# Wolfram Language & System 10.4 (2016)|Legacy Documentation

This is documentation for an earlier version of the Wolfram Language.
BUILT-IN WOLFRAM LANGUAGE SYMBOL

# LogitModelFit

LogitModelFit[{y1,y2,},{f1,f2,},x]
constructs a binomial logistic regression model of the form that fits the for successive x values 1, 2, .

LogitModelFit[{{x11,x12,,y1},{x21,x22,,y2},},{f1,},{x1,x2,}]
constructs a binomial logistic regression model of the form where the depend on the variables .

LogitModelFit[{m,v}]
constructs a binomial logistic regression model from the design matrix m and response vector v.

## Details and OptionsDetails and Options

• LogitModelFit returns a symbolic FittedModel object to represent the logistic model it constructs. The properties and diagnostics of the model can be obtained from model["property"].
• The value of the best-fit function from LogitModelFit at a particular point , can be found from .
• With data in the form , the number of coordinates , , should correspond to the number of variables .
• The are probabilities between 0 and 1.
• Data in the form is equivalent to data in the form .
• LogitModelFit produces a logistic model of the form under the assumption that the original are independent observations following binomial distributions with mean .
• In LogitModelFit[{m,v}], the design matrix m is formed from the values of basis functions at data points in the form . The response vector v is the list of responses .
• For a design matrix m and response vector v, the model is , where is the vector of parameters to be estimated.
• When a design matrix is used, the basis functions can be specified using the form LogitModelFit[{m,v},{f1,f2,}].
• LogitModelFit is equivalent to GeneralizedLinearModelFit with ExponentialFamily->"Binomial" and .
• LogitModelFit takes the same options as GeneralizedLinearModelFit, with the exception of ExponentialFamily and LinkFunction.

## ExamplesExamplesopen allclose all

### Basic Examples  (1)Basic Examples  (1)

Define a dataset:

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Fit a logistic model to the data:

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See the functional forms of the model:

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Evaluate the model at a point:

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Plot the data points and the models:

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Compute the fitted values for the model:

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Visualize the deviance residuals:

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