Difference between revisions of "Logistic Regression"

Latest revision as of 00:15, 19 September 2018

The Logistic_Regression function is obsolete, and has been replaced by the LogisticRegression function. Please see LogisticRegression.

The old Logistic_Regression function (with the underscore) is implemented as a User-Defined Function in the (Generalized Regression library). It requires the Analytica Optimizer edition to use. It still exists to support legacy models.

The newer LogisticRegression function is available in all editions of Analytica. It exists in Analytica 4.5 and up.

To convert a legacy model to use the newer version, simply remove the underscores -- the parameter order is the same.

History

In Analytica 4.5, this library function Logistic_Regression() has been superseded by the built-in LogisticRegression function that does not require the Optimizer edition.

@@ Line 1: / Line 1: @@
-[[Category:Doc Status D]] <!-- For Lumina use, do not change -->
+[[Category: Generalized Regression library functions]]
-[[Category:Data Analysis Functions]]
-Logistic regression is a techique for predicting a Bernoulli (i.e., 0,1-valued) random variable from a set of continuous dependent variables.  See the [http://en.wikipedia.org/wiki/Logistic_regression Wikipedia article on Logistic regression] for a simple description.
+The [[Logistic_Regression]] function is obsolete, and has been replaced by the [[LogisticRegression]] function. Please see [[LogisticRegression]].
-= Logistic_Regression( Y,B,I,K ) =
+The old [[Logistic_Regression]] function (with the underscore) is implemented as a [[User-Defined Function]] in the
+([[media:Generalized Regression.ana|Generalized Regression library]]). It requires the Analytica [[Optimizer]] edition to use. It still exists to support legacy models.
-(''Requires Analytica Optimizer'')
+The newer [[LogisticRegression]] function is available in all editions of Analytica. It exists in [[Analytica 4.5]] and up.
-The Logistic_regression function returns the best-fit coefficients, c, for a model of the form
+To convert a legacy model to use the newer version, simply remove the underscores -- the parameter order is the same.
-<math>
-logit(p_i) = ln\left( {{p_i}\over{1-p_i}} \right) = \sum_k c_k B_{i,k}
-</math>
-given a data set basis B, with each sample classified as y_i, having a classification of 0 or 1.
-The syntax is the same as for the Regression function.  The basis may be of a generalized linear form, that is, each term in the basis may be an arbitrary non-linear function of your data; however, the logit of the prediction is a linear combination of these.
+==History==
+In [[Analytica 4.5]], this library function [[Logistic_Regression]]() has been superseded by the built-in [[LogisticRegression]] function that does not require the Optimizer edition.
-Once you have used the Logistic_Regression function to compute the coefficients for your model, the predictive model that results returns the probability that a given data point is classified as 1.
+== See Also ==
+* [[LogisticRegression]]
-= Library =
-Generalized Regression.ana
-= See Also =
-* [[Probit_Regression]]
-* [[Regression]]