View source for Analytica User FAQs/Expressions - How to

(In progress -- two things need to occur.  Common questions need to be collected here and organized, and then answers need to be added.  Feel free to contribute to either aspect)

= Array Abstraction =

=== How do I access a single row of an array? ===

See [[Subscript/Slice Operator]].  The syntax is ''A[I=x]'', where ''A'' is the array, ''I'' is the index that identifies the row dimension, and ''x'' identifies the row using the value or label from ''I''.  

=== How do I represent a square matrix? ===

The rule in Analytica is that every dimension must correspond to a different index.  So when you have two dimensions, you need two separate indexes.  In the case of a square matrix, the second index is often a copy of the first.  

Try this.  Create an index node, name it ''State'', and define it as ''1..5''.  How create a second index, name it ''State2'', and define it as ''[[CopyIndex]](State)''.  Now you have two indexes with identical value.  You have what you need to represent a square matrix.  Finally, create a variable, name it ''State_Identity'', define it as ''State=State2'', and show the result table. You just created a square identity matrix.

=== How do I re-index an array, exchanging one index, I, for another of the same length, J? ===

Again, this is accomplished using the [[Subscript/Slice Operator]].  Two methods:
* A[I=J]
* A[@I=@J]

The first re-indexes associatively, the second re-indexes by position.  You can use the first when I and J have the same labels or values with no duplicates (in general, it is bad practice to have duplicate values in indexes).  The second can be used if they are the same length, but have different values. 

=== How do I aggregate an array from a fine grain index (e.g., days) to a coarser index (e.g., months)? ===

Generally, to aggregate, you will need a mapping (many-to-one) from the fine grain to the coarse grain.  This mapping will take the form of an array, indexed by the fine grain (e.g., days), where each value in the array is an element in the coarser index (e.g., months).    For illustration, let's call this ''Day2Month''.

Once you have this map, use the [[Aggregate]] function.  Here is an example:
<code>
:Index theDate
:Variable Revenue_by_date  { ''indexed by theDate'' }
:Variable Date_to_year_map := [[DatePart]]( theDate, "Y" )
:Variable Year := 2000..2020
:Variable Revenue_by_year := [[Aggregate]](Revenue_by_date, Date_to_year_map, theDate, Year )
</code>

See [[Aggregate]] for more details.

= Distributions =

* How do I generate independent distributions across an index, for example, so that Noise := Normal(0,1) is independent for each time point t?

* How do I define a chance variable so that its uncertainty is correlated with an existing chance variable?

* [[How to Fit a Distribution to Data|How do a fit a distribution to historical data?]]

= User-Defined Functions =

* How do I create my own User-Defined function?

* How do I create a custom distribution function?

= How do I model XXX? =

* [[Modeling Depreciation|How do I model depreciation over time?]]

== What does Analytica use to represent missing values? ==

Analytica has no special MissingValue constant.  The constant [[Null]] typically serves that purpose.

Several common functions that operate over arrays, but not all functions, will ignore [[Null]] values.  For example, [[Sum]], [[Product]], [[Max]], [[Min]], [[Average]], etc.  This treatment of [[Null]] by these functions is relatively recent -- starting with 4.0 -- and we've been enhancing the remaining functions with successive releases.  Currently there still remain several built-in functions that do not ignore [[Null]], such as many statistical functions, and your own UDFs may not ignore nulls.  You may also desire a different treatment of missing values than just ignoring the values. To deal with these cases, you will need to insert appropriate conditional logic to deal with the missing values in the appropriate fashion. For example:

:Variance( MaybeMissing(x), I )

:Cumulate( MaybeMissing(x,1), I )

where
:Function MaybeMissing( x ; defX : optional )
:Definition: if x=Null then if IsNotSpecified(defX) then 0 else defX else X

= Debugging =

== How do I go about debugging a model? ==

See [[Debugging Hints]]