Check attribute -- car costs.ana]].

=== The Performance Profiler ===

'''Date and Time:''' October 9, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

''Requires Analytica Enterprise''

When you have a model that takes a long time to compute, thrashes in virtual memory, or uses up available memory, the Performance Profiler can tell you where your model is spending its time and how much memory is being consumed by each variable to cache results. It is not uncommon to find that even in a very large model, a small number (e.g., 2 to 5) of variables account for the lion's share of time and memory. With this knowledge, you can focus your attention optimizing the definition of those few variables. On several occassions I've achieved more than 100-fold sped up in computation time on large models using this technique.

The Performance Profiler requires with Analytica Enterprise or Optimizer. I'll demonstrate how to use the profiler with some basic discussions of what is does and does not measure. One neat aspect of the profiler is that you can actually activate it after the fact. In otherwords, even though you haven't adding profiling to your model, if you happen to notice something taking a long time, you can add it in to find out where the time was spent.

Using the Profiler is pretty simple, so I expect this session will be somewhat shorter than usual. The content will be oriented primarily to people who are unfamiliar with the profiler, although I will also try to provide some behind the scenes details and can answer questions about it for

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-10-09-Performance-Profiler.wmv Performance-Profiler.wmv]. The model file containing the first few examples from the webinar can be downloaded from [[media:Simple Performance Profiler Example.ana|Simple Performance Profiler Example.ana]].

= Organizing Models =

=== Modules and Libraries ===

'''Date and Time:''' 10 Dec 2009 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Modules form the basic organizational principle of an Analytica model, allowing models to be structured hierarchically, keeping things simple at every level even in very large complex models. You can use linked modules to store your model across multiple files. This capability enables reuse of libraries and model logic across different models, and allows you to divide your model into separate pieces so that different people can work concurrently on different pieces of the model.

In this talk, I will review many aspects of modules and libraries. We'll see how to use linked modules effectively. I'll cover what the the distinctions are between Modules, Libraries, Models and Forms. I'll demonstrate various considerations when adding modules to existing models -- such as whether you want to import system variables or merge (update) existing objects, and some variations on what is possible there. We'll see how to change modules (or libraries) from being embedded to linked, or vise versa, and how to change the file location for a linked module. When distributing a model consisting of multiple module files, I'll go over directory structure considerations (the relative placement of module files), and also demonstrate how you can store a copy of your model with everything embedded in a single file for easy distribution.

I'll also discuss definition hiding and browse-only locking. By locking individual modules, you can create libraries with hidden and unchangeable logic that can be used in the context of other people's models, keeping your algorithms hidden. Or, you can distribute individual models that are locked as browse only, even in the context of a larger model where the remainder of the model is editable.

I'll talk about using linked modules in the context of a source control system, which is often of interest for projects where multiple people are modifying the same model. I'll also reveal an esoteric feature, the Sys_PreLoadScript attribute, and how this can be used to implement your own licensing and protection of intellectual property.

This webinar is appropriate for all levels of Analytica model builders.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2009-12-10-Linked-Modules.wmv Linked-Modules.wmv]. The starting model used in the webinar can be downloaded from [[media:Loan_policy_selection_start.ana|Loan_policy_selection_start.ana]], and then you can follow along to introduce and adjust modules as depicted in the recording if you like.

= Uncertainty & Probability Topics =

=== Gentle Introduction to Modeling Uncertainty: Webinar Series ===

'''Date and Time:'''
:'''Session 1:''' Thursday, 29 Apr 2010 10:00am Pacific Daylight Time
:'''Session 2:''' Thursday, 6 May 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Are you someone who has never built a model containing explicit representions of uncertainty? Did that Statistics 1A class you took a long time ago instill a belief that probability distributions are irrelevant to the type stuff you work on? Are you afraid to start representing uncertainty explicitly because you just don't have the statistics background and don't know much about probability and probability distributions?

If any of these sentiments resonate with you, then this webinar (series) may be for you.

These are interactive webinars. Be prepared to answer some questions, and have Analytica fired up in the background. You are going to use it to compute the answer to a couple exercises! Even if you are watching the recording, be ready to complete the exercises.

This webinar series is most appropriate for:
* Beginning Analytica model builders.
* Users of models that present results with uncertainty.
* Accomplished spreadsheet or Analytica model builders who have not previously incorporated uncertainty.
* People looking to learn the basics of probability for the representation of uncertainty.

==== Session 1: Uncertainty and Probability ====

In the first session discusses different sources and types of uncertainty, probability distributions and how they can be used to represent uncertainty, various different interpretations of probabilities and probability distributions, and reasons why it is valuable to represent uncertainty explicitly in your quantitative models.

A recording of this webinar can be viewed at: [http://AnalyticaOnline.com/WebinarArchive/2010-04-29-Modeling-Uncertainty1.wmv Modeling-Uncertainty1.wmv]. A copy of the model created by the presenter during the webinar (the scholarship example) can be downloaded from [[media:Modeling uncertainty 1 - princeton scholarship.ana|Modeling uncertainty 1 - princeton scholarship.ana]]. Power point slides can be downloaded from: [[media:Modeling Uncertainty 1.ppt|Modeling Uncertainty 1.ppt]].

==== Session 2: Probability Distributions ====

How do you characterize the amount of uncertainty you have regarding a real-valued quantity? This second session explores this question, and introduces the concepts of average deviation (aka absolute deviation), variance and standard deviation. It then introduces the concept of a ''probability distribution'' and the [[Normal]] and [[LogNormal]] distributions. We examine the [http://en.wikipedia.org/wiki/Expected_value_of_including_uncertainty expected value of including uncertainty] and do a few modeling exercises that demonstrate how it can be highly misleading, even expensive, to ignore uncertainty.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2010-05-06-Prob-Distributions.wmv Prob-Distributions.wmv]. The model build during the webinar can be downloaded from [[media:Probability Distributions Webinar.ana|Probability Distributions Webinar.ana]]. Power point slides are at [[media:Modeling Uncertainty 2.ppt|Modeling Uncertainty 2.ppt]].

==== Session 3: Monte Carlo ====

'''Date and Time:''' Thursday, 13 May 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

In this third webinar in the "Gentle Introduction to Modeling Uncertainty" series, we will see how a probability distribution can be represented as a set of representative samples, and how this leads to a very general method for propagating uncertainty to computed results. This method is known as Monte Carlo simulation.

Analytica represents uncertainty by storing a representative sample, so we'll be learning about how Analytica actually carries out uncertainty analysis. We explore how all the uncertainty result views in Analytica are created from the sample, and learn various 'tricks' for nice histograms for PDF views in various situations.

We'll learn about the [[Run]] index, and how this places samples across different variables in correspondence. We'll learn about the generality of the Monte Carlo for propagating uncertainty, and also learn what Latin Hypercube sampling is.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2010-05-13-Monte-Carlo.wmv Monte-Carlo.wmv]. The power point slides are at: [[media:Monte Carlo Simulation.ppt|Monte Carlo Simulation.ppt]]. Example models created during the webinar include: [[media:Representing Uncertainty - Mining Example.ana|Mining Example.ana]], [[media:Representing Uncertainty - Explicit samples.ana|Explicit samples.ana]]and [[media:Representing Uncertainty 3 - Misc.ana|Representing Uncertainty 3 - Misc.ana]] (product of normals and comparison between Latin Hypercube and Monte Carlo precision).

==== Session 4: Measures of Risk and Utility ====

'''Date and Time:''' Thursday, 20 May 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This fourth webinar in the "Gentle Introduction to Modeling Uncertainty" series will explore concepts and quantitative measures of Risk and Utility. We'll discuss various conceptions and types of risk, and explore topics relevant to model-building that include utility and loss functions, expected value, expected utility, risk neutrality, risk aversion, fractiles and Value-at-risk (VaR).

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2010-05-20-Risk-And-Utility.wmv Risk-And-Utility.wmv]. The power point slides can be viewed at [[media:Measures of Risk and Utility.ppt|Measures of Risk and Utility.ppt]]. There is an interesting modeling exercise and exploration of Expected Shortfall near the end of the power point slides that was not covered during the webinar. The worked out model examples from the webinar, along with a solution to the final example not covered, can be downloaded from [[media:Measures of Risk.ana|Measures of Risk.ana]].

==== Session 5: Risk Analysis for Portfolios ====

'''Date and Time:''' Thursday, 3 June 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Committing to a single project or investing in a single asset entails a certain amount of risk along with the potential payoff. If you are able to proceed with multiple projects or invest in multiple assets, the degree of risk may be reduced substantially with small impact on potential return. In this fifth webinar in the "Gentle Introduction to Modeling Uncertainty" series, we'll look at modeling portfolios, such as portfolios of investments or portfolios of research and development projects, and the impact this has on risk and return. Portfolio analysis is the basis for practices such as diversification and hedging, and is a key of risk management.

As with other topics in this webinar series, the presentation and discussion is designed for people who are new to the use of these concepts in a model building context.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2010-06-03-Portfolio-Risk.wmv Portfolio-Risk.wmv]. The Power Point slides are at [[media:Risk Analysis for Portfolios.ppt|Risk Analysis for Portfolios.ppt]]. These include some exercises at the end (for homework!) not covered during the webinar, including continuous portfolio allocations. The model developed during the webinar, augmented to include answers to additional exercises is at [[media:Risk Analysis for Portfolios.ana|Risk Analysis for Portfolios.ana]].

==== Session 6: Common Parametric Distributions ====

'''Date and Time:''' Thursday, 10 June 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

During the first five sessions of the ''Gentle Introduction to Modeling Uncertainty'' webinar series, you have been introduced to three distribution functions: [[Bernoulli]], [[Normal]] and [[LogNormal]]. In this webinar, we're going to increase this repertoire and learn about other common parametric distributions. I'll discuss situations where specific distributions are particularly convenient or natural for expressing uncertainty about certain types of quantities, and other reasons for why you might prefer one particular distribution type over another. We'll also examine the distinction between discrete and continuous distributions.

As with other topics in this webinar series, the presentation and discussion is designed for people who are new to the use of these concepts in a model building context.

A recording of the webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2010-06-10-Parametric-Distributions.wmv Parametric-Distributions.wmv]. The power point slides are at [[media:Common Parametric Distributions.ppt|Common Parametric Distributions.ppt]], and the Analytica model containing the exercises and solutions to exercises not covered during the live recording is at [[media:Common-Parametric-Distributions.ana|Common-Parametric-Distributions.ana]].

==== Session 7: Expert Assessment of Uncertainty ====

'''Date and Time:''' Thursday, 24 June 2010 10:00am Pacific Daylight Time

'''Presenter:''' Max Henrion, Lumina Decision Systems

'''Abstract'''

For most uncertainty analysis, uncertainties about many key quantities must be assessed by expert judgment. There has been a lot of empirical research on human abilities to express their knowledge and uncertainty in the form of probability distributions. It shows that we are liable to a variety of biases, such as overconfidence and motivational biases. I'll give an introduction to practical methods developed by decision analysts to avoid or minimize these biases. I'll give some examples from recent work in expert elicitation for the Department of Energy on the future performance of renewable energy technologies.
I'll also discuss ways to aggregate judgments from different experts.

The session is appropriate for people who are new to this area. This probably includes just about everybody!

This session will draw from Chapters 6 and 7 of "Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis" by M Granger Morgan & Max Henrion, Cambridge University Press, 1992

Note: There is no recording of this webinar.

==== Session 8: Hypothesis Testing ====

'''Date and Time:''' Thursday, 15 July 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Hypothesis testing from classical statistics addresses the question of whether the apparent support for a given hypothesis is statistically significant. In the field of classical statistics, this is perhaps the most heavily emphasized application of probability concepts, and the methodology is used (if not required by editors) when publishing results for research studies in nearly every field of empirical study.

To illustrate the basic idea, suppose a journalist selects 10 Americans at random and asks whether they support a moratorium on deep sea drilling. Seven of the 10 respond "yes", so the the next day he publishes his article "The Majority of Americans Support a Moratorium on Deep Sea Drilling". His sample is certainly consistent with this hypothesis, but his conclusion is not credible because with such a small sample, this majority could have easily been a random quirk (sampling error). Hence we would say that the conclusion is not "''statistically significant''". But how big does the sample have to be to achieve statistical significance? Where should we draw the line when determining whether the data's support is statistically significant? These are the types of questions addressed by this area of statistics.

Hypothesis testing is a central topic in every introductory Statistics 1A course, often comprising more than half of the total course syllabus. But most introductory courses emphasize a cookbook approach in favor of a conceptual understanding, apparently in the hope of providing people in non-statistical fields step-by-step recipes to follow when they need to publish results in their own fields. As a result, the methodology is possibly misused more often than it is applied correctly, and published results are commonly misinterpreted.

In this seminar, I intend to emphasize a conceptual understanding of the statistical hypothesis methodology rather than the more traditional textbook methodology. After this webinar, when you read "our hypothesis was confirmed by the data at a p-value=0.02 level", or "the hypothesis was rejected with a p-value of 0.18", you should be able to precisely relay what these statements really do or do not imply. You should understand what a p-value and confidence level really denote -- they do not represent, as many people think, the probability that the hypothesis is true.

We will also, of course, examine how we can carry out computations of significance levels (i.e., p-values) within Analytica. Statistics texts are filled with numerous "standard" hypothesis tests (e.g., t-tests, etc), each based on a specific set of assumptions. In this webinar, we'll dive into this in a more general way, where we get to start with our own set of arbitrary assumptions, leveraging the power of Monte Carlo for computation. This means there are no recipes to remember, you can compute significance levels for any statistical model, even if the same assumptions don't appear in your statistics texts, and most importantly, you'll be left with a more general understanding of the concepts.

As a prerequisite, this webinar will assume little more than the introductory background from the earlier webinars in this "Gentle Introduction to Modeling Uncertainty" series. It is appropriate for people who have never taken a Statistics 1A course, or for the majority of people who have taken that introduction to Statistics but could use a refresher.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2010-07-15-Hypothesis-Testing.wmv Hypothesis-Testing.wmv]. To follow along with the webinar, you'll want to also download the Analytica model file [[media:Hypothesis Test S&P Volatility.ana|Hypothesis Test S&P Volatility.ana]] before staring. You'll use the data in that model for the various exercises during the webinar.

Solutions to exercises are saved in this version of the model (created during the webinar): [[media:Hypothesis Test S&P Volatility solution.ana|Hypothesis Test S&P Volatility solution.ana]]. I also inserted a solution to the Parkinson's data test that wasn't covered in the webinar but is contained in the [[media:Hypothesis Testing.ppt|Power Point slides]].

=== Expecting the Unexpected: Coping with surprises in Probabilistic and Scenario Forecasting ===

'''Date and Time:''' Thursday, 7 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Max Henrion, Ph.D., Lumina Decision Systems

'''Abstract'''

The notion of "Black Swans", reinforced by the financial debacles of 2008, confirms decades of research on expert judgment and centuries of anecdotes about the perils of prediction: Our forecasts are consistently overconfident and we are too often surprised. Henrion will explain why forecasters, risk analysts, and R&D portfolio managers should embrace the inevitable uncertainties using scenarios or probability distributions. He will describe a range of practical methods including:
* The value of knowing how little you know — why and when to treat uncertainty explicitly
* Elicitation of expert judgment and how to minimize cognitive biases
* Using Monte Carlo for probabilistic forecasting and risk analysis.
* Calibrating probabilistic forecasts against the historical distributions of forecast errors and surprises.
* Brainstorming to identify "Gray Swans" — surprises that are foreseeable, but ignored in conventional forecasting.
Participants will come away with a deeper understanding of when and how to apply these methods.

You can download the PowerPoint slides used for the webinar: [[media:Expecting the Unexpected.pptx|Expecting the Unexpected.pptx]] (note: If your browser changes this into *.zip when downloading, save it and rename to "Expecting the Unexpected.pptx" before you try to open it). You can watch a recording of the webinar from [http://AnalyticaOnline.com/WebinarArchive/2011-04-07-Expecting-the-Unexpected.wmv ExpectingTheUnexpected.wmv].

=== Correlated and Multivariate Distributions ===

'''Date and Time:''' Thursday, March 13, 2008 10:00 Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This talk will discuss various techniques within Analytica for defining probability distributions with specified marginal distributions, and also being correlated with other uncertain variables. Techniques include the use of conditional and hierarchical distributions, multivariate distributions, and Iman-Conover rank-correlated distributions.

The model created during session talk is [[media:Correlated distributions.ana|Correlated distributions.ana]]. You can watch a recording of the webinar from [http://AnalyticaOnline.com/WebinarArchive/2008-03-13-Correlated-Distributions.wmv Correlated-Distributions.wmv].

=== Assessment of Probability Distributions ===

'''Date and Time:''' March 6, 2008 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

''' Abstract'''

When building a quantitative model, we usually need to come up with estimates for many of the parameters and input variables that we use in the model. Because these are estimates, it is good idea to encode these as probability distributions, so that our degree of ''subjective uncertainty'' is explicit in the model. The process of encoding a distribution to reflect the level of knowledge that you (or the experts you work with) have about the true value of the quantity is referred to as ''probability (or uncertainty) assessment'' or ''probability elicitation''.

This webinar will be a highly interactive one, where all attendees are expected to participate in a series of uncertainty assessments as we explore the effects of cognitive biases (such as over-confidence and anchoring), understand what it means to be ''well-calibrated'', and utilize scoring metrics to measure your own degree of calibration. These exercises can help you improve the quality of your distribution assessments, and serve as tools that can help you to when eliciting estimates of uncertainty from other domain experts.

The Analytica model [[media:Probability assessment.ana|Probability assessment.ana]] contains a game of sorts that takes you through several probability assessments and scores your responses. Participants of the webinar played this game by running this model, if you are going to watch the webinar, you will want to do the same. You may want to wait until the appropriate point in the webinar (after preliminary stuff has been covered) before starting. You can watch the webinar recording here: [http://AnalyticaOnline.com/WebinarArchive/2008-03-06-Probability-Assessment.wmv Probability-Assessment.wmv].
The power point slides from the talk are here: [[media:Assessment_of_distributions.ppt|Assessment_of_distributions.ppt]].

=== Statistical Functions ===

'''Date and Time:''' Thursday, 21 Aug 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

''This topic was presented in Aug 2007, but not recorded at that time.''

A statistical function is a function that process a data set containing many sample points, computing a "statistic" that summarizes the data. Simple examples are [[Mean]] and [[Variance]], but more complex examples may return matrices or tables. In this talk, I'll review statistical functions that are built into Analytica. I'll describe several built-in statistical functions such as [[Mean]], [[SDeviation]], [[GetFract]], [[Pdf]], [[Cdf]], and [[Covariance]]. I'll demonstrate how all built-in statistical functions can be applied to historical data sets over an arbitrary index, as well as to uncertain samples (the Run index). I'll discuss how the domain attribute should be utilized to indicate that numeric-valued data is discrete (such as integer counts, for example), and how various statistical functions (e.g., [[Frequency]], [[GetFract]], [[Pdf]], [[Cdf]], etc) make use of this information. In the process, I'll demonstrate numerous examples using these functions, such things as inferring sample covariance or correlation matricies from data, quickly histogramming arbitrary data and using the coordinate index setting to plot it, or using a weighted [[Frequency]] for rapid aggregation.

In addition, all built-in statistical functions can compute weighted statistics, where each point is assigned a different weight. I'll briefly touch on this feature as a segue into next week's topic, Importance Sampling.

This talk can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-08-21-Statistical-Functions.wmv Statistical-Functions.wmv]. The model built during this talk is available for download at [[media:Intro to Statistical Functions.ana|Intro to Statistical Functions.ana]].

=== [[RankCorrel|Spearman Rank Correlation]] ===

'''Date and Time:''' Thursday, 25 March 2010 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Many measures for quantifying the degree of statistical dependence between quantities are used in statistics. THe two most commonly used are [[Correlation|Pearson's Linear Correlation]] and [[RankCorrel|Spearman's Rank Correlation]], computed respectively in Analytica by the functions [[Correlation]] and [[RankCorrel]]. Pearson's [[Correlation]], which is what people usually mean when they just use the term "Correlation", is a measure of how linear the relationship between two variables is. Spearman's [[RankCorrel|Rank Correlation]] is a measure of monotonic the relationship between two variables is.

This talk provides an introduction to the concept of [[RankCorrel|rank correlation]], how it is distinguished from standard [[Correlation|Pearson correlation]], and what it measures. There are several notable and rather diverse uses of [[RankCorrel]], which include these (and probably many others):
* A quantitative measure of the degree to which two variables are monotonically related. (E.g., the degree to which an increase in one leads to an increase, or decrease, in the other).
* Testing (from measurements) whether two factors are statistically dependent
* Importance analysis: Determining how much the uncertain of an input contributes to the uncertainty of an output.
* Sampling from joint distributions with arbitrary marginals and specified rank-correlations ([[Correlate_With]] and [[Correlate_Dists]])

I will focus mostly on the first two factors in this talk (previous webinars on [[#Sensitivity_Analysis_Topics|Sensitivity Analysis]] have covered the Importance Analysis usage to some extent, and a previous webinar on [[#Correlated_and_Multivariate_Distributions|Correlated and Multivariate Distributions]] has covered the last point).

Standard hypothesis tests exist for determining whether two factors are statistically dependent by testing the hypothesis that their [[RankCorrel|rank correlation]] is non-zero (null hypothesis that it is zero). When the P-value of these tests is less than 5% (or 1%), you would be justified in concluding that the two variables are statistically dependent. I will demonstrate how to compute this P-value.

Then I will introduce a new analysis of [[RankCorrel|rank correlation]] that I came up with, which I think is novel and potentially pretty useful, somewhat related to the classical hypothesis tests just mentioned. Suppose you gather a small sample of data on two variables in a study and you want to determine how strong the monotonicity between the two variables is. You can compute the ''sample rank correlation'' for the data set, but this is only an estimate since you have a small sample size and thus sampling error may throw off this estimate. So suppose we imagine there is some "true" underlying rank correlation between the variables (this in itself is a new concept, which I will make precise). From your data set, you have some knowledge about the true value of this underlying rank correlation -- the larger your sample size, the more precise your knowledge is. The new technique I describe here computes a (posterior) distribution over the true underlying rank correlation, from which you can express your rank correlation result as a range (such as rc=0.6±0.2), and answer questions such as what is the probability that the underlying rank correlation is between -0.1 and 0.1, P(-0.1 < rc < 0.1), or P(rc>0), etc. Although this is essentially a posterior distribution, there is no prior distribution involved or needed to computate it, so it is simply a function of the measured data and of the sample size. It really is a probability distribution on the underlying rank correlation, not just a P-value, making it much more useful.

This new analysis is also useful for quantifying the probability that two factors are independent in a manner not possible with the classical tests. The classical P-value of the aforementioned tests measure the probability of a Type II error for the hypothesis that variables are dependent. These tests do not provide the probability of a Type I error, which would be the criteria for concluding that a claim of statistical independence is statistically signficant. This new measure, however, can justifiably be used for quantifying a claim of statistical independence since it allows P(-c<rc<c) to be computed for any c.

I will demonstrate how this new analysis of rank correlation works and is encoded within Analytica, and show how to read off the interesting results.

A recording of this webinar can be viewed at: [http://AnalyticaOnline.com/WebinarArchive/2010-03-25-Rank-Correlation.wmv Rank-Correlation.wmv]. The model files created during the talk are available at: [[media:Rank-Correlation-Examples.ana|Rank-Correlation-Examples.ana]] and [[media:Rank-Correlation-Analysis.ana|Rank-Correlation-Analysis.ana]].

=== Statistical Functions in Analytica 4.0 ===

Date and Time: Thursday, Aug 16, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

A statistical function is a function that process a data set containing many sample points, computing a "statistic" that summarizes the data. Simple examples are [[Mean]] and [[Variance]], but more complex examples may return matrices or tables. In this talk, I'll review statistical functions that are built into Analytica 4.0. In Analytica 4.0, all built-in statistical functions can now be applied to historical data sets over an arbitrary index, as well as to uncertain samples (the Run index), eliminating the need for separate function libraries. I will demonstrate this use, as well as several new statistical functions, e.g., [[Pdf]], [[Cdf]], [[Covariance]]. I will explain how the domain attribute should be utilized to indicate that numeric-valued data is discrete (such as integer counts, for example), and how various statistical functions (e.g., [[Frequency]], [[GetFract]], [[Pdf]], [[Cdf]], etc) make use of this information. In the process, I'll demonstrate numerous examples using these functions, such things as inferring sample covariance or correlation matricies from data, quickly histogramming arbitrary data and using the coordinate index setting to plot it, or using a weighted [[Frequency]] for rapid aggregation.

In addition, all statistical functions in Analytica 4.0 can compute weighted statistics, where each point is assigned a different weight. I'll cover the basics of sample weighting, and demonstrate some simple examples of using this for computing a Bayesian posterior and for importance sampling from an extreme distribution.

The Analytica model file that had resulted by the end of the presentation can be downloaded here:
[[media:User Group Webinar - Statistical Functions.ANA | User Group Webinar - Statistical Functions.ANA]].

=== The [[Large Sample Library: User Guide|Large Sample Library]] ===

'''Date and Time:''' Thursday, 18 Feb 2010 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The [[Large Sample Library: User Guide|Large Sample Library]] is an Analytica library that lets you run a Monte Carlo simulation for a large model or a large sample size that might otherwise exhaust computer memory, including virtual memory. It breaks up a large sample into a series of batch samples, each small enough to run in memory. For selected variables, known as the ''Large Sample Variables'' or ''LSVs'', it accumulates the batches into a large sample. You can then view the probability distributions for each LSV using the standard methods — confidence bands, PDF, CDF, etc. — with the full precision of the large sample.

Memory is saved by not storing results for non-LSVs.

This presentation introduces this library and how to use it.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2010-02-18-Large-Sample-Library.wmv Large-Sample-Library.wmv]. The Large Sample library can be downloaded for use in your own models from the [[Large Sample Library: User Guide]] page. The two example models used during this webinar were: [[media:Enterprise model3.ANA|Enterprise model3.ana]] and [[media:Simple example for Large Sample Library.ana|Simple example for Large Sample Library.ana]].

= Sensitivity Analysis Topics =

=== Tornado Charts ===

'''Time and Date:''' Thursday, 20 Mar 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract:'''

[[Image:Tornado plot.png]]

A tornado chart depicts the result of a '''local sensitivity analysis''', showing how much a computed result would change if each input were varied one input at a time, with all other inputs held to their baseline value. The result is usually plotted with horizontal bars, sorted with larger bars on top, resulting in a graph resembling the shape of a tornado, hence the name. There a numerous variations on tornado charts, resulting from different ways of varying the inputs, and in some cases, different metrics graphed.

This talk will walk through the steps of setting up a Tornado chart, and explore different variations of varying inputs. We'll also explore some more complex issues that can arise when some inputs are arrays.

The model used during this talk is here: [[media:Tornado Charts.ANA|Tornado Charts.ana]] (the stuff for the talk was in the Tornado Analysis module). You can watch a recording of this webinar from [http://AnalyticaOnline.com/WebinarArchive/2008-03-20-Tornado-Charts.wmv Tornado-Charts.wmv].

=== Advanced Tornado Charts -- when inputs are Array-Valued ===

'''Date and Time:''' Thursday, April 17, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The webinar of 20-Mar-2008 ([http://AnalyticaOnline.com/WebinarArchive/2008-03-20-Tornado-Charts.wmv Tornado-Charts.wmv], see webinar archives) went through the fundamentals of setting up a local sensitivity analysis and plotting the results in the form of a tornado chart. That webinar also discussed the many variations of tornado analyses (or more generally, local sensitivity analyses) that are possible.

This talk builds on those foundations by going a step further and addressing tornado analyses when some of the input variables are array-valued. The presence of array-valued inputs introduces many additional possible variations of analyses, as well as many modeling complications. For example, a local sensitivity analysis varies one input at a time, but that could mean you vary each input variable (as a whole) at a time, or it could mean that you vary each cell of each input array individually. Either is possible, each resulting in a different analysis. Some of these variations compute the correct result automatically through the magic of array abstraction, once you've set up the basic tornado analysis that we covered in the first talk, while other require quite a bit of additional modeling effort. However, even the ones that produce the correct result can often be made more efficient, particularly when the indexes of each input variable are different across input variables.

When we do opt to vary input arrays one cell at a time, the display of the results may be dramatically effected. Although we can keep the results in an array form, the customary tornado chart require us to ''flatten'' the multi-D arrays and label each bar on the chart with a cell coordinate.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-04-17-Tornados-With-Arrays.wmv Tornados-With-Arrays.wmv]. This webinar made use of the following models: [[media:Sales Effectiveness Model with tornado.ana|Sales Effectiveness Model with tornado.ana]], [[media:Biotech R&D Portfolio Tornado.ana|Biotech R&D Portfolio with Tornado.ana]], [[media:Sensitivity Analysis Library.ana|Sensitivity Analysis Library.ana]], and [[media:Sensitivity Functions Examples.ana|Sensitivity Functions Examples.ana]]. See [[The Sensitivity Analysis Library]] for more information on how to use [[media:Sensitivity Analysis Library.ana|Sensitivity Analysis Library.ana]] in your own models.

= Financial Analysis =

=== Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]]) ===

'''Date and Time:''' 18 Dec 2008, 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This is Part 3 of a multi-part webinar series where we have been covering the modeling and evaluation of cash flows over time in an interactive exercise-based webinar format, where concepts are introduced in the form of modeling exercises, and participants are asked to complete the exercises in Analytica during the webinar. Part 3 covers Internal Rate of Return ([[IRR]]) and Modified Internal Rate of Return ([[MIRR]]), and includes seven modeling exercises.

To speed the presentation up, I am providing the exercises in advance: [[media:NPV_and_IRR.ppt|NPV_and_IRR.ppt]]. I urge you to take a shot at completing them before the webinar begins, and we'll advance through the exercises more rapidly so as to complete the topic material within the hour. By attempting the exercises in advance, you'll have a good opportunity to compare your solutions to mine, and to ask questions about things you got stuck on.

A dollar received today is not worth the same as a dollar received next year. Taking this time-value of money (or more generally, time-value of utility) into account is very important when comparing cash flows over time that result from long-term capital budgeting decisions. Net Present Value (NPV) and Internal Rate of Return (IRR) are the two most commonly used metrics examining the effective value of an investment's cash flow over time. Both concepts are pervasive in decision-analytic models.

This webinar will be highly interactive. Fire up a instance of Analtyica as you join on. As I introduce each concept, I'll provide you with cash flow scenarios, and give you a chance to compute the result yourself using Analytica. This talk is intended for people who are not already well-versed in NPV and IRR, or for people who already have a good background with those concepts but are new to Analytica and thus can learn from the interactive practice of addressing these exercises during the talk.

See also the materials from Parts 1 and 2 (Net Present Value, 20 Nov 2008 and 4 Dec 2008) elsewhere on this page. This session begins with the model [[media:Cash Flow Metrics 2.ana|Cash Flow Metrics 2.ana]], and ends with [[media:Cash Flow Metrics 3.ana|Cash Flow Metrics 3.ana]]. You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-12-18-IRR.wmv IRR.wmv].

=== Bond Portfolio Analysis ===

'''Date and Time: ''' 11 Dec 2008, 10:00am Pacific Standard Time

'''Presenter:''' Rob Brown, Incite! Decision Technologies

'''Abstract'''

I demonstrate how to value a bond portfolio in which bonds are bought and sold on an uncertain frequency. The demonstration shows how Intelligent Arrays and related functions can greatly simplify calculations of multiple dimensions that would typically require multiple interconnected sheets in a spreadsheet or nested do-loops in a procedural language.

You can watch a recording of this webinar at
[http://AnalyticaOnline.com/WebinarArchive/2008-12-11-Bond-Portfolio-Analysis.wmv Bond-Portfolio-Analysis.wmv]. The model underlying the presentation is [[media:Bond Portfolio Valuation.ana|Bond Portfolio Valuation.ana]], and the power point slides are at [[media:Bond Portfolio Valuation.ppt|Bond Portfolio Valuation.ppt]].

=== Net Present Value ([[NPV]]) ===

'''Date and Time:''' Part I : Thursday, 20 Nov 2008, 10:00am Pacific Standard Time
::::Part II : Thursday, 4 Dec 2008, 10:00am Pacific Standard Time

(Parts 1 & 2 cover NPV -- part 3, listed now separately, covers IRR)

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

A dollar received today is not worth the same as a dollar received next year. Taking this time-value of money (or more generally, time-value of utility) into account is very important when comparing cash flows over time that result from long-term capital budgeting decisions. Net Present Value ([[NPV]]) and Internal Rate of Return ([[IRR]]) are the two most commonly used metrics examining the effective value of an investment's cash flow over time. Both concepts are pervasive in decision-analytic models.

This multi-part webinar provides an introduction to the concepts of present value, discount rate, [[NPV]] and [[IRR]]. We'll discuss the interpretation of ''discount rate'', and we'll get practice computing these metrics in Analytica. We'll examine the pitfalls of each metric, and we'll examine the interplay of each metric with explicitly modelled uncertainty (including the concepts of Expected NPV (ENPV) and Expected IRR (EIRR)).

This webinar will be highly interactive. Fire up a instance of Analtyica as you join on. As I introduce each concept, I'll provide you with cash flow scenarios, and give you a chance to compute the result yourself using Analytica. This talk is intended for people who are not already well-versed in [[NPV]] and [[IRR]], or for people who already have a good background with those concepts but are new to Analytica and thus can learn from the interactive practice of addressing these exercises during the talk.

I have assembled quite a bit of material, which I believe will fill two webinar sessions. Part 1 will focus mostly on present value, NPV, discount rate, and the use of NPV with uncertainty. Part 2 will focus mostly on IRR, several "gotchas" with IRR, and MIRR.

Materials:
* [[media:Cash Flow Metrics 2.ana|Cash Flow Metrics 2.ana]] : Model at end of second session
* [[media:Cash Flow Metrics 1.ANA|Cash Flow Metrics 1.ana]] : Model at end of first session
* [http://AnalyticaOnline.com/WebinarArchive/2008-11-20-NPV-and-IRR1.wmv NPV-and-IRR1.wmv] : Webinar recording of Part 1.
* [http://AnalyticaOnline.com/WebinarArchive/2008-12-04-NPV-and-IRR2.wmv NPV-and-IRR2.wmv] : Webinar recording of Part 2.

Note: Part 1 covered 5 exercises, covering present value, discount rate, modeling certain cash flows, computing NPV, and graphing the NPV curve. Part 2 added exercises 6-9, covering cash flows at non-uniformly-spaced time periods, valuating bonds and treasury notes, cash flows with uncertainty, and using the CAPM to find invester-implied corporate discount rate.

The "class" will continue with Part 3 beginning with Internal Rate of Return.

= Data Analysis Techiques =

=== Statistical Functions ===

'''Date and Time:''' Thursday, May 22, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

A statistical function is a function that processes a data set containing many sample points, computing a "statistic" that summarizes the data. Simple examples are [[Mean]] and [[Variance]], but more complex examples may return matrices or tables. In this talk, I'll review statistical functions that are built into Analytica 4.0. In Analytica 4.0, all built-in statistical functions can now be applied to historical data sets over an arbitrary index, as well as to uncertain samples (the Run index), eliminating the need for separate function libraries. I will demonstrate this use, as well as several new statistical functions, e.g., [[Pdf]], [[Cdf]], [[Covariance]]. I will explain how the domain attribute should be utilized to indicate that numeric-valued data is discrete (such as integer counts, for example), and how various statistical functions (e.g., [[Frequency]], [[GetFract]], [[Pdf]], [[Cdf]], etc) make use of this information. In the process, I'll demonstrate numerous examples using these functions, such things as inferring sample covariance or correlation matricies from data, quickly histogramming arbitrary data and using the coordinate index setting to plot it, or using a weighted [[Frequency]] for rapid aggregation.

In addition, all statistical functions in Analytica 4.0 can compute weighted statistics, where each point is assigned a different weight. I'll cover the basics of sample weighting, and demonstrate some simple examples of using this for computing a Bayesian posterior and for importance sampling from an extreme distribution.

This talk is appropriate for moderate to advanced users.

A recording of this webinar can be watched at
[http://AnalyticaOnline.com/WebinarArchive/2008-05-22-Statistical-Functions.wmv Statistical-Functions.wmv].
The model created during this webinar is at [[media:Statistical Functions.ANA|Statistical Functions.ana]].

=== Principal Components Analysis (PCA) ===

'''Date and Time:''' 15 Jan 2009, 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Principal component analysis (PCA) is a widely used data analysis technique for dimensionality reduction and identification of underlying common factors. This webinar will provide a gentle introduction to PCA and demonstrate how to compute principal components within Analytica. Intended to be at an introductory level, with no prior experience with PCA (or even knowledge of what it is) assumed.

The model developed during this talk, where the principal components were computed for 17 publically traded stocks based on the previous 2 years of price change data is [[media:Principal Component Analysis.ana|Principal Component Analysis.ana]]. A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2009-01-15-PCA.wmv PCA.wmv].

=== Variable Stiffness Cubic Splines ===

'''Date and Time:''' 2 October 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Brian Parsonnet, ICE Energy

'''Abstract'''

The Variable Stiffness Cubic Spline is a highly robust data smoothing and interpolation technique. A stiffness parameter adjusts the variability of the curve. At the extreme of minimal stiffness, the curve approaches a cubic spline (like [[CubicInterp]]) that passes through all data points, while at the other extreme of maximal stiffness, the spline curve becomes the best-fit line. Weight parameters can be used to constrain the curve to include selected points, while smoothing over others. The first, second and third derivatives all exist and are readily available.

I'll introduce and demonstrate [[User-Defined Functions]] that compute the variable stiffness cubic spline and interpolate to new points. I'll also show how these curves can be used to detect or eliminate anomalies in data.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-10-02-Variable-Stiffness-Cubic-Splines.wmv Variable-Stiffness-Cubic-Splines.wmv]. The model and library with the vscs functions will be posted here within a few weeks.

=== Using [[Regression]] ===

'''Date and Time:''' Thursday, May 1, 2008 at 10:00 - 11:00 Pacific Daylight Time

'''Date and Time:''' Thursday, Aug 30, 2007 at 10:00 - 11:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Regression analysis is a statistical technique for curve fitting, discovering relationships in data, and testing hypotheses between variables. In this webinar, I will focus on generalized linear regression, which is provided by Analytica's [[Regression]] function, and examine many ways in which is can be used, including fitting simple lines to data, polynomial regression, use of other non-linear terms, and fitting of autoregressive models (e.g., ARMA). I'll examine how we can assess how likely it is the data might have been generated from the particular form of the regression model used. We can also determine the level of uncertainty in our inferred parameter values, and incorporate these uncertainties into a model that uses the result of the regression. The talk will cover Analytica 4.0 functions [[Regression]], [[RegressionDist]], [[RegressionFitProb]], and [[RegressionNoise]].

You can watch a recording of the 1 May 2008 webinar here: [http://AnalyticaOnline.com/WebinarArchive/2008-05-01-Regression.wmv Regression.wmv] (or [http://youtu.be/HV5ll2Bhf18 on You Tube]). The model developed during that webinar is here: [[media:Using_Regression.ANA|Using Regression.ana]]

=== Logistic Regression ===

'''Date and Time:''' Thursday, 5 June 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

''' Abstract'''

''(Features covered in this webinar require Analytica Optimizer)''

Logistic regression is a technique for fitting a model to historical data to predict the probability of an event from a set of independent variables. In this talk, I'll introduce the concept of Logistic regression, explain how it differs from standard [[Regression|linear regression]], and demonstrate how to fit a logistic regression model to data in Analytica. Probit regression is for all practical purposes the same idea as Logistic regression, differing only in the specific functional form for the model. Poisson regression is also similar except is appropriate when predicting a probability distribution over a dependent variable that represents integer "counts". All are examples of generalized linear models, and after reviewing these forms of logistic regression, it should be clear how other generalized linear model forms can be handled within Analytica.

This topic is appropriate for advanced modelers. I will assume familiarity with [[Regression|regression]] (see the earlier talk on the topic), but will not assume a previous knowledge of logistic regression.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2008-06-05-Logistic-Regression.wmv Logistic-Regression.wmv]. The model developed during this webinar can be downloaded from [[media:Logistic_regression_example.ana|Logistic_regression_example.ana]]. You'll also need the file [[media:BreastCancer.data|BreastCancer.data]].

= Bayesian Techniques =

=== Bayesian Posteriors using Importance Sampling ===

'''Date and Time:''' Thursday, September 4, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Several algorithms for computing Bayesian posterior probabilities are special cases of ''importance sampling''. The [[#Importance_Sampling_.28Rare_events.29|webinar of the previous week, Importance Sampling (rare events)]] introduced importance sampling, covered the theory behind it, how it is applied, and how Analytica's sample weighting feature can be use for importance sampling. This webinar continues with importance sampling, this time exploring how it can be used (at least in some cases) to compute Bayesian posterior probabilities.

I'll provide an introduction to what Bayesian posterior probabilities are, describe a couple importance sampling-based approaches to computing them, and implement a few examples in Analytica. Importance sampling techniques for computing posteriors have limited applicability -- in some cases they work well, other not. I'll try to characterize what those conditions are.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-09-04-Posteriors_using_IS.wmv Posteriors_using_IS.wmv]. About two-thirds through the presentation, we noticed a result that seemed to be coming out incorrectly. I explain what the problem was and fix it in [http://AnalyticaOnline.com/WebinarArchive/2008-09-04-Posteriors_using_IS_addendum.wmv Posteriors_using_IS_addendum.wmv]. The models used during this presentation can be downloaded from [[media:Posterior sprinklers.ana|Posterior sprinklers.ana]] and [[media:Likelihood weighting.ana|Likelihood weighting.ana]].

=== Importance Sampling (Rare events) ===

'''Date and Time:''' Thursday, 28 Aug 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Importance sampling is a technique that simulates a target probability distribution of interest by sampling from a different sampling distribution and then re-weighting the sampled points so that computed statistics match those of the target distribution. The technique has has applicability when the target distribution is difficult to sample from directly, but where the probability density function is readily available. The technique produces valid results in the large sample size limit for any selection of sampling distribution (provided it is absolutely continuous with respect to the target distribution), but best results (i.e., fastest convergence with smaller sample size) are obtained when a good sampling distribution is used. The technique is commonly used for rare-event sampling, where you want to ensure greater sampling coverage in the tails of distributions, where few samples would occur with standard Monte Carlo sampling. During the talk, we develop a rare event model. It also has applicability to the computation of Bayesian posteriors, and sampling of complex distribution.

In this talk we cover the theory behind importance sampling and introduce the [[SampleWeighting|sample weighting]] mechanism that is built into Analytica. We develop a rare-event model to demonstrate how the weighting mechanism is used to achieve the importance sampling. Next week we'll continue with an example of computing a Bayesian posterior probability.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-08-28-Importance-Sampling.wmv Importance-Sampling.wmv]. The model developed during this talk can be downloaded from: [[media:Importance sampling rare events.ANA|Importance Sampling rare events.ana]].

= Presenting Models to Others =

=== The Analytica Cloud Player Style Library ===

'''Date and Time:''' Tuesday, 31 Jan 2012 10:00 am Pacific Standard Time

'''Presenter:''' Max Henrion or Fred Brunton (TBD), Lumina Decision Systems

'''Abstract'''

How to use the ACP Style Library and custom ACP-based web applications. Good practices for designing Analytica-model applications for the web.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2012-01-31-ACP-Style-Library.wmv ACP-Style-Library.wmv].

=== Intro to Analytica Cloud Player ===

'''Date and Time:''' Thursday, 26 Jan 2012 10:00am Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This talk provides an introduction to the [[Analytica Cloud Player]] (ACP). We'll browse several example models on the web, demonstrating various capabilities and illustrating what a user of models needs to know. You'll see how to set up an ACP account, and we'll cover free usage of ACP with active support, the details of individual and group plans, session credits and pricing. Finally, you'll see how to publish (upload) models to the cloud. This talk will not cover how to tailor a model for the web with specific cloud-player style settings or the ACP style library -- those will be covered the following week.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2012-01-26-Intro-ACP.wmv Intro-ACP.wmv]. You can also view the [[media:Intro-ACP.pptx|Power Point Slides]] from the talk (The power point slides were a very small part of the webinar).

=== Guidelines for Model Transparency ===

'''Date and Time:''' 19 Feb 2009, 10:00am Pacific Standard Time

'''Presenter:''' Max Henrion, Lumina Decision Systems

'''Abstract'''

What makes Analytica models easy for others to use and understand? I will review some example models that illustrate ways to improve transparency -- or opacity. Feel free to send me your candidates ahead of time!
We'll review some proposed guidelines. I hope to stimulate a discussion about what you think works well or not, and enlist your help in refining these guidelines.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2009-02-19-Transparency-Guidelines.wmv Transparency-Guidelines.wmv].

=== Creating Control Panels ===

'''Date and Time:''' Thursday, May 29, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

It is quite easy to put together "control panels" or "forms" for your Analytica models by creating input and output nodes for the inputs and outputs of interest to your model end users. This webinar will cover the basic steps involved in creating and arranging these forms, along with some tricks for making the process efficient. We'll cover the different types of input and output controls that are currently available, the use of text nodes to create visual groupings, use of images and icons, and the alignment commands that make the process very rapid. We'll learn how to change colors, and look at the use of buttons very briefly. This talk is appropriate for beginning Analytica users.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-05-29-Control-Panels.wmv Control-Panels.wmv] (required Windows Media Player). The model used during this webinar is at [[media:Building Control Panels.ana|Building Control Panels.ana]].

=== Sneak preview of Analytica Web Publisher ===

'''Date and Time:''' Thursday, February 21, 2008, 10:00 - 11:00 Pacific Standard Time

'''Presenter:''' Max Henrion, Lumina Decision Systems

'''Abstract'''

In this week's webinar, Max Henrion, Lumina's CEO, will provide a sneak preview of the Analytica Web Publisher. AWP offers a way to make Analytica models easily accessible to anyone with a web browser. Users can open a model, view diagrams and objects, change input variables, and view results as tables and graphs. Users will also be able to save changed models, to revisit them in later sessions. Model builders can upload models into AWP directly from their desktop. Usually, AWP directories are password protected, so only authorized users can view and use models. But, we also plan to make a free AWP directory available for people who want to share their models openly.

AWP is nearing release for alpha testing. We will welcome your comments and hearing how you might envisage using AWP.

''This webinar was not recorded.''

= Application Integration Topics =

=== OLE Linking ===

'''Time and Date:''' Thursday, 27 Mar 2008 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract:'''

OLE linking is a commonly used methods for linking data from Excel spreadsheets into Analytica and results from Analytica into Excel spreadsheets. It can be used with other applications that support OLE-linking as well. The basic usage of OLE linking is pretty simple -- it is a lot like copy and paste. This webinar covers basics of using OLE linking of fixed-sized 1-D or 2-D tables. I also demonstrate the basic tricks you must go through to link index values and multi-D inputs and outputs. In addition, we discuss what some of those OLE-link settings actually do, and explain how OLE-connected applications connect to their data sources.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-03-27-OLE-Linking.wmv 2008-03-27-OLE-Linking.wmv].

Note: Another 10 minute fast-paced video (separate from the webinar) demonstrates linking data from Analytica into Excel, computing something from that data, and linking the result back into Analytica: [http://AnalyticaOnline.com/WebinarArchive/OLE-to-Excel-and-back.wmv OLE-to-Excel-and-back.wmv].

=== Querying an OLAP server ===

'''Date and Time:''' Thursday, February 14, 2008, 10:00 - 11:00 Pacific Standard Time 
(''Note: Schedule change from an earlier posting. This is now back to the usual Thursday time. '')

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

In this session, I'll show how the [[MdxQuery]] function can be used to extract multi-dimensional arrays from an On-Line Analytical Processing (OLAP) server. In particular, during this talk we'll query Microsoft Analysis Services using MDX. In this talk, I'll introduce some basics regarding OLAP and Analysis Services, discuss the differences between multi-dimensional arrays in OLAP and Analytica, cover the basics of the MDX query language, show how to form a connection string for [[MdxQuery]], and import data. I'll also show how hierarchical dimensions can be handled once you get your data to Analytica.

''Note: Use of the features demonstrated in this webinar require the Analytica Enterprise or Optimizer edition, or the Analytica Power Player. They are also available in ADE.''

The model created during this webinar is available here: [[media:Using MdxQuery.ana|Using MdxQuery.ana]]. You can watch a recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2008-02-14-MdxQuery.wmv MdxQuery.wmv] (requires Microsoft Media Player)

=== Querying an ODBC relational database ===

Date and Time: Thursday, February 7, 2008, 10:00 - 11:00 Pacific Standard Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this talk I'll review the basics of querying an external relational ODBC database using [[DbQuery]]. This provides a flexible way to bring in data from SQL Server, Access, Oracle, and mySQL databases, and can also be used to read CSV-text databases and even Excel. In this talk, I will cover the topics of how to configure and specify the data source, the rudimentary basics of using SQL, the use of Analytica's [[DbQuery]], [[DbWrite]], [[DbLabels]] and [[DbTable]] functions.

''Note: Use of the features demonstrated in this webinar require the Analytica Enterprise or Optimizer edition, or the Analytica Power Player. They are available in ADE."

You can grab the model created during this webinar from here: [[media:Querying an ODBC relational database.ANA|Querying an ODBC relational database.ana]]. A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-02-07-Using-ODBC-Queries.wmv Using-ODBC-Queries.wmv].

=== Calling External Applications ===

Date and Time: Thursday, Oct 18, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

The [[RunConsoleProcess]] function runs an external program, can exchange data with that program, and can be used to perform a computation or acquire data outside of Analytica, that then can be used within the model. I'll demonstrate how this can be used with a handful of programs, and code written in several programming and scripting languages. I'll demonstrate a user-defined function that retrieves historical stock data from a web site.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-10-18-Calling-External-Applications.wmv Calling-External-Applications.wmv] (Requires Windows Media Player)

Files created or used during this webinar can be downloaded:
* [[media:Regular Expression Matching.ANA|Regular Expression Matching.ana]]
* [[media:RegExp.vbs|RegExp.vbs]]
* [[media:Read Historical Stock Data.ana|Read Historical Stock Data.ana]]
* For plotting to gnuplot, these gnuplot command files were used. (Note: You may have to adjust some file paths within these files, and within the model): [[media:Gnuplot-candlesticks.dat|Gnuplot-candlesticks.dat]], [[media:Gnuplot-3dsurface.dat|Gnuplot-3dsurface.dat]]
* [[media:ReadURL.exe|ReadURL.exe]] (for C++/CLR source code, see [[Retrieving Content From the Web]])

The example of retrieving stock data from Yahoo Finance is also detailed in an article here: [[Retrieving Content From the Web]]

=== New Functions for Reading Directly from an Excel File ===

'''Date and Time:''' Thursday, 24 April 2008 10:00 Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

'''(Feature covered requires Analytica Enterprise or better)'''

Hidden within the new release of Analytica 4.1 are three new functions for reading values directly from Excel spreadsheets: [[OpenExcelFile]], [[WorksheetCell]], [[WorksheetRange]]. These provide an alternative to OLE linking and ODBC for reading data from spreadsheets, which may be more convenient, flexible and reliable in many situations. We have not yet exposed these functions on the Definitions menu or in the Users Guide in release 4.1, since they are still in an experimental stage. I would like know that they have been "beta-tested" in a variety of scenarios before we fully expose them (also, the symmetric functions for writing don't exist yet). In this webinar, I will introduce and demonstrate these functions, after which you can start using them with your own problems.

The model created during this talk is here: [[Media:Functions for Reading Excel Worksheets.ana|Image:Functions for Reading Excel Worksheets.ana]]. It read from the example that comes with Office 2003, to which we added a few range names during the talk, resulting in [[media:SOLVSAMP.XLS|SolvSamp.xls]]. Place the excel file in the same directory as the model. A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-04-24-Reading-From-Excel.wmv Reading-From-Excel.wmv].

=== Reading Data from URLs to a Model ===

'''Date and Time:''' Thursday, 27 Aug 2009, 10:00am-11:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

''Requires Analytica Enterprise''

The new built-in function, [[ReadFromUrl]], can be used to read data (and images) from websites, such as HTTP web pages, FTP pages, or even web services like SOAP. In this webinar, I'll demonstrate the use of this function in several ways, including reading live stock and stock option price data, posting data to a web form, retrieving a text file from an FTP site, supplying user and password credentials for a web site or ftp service, downloading and displaying images including customized map and terrain images, and querying a SOAP web service.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2009-08-27-ReadFromUrl.wmv ReadFromUrl.wmv]. The model with the examples shown during the webinar is at [[media:Reading_Data_From_the_Web.ana|Reading_Data_From_the_Web.ana]].

=== Using the Analytica Decision Engine (ADE) from ASP.NET ===

'''Date and Time:''' Thursday, April 10, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Fred Brunton, Lumina Decision Systems

The Analytica Decision Engine (ADE) allows you to utilize a model developed in Analytica as a computational back-end engine from a custom application. In this webinar, we'll create a simple active web server application using ASP.NET that sends inputs submitted by a user to ADE, and displays results computed by ADE on a custom web page. In doing this, you will get a flavor how ADE works and how you program with it. If you've never created an active server page, you may enjoy seeing how that is done as well. This introductory session is oriented more towards people who do not have experience using ADE, so that you can learn a bit more about what ADE is and where it is appropriate by way of example.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2008-04-10-ADE-from-ASPNET.wmv ASP-from-ASPNET.wmv]. To download the program files that were created during this webinar [[Media:WebSite6.rar| Click here]].

= Optimization =

=== [[Analytica_4.3#Structured_Optimization|Introduction to Structured Optimization]] ===

'''Date and Time:''' Thursday, Febrary 24, 2011 at 10:00am PST (1:00pm EST, 6:00pm GMT)

'''Presenter''': Paul Sanford, Lumina Decision Systems

'''Abstract'''

[[Analytica 4.3]] is now available for beta testing and will be released in early March. The new version includes expanded optimization capabilities and simplified workflow for encoding optimization problems. The new '''Structured Optimization''' framework in 4.3 is centered around a new function, [[DefineOptimization]](), which replaces all three of the previous type-specific functions: LPDefine(), QPDefine() and NLPDefine(). It also introduces a new node type, '''Constraint''', which allows you to specify constraints using ordinary expressions. Paul will build up some basic examples using Structured Optimization and field questions from users.

A recording of this webinar can be viewed at: [http://AnalyticaOnline.com/WebinarArchive/2011-2-24-Structured-Optimization.wmv Structured-Optimization.wmv]. The example models used during this webinar are: [[Media:Beer Distribution LP1.ana|Beer Distribution LP1.ana]],[[Media:Beer Distribution LP2.ana|Beer Distribution LP2.ana]], [[File:Plane Allocation LP.ana|Plane Allocation LP.ana]],[[File:polynomial NLP.ana|polynomial NLP.ana]]

=== Interactive Optimization Workshop ===

'''Date and Time:''' Thursday, 24 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Paul Sanford, Lumina Decision Systems

'''Abstract'''

This is an interactive workshop where you will learn the basics of creating Structured Optimization models and challenge yourself to set up and solve some basic examples on your own! No prior training in optimization is required. [http://www.lumina.com/products/analytica-optimizer/optimizer-trial/ Trial Downloads of Analytica Optimizer] are now available. Attendees are encouraged to have Analytica Optimizer 4.3 installed and running during the workshop.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2011-03-24-Optimization-Workshop.wmv Optimization Workshop.wmv]. You can download the models from the talk: [[media:Optimal_Box.ana|Optimal Box.ana]] and [[media:Call_center.ana|call_center.ana]].

=== Optimizing Parameters in a Complex Model to Match Historical Data ===

'''Date and Time:''' Thursday, 31 March 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Almost all quantitative models have parameters that must be assessed by experts or estimated from historical data. Estimation from historical data can be complicated by the presence of variables that are either unobservable or unavailable in the historical record. Maximum likelihood estimation addresses this by finding the parameter settings that maximize the likelihood of the historical data predicted by the model. In this talk, I will formulate the parameter fitting task as a structured optimization problem (NLP), providing a hands-on demonstration of the new structured optimization features in Analytica 4.3.

A webinar recording of this can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2011-03-31-Parameter-Optimization.wmv Parameter-Optimization.wmv]. The model file developed during the webinar is [[media:Parameter_Optimization.ana|Parameter_Optimization.ana]]. The webinar also mentioned the [[Arbitrage Theorem]].

=== Optimization with Uncertainty ===

'''Date and Time:''' Thursday, 14 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D., Lumina Decision Systems

'''Abstract'''

Analytica analyzes uncertainty by conducting a Monte Carlo analysis. When you optimize decision variables in a model containing uncertainty, you have a choice: You can perform one optimization over the Monte Carlo analysis, or you can perform a Monte Carlo sampling of optimizations (i.e., the Monte Carlo is inside the optimization, or the optimization is inside the Monte Carlo). The first case is used when the decision must be taken while the quantities are still uncertain. The second case is used when the values of the uncertain quantities will be resolved before the decisions are taken.

To illustrate, consider the situation faced by a relief organization that provides aid to victims of natural disasters. In one situation, a decision must be made regarding how to allocate resources among several currently occuring famines. At the time the decision must be made, the actual intensity, progress and aid effectiveness for each famine is uncertain. In a different situation, the organization wants to characterize the uncertainty in its need for resources for the upcoming year, perhaps forecasting the damage from next year's famines, and using these forecasts in its budgeting and planning decisions.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2011-04-14-Optimization-w-Uncertainty.wmv Optimization-w-Uncertainty.wmv]. The example model developed during the webinar can be downloaded from [[media:Famine Relief.ana|Famine Relief]]. You can also download the [[media:Optimization with Uncertainty.ppt|PowerPoint slides]] from the talk.

=== Neural Networks ===

'''Date and Time:''' Thursday, 21 April 2011, 10:00am Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Ph.D.

'''Abstract'''

A feed-forward artificial neural networks is a non-linear function that predicts one or more outputs from a set of inputs. These are usually used in two layers, where the first layer of inputs are weighted and summed, and then passed through a sigmoid function to determine the activations of a hidden layer, those those activations are weighted, summed and then passed through a sigmoid function to predict the final output. A training phase is used to adjust the weight to "fit" an example data set.

In this webinar, I'll create a nearal network model in Analytica and train it on example data as a demonstration of the use of structured optimization. It provides a simple and easily understood example of the use of intrinsic indexes in a structured optimization model, while at the same time introducing the basics of the interesting topic if neural networks.

You can watch a recording of this webinar at [http://AnalyticaOnline.com/WebinarArchive/2011-04-21-Neural-Networks.wmv Neural-Networks.wmv]. The neural network model created during the webinar (requires Analytica Optimizer) is [[media:Neural-Network.ana|Neural-Network.ana]].

=== Introduction to Linear and Quadratic Programming ===

Date and Time: Thursday, Oct 11, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

This talk is an introduction to linear programming and quadratic programming, and an introduction to solving LPs and QPs from inside an Analytica model (via Analytica Optimizer). LPs and QPs can be efficiently encoded using the Analytica Optimizer functions [[LpDefine]] and [[QpDefine]]. I'll introduce what a linear program is for the sake of those who are not already familiar, and examine some example problems that fit into this formalism. We'll encode a few in Analytica and compute optimal solutions. Although LPs and QPs are special cases of non-linear programs (NLPs), they are much more efficient and reliable to solve, avoid many of the complications present in non-linear optimization, and fully array abstract. Many problems that initially appear to be non-linear can often be reformulated as an LP or QP. We'll also see how to compute secondary solutions such as dual values (slack variables and reduced prices) and coefficient sensitivies. Finally, [[LpFindIIS]] can be useful for debugging an LP to isolate why there are no feasible solutions.

You can watch a recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-10-11-LP-QP-Optimization.wmv LP-QP-Optimization.wmv] (requires Windows Media Player)

The model file created during this webinar is here: [[media:LP QP User Group.ANA|LP QP Optimization.ana]]

=== Non-Linear Optimization ===

Date and Time: Thursday, Oct 4, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

This talk focuses on the problem of maximizing or minimizing an objective criteria in the presence of contraints. This problem is referred to as a non-linear program, and the capability to solve problems of this form is provided by the Analytica Optimizer via the [[NlpDefine]] function. In this talk, I'll introduce the use of [[NlpDefine]] for those who have not previously used this function, and demonstrate how NLPs are structured within Analytica models. I'll examine various challenges inherent in non-linear optimization, tricks for diagnosing these and some ways to address these. We'll also examine various ways in which to structure models for parametric analyses (e.g., array abstraction over optimization problems), and optimizations in the presence of uncertainty.

You can watch a recording of this session here: [http://AnalyticaOnline.com/WebinarArchive/2007-10-04-Nonlinear-Optimization.wmv Nonlinear-Optimization.wmv]

During the talk, these two models were created:
* [[media:Simple Nonlinear optimization.ANA|Simple Nonlinear Optimization.ana]]
* [[media:Nonlinear asset allocation.ANA|Nonlinear asset allocation.ana]]

= Vertical Applications and Case Studies =

=== Regional Weather Data Analysis ===

'''Date and Time:''' Thursday, 22 April 2010 10:00am Pacific Daylight Time

'''Presenter:''' Brian Parsonnet, Ice Energy

'''Abstract'''

There are numerous sources of weather data on the web. Users of this data face a few common problems: how to gather the data in volume, how to normalize the data regardless of source, and how to analyze the results to generate insight. Analytica is the perfect tool to address all three issues simply and efficiently. A sample model will be shown illustrating some of techniques.

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2010-04-22-Regional-Weather-Analysis.wmv Regional-Weather-Analysis.wmv]. The model shown during the talk is [[media:Weather analysis.ana|Weather analysis.ana]], and the data file used by this model for Burbank weather can be downloaded from [[media:Burbank.zip|Burbank.zip]] (remember to Unzip it first to Burbank.txt). To avoid issues with ownership of the data, the temperatures in this file have been randomized (so the data is not accurate) and other fields zeroed out, but this will still allow you to play with the model and data.

=== Automated Monitoring and Failure Detection ===

'''Date and Time:''' 5 Feb 2009, 10:00am Pacific Standard Time

'''Presenter:''' Brian Parsonnet, ICE Energy

'''Abstract'''

In many complex physical systems, the automatic and proactive detection of system failures can be highly beneficial. Often dozens of sensor readings are collected over time, and a computer analyzes these to detect when system behavior is deviating from normal. Sounding an alert can then facilitate early intervention, perhaps catching a component that is just starting to go bad.

In a complex physical system with multiple operating modes and placed in a changing environment, anomaly detection is a very difficult problem. Simple sensor thresholds (and other related approaches) lack context-dependence, often making these simple approaches insufficient for the task. What is normal for any given sensor depends on the system's operating mode, time of day, activities in progress, and environmental factors. Simple thresholds that don't take such context into account either end up being so loose that they miss legitimate anomalies, or so tight that too many excess alarms are generated during normal conditions.

In this webinar, I'll show an expert system I've developed in Analytica that detects anomalies and developing failures in our deployed cooling system products. Data from dozens of sensors is collected in 5 minute intervals and the system transitions through multiple operating modes, daily and seasonal environmental fluctuations, and system demands. The Analytica model provides a framework in which complex rules that take multiple factors into account can be expressed, and used to estimate acceptable upper and lower operating ranges that are dynamically adjusted across each moment in time, taking into account whatever context is available. The Analytica environment presents a very readable and understandable language for expressing monitoring rules, and the overall transparency enables us to spot where other rules are needed and what they need to be.

[[Image:AutoMonitoring.png|frame|none|Graph illustrates how upper and lower bounds on operating range is adjusted to context. Actual sensor data is green, the red and blue lines show the computed bounds on acceptable operating range at each point in time.]]

A recording of this webinar can be watched at [http://AnalyticaOnline.com/WebinarArchive/2009-02-05-Failure-detection.wmv Failure-Detection.wmv].

=== Data Center Capacity Planning ===

''Please note that this presentation will be on Wednesday rather than Thursday this week.''

'''Date and Time:''' Wednesday, October 21, 2008 10:00am Pacific Daylight Time

'''Presenter:''' Max Henrion, Lumina Decision Systems

'''Abstract'''

Data center energy demands are on the rise, creating serious financial as well as infrastructural challenges for data center operators. In 2006, data centers were responsible for a costly 1.5 percent of total U.S. electricity consumption, and national energy consumption by data centers is expected to nearly double by 2011. For data center operators, this means that many data centers are reaching the limits of power capacity for which they were originally designed. In fact, Gartner predicts that 50 percent of data centers will discover they have insufficient power and cooling capacity in 2008.

This week's presentation will provide an overview of [http://www.lumina.com/products/capacity-planning-tools/data-center-capacity-planning-tool/ ADCAPT -- the Analytica Data Center Capacity Planning Tool]. For this webinar, the User Group will be joining a presentation that is also being given outside of the Analytica User Group, but I (Lonnie) think is also of interest to the User Group community in that it shows of an example of a re-usable Analytica model, containing several very interesting and novel techniques, applied to a very interesting application area.

Due to technical difficulties, this webinar was not recorded.

=== Modeling the Precision Strike Process ===

'''Date and Time:''' Thursday, October 16, 2008, 10:00am Pacific Daylight Time

'''Presenter:''' Henry Neimeier, MITRE

'''Abstract'''

We describe a new paradigm for modeling, and apply it to a simple view of the precision strike attack process against mobile targets. The new modeling paradigm employs analytic approximation techniques that allow rapid model development and execution. These also provide a simple dynamic analytic risk evaluation capability for the first time. The beta distribution is used to summarize a broad range of target dwell and execution time scenarios in compact form. The data processing and command and control processes are modeled as analytic queues.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2008-10-16-Precision-Strike-Process.wmv Precision-Strike-Process.wmv]. Several related papers and materials are also available, including:
* [[media:MILCOM96.pdf|A New Paradigm For Modeling The Precision Strike Process (U)]] by H. Neimeier from MILCOM96
* [[media:Milcom96.ana|Milcom96.ana]] -- the model from the talk and above paper.
* [[media:PhalanxMar.pdf|Analytic Uncertainty Modeling Versus Discrete Event Simulation]] by H. Neimeier, PHALANX March 1996.
* [[media:AnalQueNet.pdf|Analytica Queuing Networks]] by H. Neimeier, Proc. 12th Int'l Conf. Systems Dynamics Soc. 1994.
* [[media:Kuskey CAPE MTR-11.pdf|The Architecture of CAPE Models]] by K.P. Kuskey and S.K. Parker, MITRE Tech. Report.
* [[media:Cmac2pap_2_.pdf|Analytical Modeling in Support of C4ISR Mission Assessment (CMA)]] by F.R. Richards, H.A. Neimeier, W.L. Hamm, and D.L. Alexander, 3rd Int'l Symp. on Command and Control Research and Technology, 1997.
* [[media:INCOSE96.pdf|Analyzing Processes with HANQ]] by H. Neimeier and C. McGowan, MITRE, from INCOSE96.
* Functions for drawing ''radar plots'': [[media:Radarplt.ANA|Radarplt.ana]]
* Power point slides: [[media:Cape.ppt|Cape.ppt]], [[media:PGMrisk.ppt|PGMrisk.ppt]], and [[media:JDEMweb.ppt|JDEMweb.ppt]]

=== Modeling Utility Tariffs in Analytica ===

Presenter: Brian Parsonnet, Ice Energy

Date and Time: Thursday, Nov 8, 2007 at 10:00 - 11:00am Pacific Standard Time

Modeling utility tariffs is a tedious and complicated task. There is no standard approach to how a utility tariff is constructed, and there are 1000’s of tariffs in the U.S. alone. Ice Energy has made numerous passes at finding a “simple” approach to enable tariff vs. product analysis, including writing VB applications, involved Excel spreadsheets, using 3rd party tools, or outsourcing projects to consultants. The difficulty stems from the fact that there is little common structure to tariffs, and efforts to standardize on what structure does exist is confounded by an endless list of exceptions. But using the relatively simple features of Analytica we have created a truly generic model that allows a tariff to be defined and integrated in just a few minutes. The technique is not fancy by Analytica standards, so this in essence demonstrates how Analytic’s novel modeling concept can tackle tough problems.

You can watch a recording of this webinar at: [http://AnalyticaOnline.com/WebinarArchive/2007-11-08-Tariff-Modeling.wmv 2007-11-08-Tariff-Modeling] (Requires Windows Media Player)

=== Modeling Energy Efficiency in Large Data Centers ===

Date and TimeThursday, Oct 25, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter:Surya Swamy, Lumina Decision Systems

Abstract

The U.S. data center industry is witnessing a tremendous growth period stimulated by increasing demand for data processing and storage. This has resulted in a number of important implications including increased energy costs for business and government, increased emissions from electricity generation, increased strain on the power grid and rising capital costs for data center capacity expansion. In this webinar, Analytica's dynamic modeling capabilities coupled with it's advanced uncertainty capabilities, which offer tremendous support in building cost models for planning and development of energy efficient data centers, will be illustrated. The model enables users to explore future technologies, the performance, costs and efficiencies of which are uncertain and hence to be probabilistically evaluated over time.

You can watch a recording of this presentation at: [http://AnalyticaOnline.com/WebinarArchive/2007-10-25-Data-Center-Model.wmv Data-Center-Model.wmv] (Requires Windows Media Player)

= Graphing =

=== Creating Scatter Plots ===

'''Date and Time:''' Thursday, May 15, 2008 at 10:00 - 11:00am Pacific Daylight
Time

'''Date and Time:''' Thursday, Aug 23, 2007 at 10:00 - 11:00am Pacific Daylight
Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

This webinar focuses on utilizing graphing functionality new to Analytica 4.0, and specifically, functionality enabling the creative use of scatter plots. The talk will focus primarily on techniques for simultaneously displaying many quantities on a single 2-D graph. I'll discuss several methods in which multiple data sources (i.e., variable results) can be brought together for display in a single graph, including the use of result comparison, comparison indexes, and external variables. I'll describe the basic new graphing-role / filler-dimension structure for advanced graphing in Analytica 4.0, enabling multiple dimensions to be displayed on the horizontal and vertical axes, or as symbol shape, color, or symbol size, and how all these can be rapidly pivoted to quickly explore the underlying data. I'll discuss how graph settings adapt to changes in pivot or result view (such as Mean, Pdf, Sample views).

A recording of this webinar can be viewed at [http://AnalyticaOnline.com/WebinarArchive/2008-05-15-Scatter-Plots.wmv Scatter-Plots.wmv].

Model used: During this webinar, I started with some example data in the model [[media:Chemical elements orig.ANA|Chemical elements.ana]]. The original file is in the form before graph settings were changed. By the end of the webinar, many graph settings had been altered, and various changes made, resulting in [[media:UG_Webinar_Scatter_Plots.ana|Scatter-Plots.ana]] (during the Aug 23 presentation, this was the final model: [[media:Chemical elements2.ANA|Chemical elements2.ana]]).

=== Graph Style Templates ===

''' Date and Time:''' Thursday, February 28, 2008, 10:00 - 11:00 Pacific Standard Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

Graph style templates provide a convenient and versitile way to bundle graph setup options so that they can be reused when viewing other result graphs. For example, if you've discovered a set of colors and fonts and a layout that creates the perfect pizzazz for your results, you can bundle that into a template where you can quickly select it for any graph. In this talk, I'll introduce how templates can be used and how you can create and re-use your own. I'll show the basics of using existing templates, previewing what templates will look like, and applying a given template to a single result or to your entire model. We'll also see how to create your own templates, and in the process I'll discuss what settings can be controlled from within a template. I'll discuss how graph setup options are a combination of global settings, template settings, and graph-specific overrides. I'll show how to place templates into libraries (thus allowing you to have template libraries that can be readily re-used in different models), and even show how to control a few settings using templates that aren't selectable from the Graph Setup UI. I'll also touch on how different graph setting are associated with different aspects of a graph, ultimately determining how the graph adapts to changes in uncertainty view or pivots.

The model created during this webinar is here: [[media:Graph style templates.ana|Graph style templates.ana]].
You can watch a recording of the webinar here: [http://AnalyticaOnline.com/WebinarArchive/2008-02-28-Graph-Style-Templates.wmv Graph-Style-Templates.wmv].

= Scripting =

=== Button Scripting ===

Date and Time: Thursday, Sept. 6, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Max Henrion, Lumina Decision Systems

Abstract

This webinar is an introduction to Analytica's typescript and button scripting. Unlike variable definitions, button scripts can have side-effects, and this can be useful in many circumstances. I'll cover the syntax of typescript (and button scripts), and how scripts can be used from buttons, picture nodes or choice inputs. I'll introduce some of the Analytica scripting language to those who may have seen or used it before. And we'll examine some ways in which button scripting can be used.

You can watch the recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-06-Button-Scripting.wmv Button Scripting.wmv] (Requires Windows Media Player or equiv). The model files and libraries used during the webinar are in [[media:Ana_tech_webinar_on_scripting.zip|Ana_tech_webinar_on_scripting.zip]].

= Analytica User Community =

=== The Analytica Wiki, and How to Contribute ===

'''Date and Time:''' (tentative) Thursday, October 30, 2008, Pacific Daylight Time

'''Presenter:''' Lonnie Chrisman, Lumina Decision Systems

'''Abstract'''

The Analytica Wiki is a central repository of resources for active Analytica users. What's more, you -- as an active Analytica user -- can contribute to it. As an Analytica community, we have a lot to learn from each other, and the Analytica Wiki provides one very nice forum for doing so. You can contribute example models and libraries, hints and tricks, and descriptions of new techniques. You can fix errors in the Wiki documentation if you spot them, or add to the information that is there when you find subtleties that are not fully described. If you spend a lot of time debugging a problem, after solving it you could document the issue and how it was solved for your own benefit in the future, as well as for others in the user community who may encounter the same problem. When you publish a relevant paper, I hope you will add it to the page listing publications that utilize Analytica models.

I will provide a quick tour of the Analytica Wiki as it exists today. I'll then provide a tutorial on contributing to the Wiki -- e.g., the basics of how to edit or add content. The [http://Wikipedia.org Wikipedia] has had tremendous success with this community content contribution model, and I hope that after this introduction many of you will feel more comfortable contributing to the Wiki as you make use of it.

Due to a problem with the audio on the recording, the recording of this webinar is not available.

= Licensing or Installation =

=== Reprise License Manager Tutorial ===

'''Date and Time:''' Wednesday, 11 March 2010, 10:00am Pacific Standard Time

'''Presenter:''' Bob Mearns, Reprise Software Inc.

'''Abstract'''

The Reprise License Manager (RLM) allows all Analytica and ADE licenses within an organization to be managed from a central server. RLM can be used with either ''floating'' or ''named-user'' licenses.

This tutorial on RLM administration is being given by Bob Mearns, lead software developer at Reprise Software, Inc., who has over 15 years' experience developing and supporting software license managers. This session will focus on:

* Basic RLM Server Setup
* How and where RLM looks for licenses
* Using the RLM Web Server Admin Interface
* Using RLM diagnostics, new in RLM v8
* A systematic approach to diagnosing license server connectivity problems

There is a big focus in this talk on how to debug problems with the RLM license manager, and in the process many of the technical details pertaining to the RLM setup are covered. This talk is most relevant for IT managers who administer the license server, and for people who may be installing the RLM server who would like a more thorough understanding of how things work. The RLM license manager is used to host centrally managed licenses, which includes floating and named-user licenses.

This talk is being provided by Reprise Software.

This webinar may be viewed here: [http://analyticaonline.com/WebinarArchive/2010-03-11-RLM-troubleshooting.wmv RLM-troubleshooting.wmv]. The trouble-shooting tips document covered in the talk is at [http://reprisesoftware.com/RLM_Troubleshooting_Tips.pdf RLM Troubleshooting Tips].

See also:
* [[Configuring an RLM Server]] -- step-by-step for installing the RLM server
* [[How to Install Analytica -- Centrally Managed License]] -- for the Analytica user's side installation