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. For more details about creating indicator variables, see Automatic Creation of Dummy Variables.
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.