Create generalized linear regression model

returns
a generalized linear model with additional options specified by one
or more `mdl`

= fitglm(___,`Name,Value`

)`Name,Value`

pair arguments.

For example, you can specify which variables are categorical, the distribution of the response variable, and the link function to use.

The generalized linear model

`mdl`

is a standard linear model unless you specify otherwise with the`Distribution`

name-value pair.For methods such as

`plotResiduals`

or`devianceTest`

, or properties of the`GeneralizedLinearModel`

object, see`GeneralizedLinearModel`

.After training a model, you can generate C/C++ code that predicts responses for new data. Generating C/C++ code requires MATLAB Coder™. For details, see Introduction to Code Generation.

`fitglm`

treats a categorical predictor as follows:A model with a categorical predictor that has

*L*levels (categories) includes*L*– 1 indicator variables. The model uses the first category as a reference level, so it does not include the indicator variable for the reference level. If the data type of the categorical predictor is`categorical`

, then you can check the order of categories by using`categories`

and reorder the categories by using`reordercats`

to customize the reference level.`fitglm`

treats the group of*L*– 1 indicator variables as a single variable. If you want to treat the indicator variables as distinct predictor variables, create indicator variables manually by using`dummyvar`

. Then use the indicator variables, except the one corresponding to the reference level of the categorical variable, when you fit a model. For the categorical predictor`X`

, if you specify all columns of`dummyvar(X)`

and an intercept term as predictors, then the design matrix becomes rank deficient.Interaction terms between a continuous predictor and a categorical predictor with

*L*levels consist of the element-wise product of the*L*– 1 indicator variables with the continuous predictor.Interaction terms between two categorical predictors with

*L*and*M*levels consist of the (*L*– 1)*(*M*– 1) indicator variables to include all possible combinations of the two categorical predictor levels.You cannot specify higher-order terms for a categorical predictor because the square of an indicator is equal to itself.

`fitglm`

considers`NaN`

,`''`

(empty character vector),`""`

(empty string),`<missing>`

, and`<undefined>`

values in`tbl`

,`X`

, and`Y`

to be missing values.`fitglm`

does not use observations with missing values in the fit. The`ObservationInfo`

property of a fitted model indicates whether or not`fitglm`

uses each observation in the fit.

Use

`stepwiseglm`

to select a model specification automatically. Use`step`

,`addTerms`

, or`removeTerms`

to adjust a fitted model.

[1] Collett, D. *Modeling Binary
Data*. New York: Chapman & Hall, 2002.

[2] Dobson, A. J. *An Introduction
to Generalized Linear Models*. New York: Chapman &
Hall, 1990.

[3] McCullagh, P., and J. A. Nelder. *Generalized
Linear Models*. New York: Chapman & Hall, 1990.