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ProbitModelFit[{{x1,y1},{x2,y2},},{f1,f2,},x]

constructs a binomial probit regression model of the form that fits the yi for each xi.

ProbitModelFit[data,{f1,f2,},{x1,x2,}]

constructs a binomial probit regression model of the form where the fi depend on the variables xk.

ProbitModelFit[{m,v}]

constructs a binomial probit regression model from the design matrix m and response vector v.

Details and Options

  • ProbitModelFit attempts to model the data using a linear combination of basis functions composed with the inverse of the probit function ().
  • LogitModelFit is typically used in classification to model probability values.
  • ProbitModelFit produces a generalized linear model of the form under the assumption that the original are independent realizations of Bernoulli trials with probabilities .
  • The function is the CDF of the standard NormalDistribution.
  • ProbitModelFit returns a symbolic FittedModel object to represent the probit model it constructs. The properties and diagnostics of the model can be obtained from model["property"].
  • The value of the best-fit function from ProbitModelFit at a particular point x1, can be found from model[x1,].
  • Possible forms of data are:
  • {y1,y2,}equivalent to the form {{1,y1},{2,y2},}
    {{x11,x12,,y1},}a list of independent values xij and the responses yi
    {{x11,x12,}y1,}a list of rules between input values and responses
    {{x11,x12,},}{y1,y2,}a rule between a list of input values and responses
    {{x11,,y1,},}nfit the nth column of a matrix
  • With multivariate data such as {{x_(11),x_(12),... ,y_(1)},{x_(21),x_(22),... ,y_(2)},...}, the number of coordinates xi1, xi2, should equal the number of variables xi.
  • The yi are probabilities between 0 and 1.
  • Additionally, data can be specified using a design matrix without specifying functions and variables:
  • {m,v}a design matrix m and response vector v
  • In ProbitModelFit[{m,v}], the design matrix m is formed from the values of basis functions fi at data points in the form {{f1,f2,},{f1,f2,},}. The response vector v is the list of responses {y1,y2,}.
  • 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 fi can be specified using the form ProbitModelFit[{m,v},{f1,f2,}].
  • ProbitModelFit is equivalent to GeneralizedLinearModelFit with ExponentialFamily->"Binomial" and LinkFunction->"ProbitLink".
  • ProbitModelFit takes the same options as GeneralizedLinearModelFit, with the exception of ExponentialFamily and LinkFunction.

Examples

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Basic Examples  (1)Summary of the most common use cases

Define a dataset:

Fit a probit model to the data:

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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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Scope  (13)Survey of the scope of standard use cases

Data  (6)

Fit data with success probability responses, assuming increasing integer-independent values:

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This is equivalent to:

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Weight by the number of observations for each predictor value:

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This gives the same best fit function as success failure data:

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Fit a list of rules:

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Fit a rule of input values and responses:

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Specify a column as the response:

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Fit a model given a design matrix and response vector:

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See the functional form:

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Fit the model referring to the basis functions as and :

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Obtain a list of available properties:

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Properties  (7)

Data & Fitted Functions  (1)

Fit a probit model:

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Extract the original data:

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Obtain and plot the best fit:

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Obtain the fitted function as a pure function:

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Get the design matrix and response vector for the fitting:

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Residuals  (1)

Examine residuals for a fit:

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Visualize the raw residuals:

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Visualize Anscombe residuals and standardized Pearson residuals in stem plots:

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Dispersion and Deviances  (1)

Fit a probit model to some data:

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The estimated dispersion is 1 by default:

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Use Pearson's as the dispersion estimator instead:

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Plot the deviances for each point:

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Obtain the analysis of deviance table:

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Get the residual deviances from the table:

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Parameter Estimation Diagnostics  (1)

Obtain a formatted table of parameter information:

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Extract the column of -statistic values:

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Influence Measures  (1)

Fit some data containing extreme values to a probit model:

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Check Cook distances to identify highly influential points:

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Check the diagonal elements of the hat matrix to assess influence of points on the fitting:

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Prediction Values  (1)

Fit a probit model:

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Plot the predicted values against the observed values:

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Goodness-of-Fit Measures  (1)

Obtain a table of goodness-of-fit measures for a probit model:

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Compute goodness-of-fit measures for all subsets of predictor variables:

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Rank the models by AIC:

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Generalizations & Extensions  (1)Generalized and extended use cases

Perform other mathematical operations on the functional form of the model:

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Integrate symbolically and numerically:

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Find a predictor value that gives a particular value for the model:

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Options  (8)Common values & functionality for each option

ConfidenceLevel  (1)

The default gives 95% confidence intervals:

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Use 99% intervals instead:

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Set the level to 90% within FittedModel:

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CovarianceEstimatorFunction  (1)

Fit a probit model:

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Compute the covariance matrix using the expected information matrix:

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Use the observed information matrix instead:

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DispersionEstimatorFunction  (1)

Fit a probit model:

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Compute the covariance matrix:

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Compute the covariance matrix estimating the dispersion by Pearson's :

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IncludeConstantBasis  (1)

Fit a probit model:

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Fit the model with zero constant term:

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LinearOffsetFunction  (1)

Fit data to a probit model:

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Fit data to a model with a known Sqrt[x] term:

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NominalVariables  (1)

Fit the data, treating the first variable as a nominal variable:

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Treat both variables as nominal:

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Weights  (1)

Fit a model using equal weights:

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Give explicit weights for the data points:

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WorkingPrecision  (1)

Use WorkingPrecision to get higher precision in parameter estimates:

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Obtain the fitted function:

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Reduce the precision in property computations after the fitting:

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Properties & Relations  (4)Properties of the function, and connections to other functions

ProbitModelFit is equivalent to a "Binomial" model from GeneralizedLinearModelFit with "ProbitLink":

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LogitModelFit is a "Binomial" model from GeneralizedLinearModelFit with default "LogitLink":

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ProbitModelFit assumes binomially distributed responses:

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NonlinearModelFit assumes normally distributed responses:

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The fits are not identical:

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ProbitModelFit will use the time stamps of a TimeSeries as variables:

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Rescale the time stamps and fit again:

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Find fit for the values:

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ProbitModelFit acts pathwise on a multipath TemporalData:

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Possible Issues  (1)Common pitfalls and unexpected behavior

Responses outside the interval from 0 to 1 are not valid for probit models:

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Wolfram Research (2008), ProbitModelFit, Wolfram Language function, https://reference.wolfram.com/language/ref/ProbitModelFit.html.
Wolfram Research (2008), ProbitModelFit, Wolfram Language function, https://reference.wolfram.com/language/ref/ProbitModelFit.html.

Text

Wolfram Research (2008), ProbitModelFit, Wolfram Language function, https://reference.wolfram.com/language/ref/ProbitModelFit.html.

Wolfram Research (2008), ProbitModelFit, Wolfram Language function, https://reference.wolfram.com/language/ref/ProbitModelFit.html.

CMS

Wolfram Language. 2008. "ProbitModelFit." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ProbitModelFit.html.

Wolfram Language. 2008. "ProbitModelFit." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ProbitModelFit.html.

APA

Wolfram Language. (2008). ProbitModelFit. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ProbitModelFit.html

Wolfram Language. (2008). ProbitModelFit. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ProbitModelFit.html

BibTeX

@misc{reference.wolfram_2024_probitmodelfit, author="Wolfram Research", title="{ProbitModelFit}", year="2008", howpublished="\url{https://reference.wolfram.com/language/ref/ProbitModelFit.html}", note=[Accessed: 08-January-2025 ]}

@misc{reference.wolfram_2024_probitmodelfit, author="Wolfram Research", title="{ProbitModelFit}", year="2008", howpublished="\url{https://reference.wolfram.com/language/ref/ProbitModelFit.html}", note=[Accessed: 08-January-2025 ]}

BibLaTeX

@online{reference.wolfram_2024_probitmodelfit, organization={Wolfram Research}, title={ProbitModelFit}, year={2008}, url={https://reference.wolfram.com/language/ref/ProbitModelFit.html}, note=[Accessed: 08-January-2025 ]}

@online{reference.wolfram_2024_probitmodelfit, organization={Wolfram Research}, title={ProbitModelFit}, year={2008}, url={https://reference.wolfram.com/language/ref/ProbitModelFit.html}, note=[Accessed: 08-January-2025 ]}