Logistic Regression


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 Wikipedia article on Logistic regression for a simple description. Another generalized logistic model that can be used for this purpose is the Probit_Regression model. These differ in functional form, with the logistic regression using a logit function to link the linear predictor to the predicted probability, while the probit model uses a cumulative normal for the same.

Logistic_Regression( Y,B,I,K )

(Requires Analytica Optimizer)

The Logistic_regression function returns the best-fit coefficients, c, for a model of the form [math]\displaystyle{ 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.

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.

Library

Generalized Regression.ana

See Also

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