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GeneralizedLinearModelFit: GLM—Wolfram Documentation

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• See Also
  • FittedModel
  • LogitModelFit
  • ProbitModelFit
  • LinearModelFit
  • NonlinearModelFit
  • TimeSeriesModelFit
  • Fit
  • LeastSquares
  • FindFit
  • Methods
  • LinearRegression
• Related Guides
  • Statistical Model Analysis
  • Statistical Data Analysis
  • Scientific Data Analysis
  • Numerical Data
  • Matrix-Based Minimization
  • Tabular Modeling
  • Supervised Machine Learning
• Tech Notes
  • Statistical Model Analysis

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

constructs a generalized linear model of the form that fits the y_i for each x_i.

GeneralizedLinearModelFit[data,{f₁,f₂,…},{x₁,x₂,…}]

constructs a generalized linear model of the form where the f_i depend on the variables x_k.

• GeneralizedLinearModelFit attempts to model the input data using a linear combination of functions transformed by a generic invertible function (link function).
• GeneralizedLinearModelFit produces a generalized linear model of the form under the assumption that the original are independent observations following an exponential family distribution with mean and the function being an invertible link function.
• The ExponentialFamily option controls the distribution while the LinkFunction option controls the form of .
• GeneralizedLinearModelFit returns a symbolic FittedModel object to represent the generalized linear model it constructs. The properties and diagnostics of the model can be obtained from model["property"].
• The value of the best-fit function from GeneralizedLinearModelFit 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 x_ij and the responses y_i {{x₁₁,x₁₂,…}y₁,…} a list of rules between input values and response {{x₁₁,x₁₂,…},…}{y₁,y₂,…} a rule between a list of input values and responses {{x₁₁,…,y₁,…},…}n fit the n column of a matrix Tabular[…]name fit the column name in a tabular object
• With multivariate data such as , the number of coordinates x_i1, x_i2, … should equal the number of variables x_i.
• 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 GeneralizedLinearModelFit[m,v], the design matrix m is formed from the values of basis functions f_i 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 f_i can be specified using the form GeneralizedLinearModelFit[{m,v},{f₁,f₂,…}].
• GeneralizedLinearModelFit takes the following options:
• With the setting IncludeConstantBasis->False, a model of the form is fitted.
• With the setting LinearOffsetFunction->h, a model of the form is fitted.
• With ConfidenceLevel->p, probability-p confidence intervals are computed for parameter and prediction intervals.
• With the setting DispersionEstimatorFunction->f, the common dispersion is estimated by f[y,,w] where y={y₁,y₂,…} is the list of observations, ={,,…} is the list of predicted values, and w={w₁,w₂,…} is the list of weights for the measurements y_i.
• Possible settings for ExponentialFamily include: "Gaussian", "Binomial", "Poisson", "Gamma", "InverseGaussian", or "QuasiLikelihood".
Properties
• Properties related to data and the fitted function obtained using model["property"] include:
• "BasisFunctions" list of basis functions "BestFit" fitted function "BestFitParameters" parameter estimates "Data" the input data or design matrix and response vector "DesignMatrix" design matrix for the model "Function" best fit pure function "LinearPredictor" fitted linear combination "Response" response values in the input data "Weights" weights used to fit the data
• Properties related to dispersion and model deviances include:
• "Deviances" deviances "DevianceData" deviance table dataset "EstimatedDispersion" estimated dispersion parameter "NullDeviance" deviance for the null model "NullDegreesOfFreedom" degrees of freedom for the null model "ResidualDeviance" difference between the deviance for the fitted model and the deviance for the full model "ResidualDegreesOfFreedom" difference between the model degrees of freedom and null degrees of freedom
• Types of residuals include:
• "AnscombeResiduals" Anscombe residuals "DevianceResiduals" deviance residuals "FitResiduals" difference between actual and predicted responses "LikelihoodResiduals" likelihood residuals "PearsonResiduals" Pearson residuals "StandardizedDevianceResiduals" standardized deviance residuals "StandardizedPearsonResiduals" standardized Pearson residuals "WorkingResiduals" working residuals
• Properties and diagnostics for parameter estimates include:
• "CorrelationMatrix" asymptotic parameter correlation matrix "CovarianceMatrix" asymptotic parameter covariance matrix "ParameterEstimates" table of fitted parameter information
• Properties related to influence measures include:
• "CookDistances" list of Cook distances "HatDiagonal" diagonal elements of the hat matrix
• Properties of predicted values include:
• "PredictedResponse" fitted values for the data
• Properties that measure goodness of fit include:

Define a dataset:

Fit a log-linear Poisson model to the data:

See the functional forms of the model:

Evaluate the model at a point:

Plot the data points and the models:

Compute and plot the deviance residuals for the model:

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

This is equivalent to:

Fit a model of more than one variable:

Fit data to a linear combination of functions of predictor variables:

Fit a list of rules:

Fit a rule of input values and responses:

Specify a column as the response:

Fit a model with categorical predictor variables:

Obtain a deviance table for the model:

Fit a model given a design matrix and response vector:

See the functional form:

Fit the model referring to the basis functions as x and y:

Obtain a list of available properties for a generalized linear model:

Fit a generalized linear model:

Extract the original data:

Obtain and plot the best fit:

Obtain the fitted function as a pure function:

Get the design matrix and response vector for the fitting:

Examine residuals for a fit:

Visualize the raw residuals:

Visualize Anscombe residuals and standardized Pearson residuals in stem plots:

Fit a gamma regression model to some data:

Obtain the estimated dispersion:

Plot the deviances for each point:

Get a dataset of the deviance table:

Get the residual deviances from the table:

Obtain a formatted table of parameter information:

Extract the column of -statistic values:

Fit some data containing extreme values to a logit model:

Check Cook distances to identify highly influential points:

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

Fit an inverse Gaussian model:

Plot the predicted values against the observed values:

Obtain a table of goodness-of-fit measures for a log-linear Poisson model:

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

Rank the models by AIC:

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

Integrate symbolically and numerically:

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

The default gives 95% confidence intervals:

Use 99% intervals instead:

Set the level to 90% within FittedModel:

Fit a generalized linear model:

Compute the covariance matrix using the expected information matrix:

Use the observed information matrix instead:

Fit a binomial model:

Compute the covariance matrix:

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

Fit data to a simple linear regression model:

Fit to a canonical gamma regression model:

Fit to a canonical inverse Gaussian regression model:

Fit a simple linear regression model:

Fit the linear model with intercept zero:

Fit data to a canonical gamma regression model:

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

Fit a Poisson model with canonical Log link:

Use a named link:

Use a pure function for a shifted Sqrt link:

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

Treat both variables as nominal:

Fit a model using equal weights:

Give explicit weights for the data points:

Use WorkingPrecision to get higher precision in parameter estimates:

Obtain the fitted function:

Reduce the precision in property computations after the fitting:

Simulate some probability data:

Fit and visually compare binomial generalized linear models with a variety of link functions:

Fit count data from a contingency table to a Poisson log-linear model:

Display counts, predicted values, and standardized residuals in a tabular form:

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