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Generalized Linear Regression

Regression models for limited responses

For greater accuracy and link-function choices on low- through medium-dimensional data sets, fit a generalized linear model using fitglm.

For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, train a binary, linear classification model, such as a logistic regression model, using fitclinear. You can also efficiently train a multiclass error-correcting output codes (ECOC) model composed of logistic regression models using fitcecoc.

Classes

GeneralizedLinearModel Generalized linear regression model class
ClassificationLinear Linear model for binary classification of high-dimensional data
ClassificationECOC Multiclass model for support vector machines or other classifiers
ClassificationPartitionedLinear Cross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOC Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Functions

fitglm Create generalized linear regression model
stepwiseglm Create generalized linear regression model by stepwise regression
disp Display generalized linear regression model
feval Evaluate generalized linear regression model prediction
predict Predict response of generalized linear regression model
random Simulate responses for generalized linear regression model
fitclinear Fit linear classification model to high-dimensional data
templateLinear Linear classification learner template
fitcecoc Fit multiclass models for support vector machines or other classifiers
predict Predict labels for linear classification models
mnrfit Multinomial logistic regression
mnrval Multinomial logistic regression values
glmfit Generalized linear model regression
glmval Generalized linear model values

Examples and How To

Generalized Linear Model Workflow

Fit a generalized linear model and analyze the results.

Train Logistic Regression Classifiers Using Classification Learner App

Learn how to train logistic regression classifiers.

Concepts

Generalized Linear Models

Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.

Multinomial Models for Nominal Responses

A nominal response variable has a restricted set of possible values with no natural order between them. A nominal response model explains and predicts the probability that an observation is in each category of a categorical response variable.

Multinomial Models for Ordinal Responses

An ordinal response variable has a restricted set of possible values that fall into a natural order. An ordinal response model describes the relationship between the cumulative probabilities of the categories and predictor variables.

Hierarchical Multinomial Models

A hierarchical multinomial response variable (also known as a sequential or nested multinomial response) has a restricted set of possible values that fall into hierarchical categories. The hierarchical multinomial regression models are extensions of binary regression models based on conditional binary observations.

Wilkinson Notation

Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

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