Superclasses: CompactGeneralizedLinearModel
Generalized linear regression model class
An object comprising training data, model description, diagnostic
information, and fitted coefficients for a generalized linear regression.
Predict model responses with the predict
or feval
methods.
or mdl
=
fitglm(tbl
)
creates
a generalized linear model of a table or dataset array mdl
=
fitglm(X
,y
)tbl
,
or of the responses y
to a data matrix X
.
For details, see fitglm
.
or mdl
= stepwiseglm(tbl
)
creates
a generalized linear model of a table or dataset array mdl
=
stepwiseglm(X
,y
)tbl
,
or of the responses y
to a data matrix X
,
with unimportant predictors excluded. For details, see stepwiseglm
.
tbl
— Input dataInput data, specified as a table or dataset array. When modelspec
is a
formula, the formula specifies the predictor and response variables. Otherwise, if you
do not specify the predictor and response variables, the last variable in
tbl
is the response variable and the others are the predictor
variables by default.
The predictor variables and response variable can be numeric, logical, categorical, character,
or string. The response variable can have a data type other than numeric only if
'Distribution'
is 'binomial'
.
To set a different column as the response variable, use the ResponseVar
namevalue
pair argument. To use a subset of the columns as predictors, use the PredictorVars
namevalue
pair argument.
X
— Predictor variablesPredictor variables, specified as an nbyp matrix,
where n is the number of observations and p is
the number of predictor variables. Each column of X
represents
one variable, and each row represents one observation.
By default, there is a constant term in the model, unless you
explicitly remove it, so do not include a column of 1s in X
.
Data Types: single
 double
y
— Response variableResponse variable, specified as an nby1
vector, where n is the number of observations.
Each entry in y
is the response for the corresponding
row of X
.
Data Types: single
 double
 logical
 categorical
CoefficientCovariance
— Covariance matrix of coefficient estimatesThis property is readonly.
Covariance matrix of coefficient estimates, specified as a pbyp matrix of numeric values. p is the number of coefficients in the fitted model.
For details, see Coefficient Standard Errors and Confidence Intervals.
Data Types: single
 double
CoefficientNames
— Coefficient namesThis property is readonly.
Coefficient names, specified as a cell array of character vectors, each containing the name of the corresponding term.
Data Types: cell
Coefficients
— Coefficient valuesThis property is readonly.
Coefficient values, specified as a table.
Coefficients
contains one row for each coefficient and these
columns:
Estimate
— Estimated
coefficient value
SE
— Standard error
of the estimate
tStat
— tstatistic for a test that the
coefficient is zero
pValue
— pvalue for the
tstatistic
Use anova
(only for a linear regression model) or
coefTest
to perform other tests on the coefficients. Use
coefCI
to find the confidence intervals of the coefficient
estimates.
To obtain any of these columns as a vector, index into the property
using dot notation. For example, obtain the estimated coefficient vector in the model
mdl
:
beta = mdl.Coefficients.Estimate
Data Types: table
Deviance
— Deviance of the fitThis property is readonly.
Deviance of the fit, specified as a numeric value. Deviance is useful for comparing two models when one model is a special case of the other model. The difference between the deviance of the two models has a chisquare distribution with degrees of freedom equal to the difference in the number of estimated parameters between the two models. For more information on deviance, see Deviance.
Data Types: single
 double
DFE
— Degrees of freedom for errorThis property is readonly.
Degrees of freedom for the error (residuals), equal to the number of observations minus the number of estimated coefficients, specified as a positive integer.
Data Types: double
Diagnostics
— Diagnostic informationThis property is readonly.
Diagnostic information for the model, specified as a table. Diagnostics
can help identify outliers and influential observations.
Diagnostics
contains the following fields.
Field  Meaning  Utility 

Leverage  Diagonal elements of HatMatrix  Leverage indicates to what extent the predicted value for
an observation is determined by the observed value for that
observation. A value close to 1 indicates
that the prediction is largely determined by that
observation, with little contribution from the other
observations. A value close to 0
indicates the fit is largely determined by the other
observations. For a model with p
coefficients and n observations, the
average value of Leverage is
p/n. An
observation with Leverage larger than
2*p/n can be an
outlier. 
CooksDistance  Cook's measure of scaled change in fitted values  CooksDistance is a measure of scaled
change in fitted values. An observation with
CooksDistance larger than three times
the mean Cook's distance can be an outlier. 
HatMatrix  Projection matrix to compute fitted from observed responses  HatMatrix is an
nbyn matrix such
that Fitted = HatMatrix*Y ,
where Y is the response vector and
Fitted is the vector of fitted
response values. 
All of these quantities are computed on the scale of the linear predictor. For example, in the equation that defines the hat matrix:
Yfit = glm.Fitted.LinearPredictor Y = glm.Fitted.LinearPredictor + glm.Residuals.LinearPredictor
Data Types: table
Dispersion
— Scale factor of the variance of the responseThis property is readonly.
Scale factor of the variance of the response, specified as a numeric
value. Dispersion
multiplies the variance function for
the distribution.
For example, the variance function for the binomial distribution is
p(1–p)/n, where
p is the probability parameter and
n is the sample size parameter. If
Dispersion
is near 1
, the variance
of the data appears to agree with the theoretical variance of the binomial
distribution. If Dispersion
is larger than
1
, the data set is “overdispersed”
relative to the binomial distribution.
Data Types: double
DispersionEstimated
— Flag to indicate use of dispersion scale factorThis property is readonly.
Flag to indicate whether fitglm
used the
Dispersion
scale factor to compute standard errors
for the coefficients in Coefficients.SE
, specified as a
logical value. If DispersionEstimated
is
false
, fitglm
used the theoretical
value of the variance.
DispersionEstimated
can be
false
only for 'binomial'
or 'poisson'
distributions.
Set DispersionEstimated
by setting the
DispersionFlag
namevalue pair in
fitglm
.
Data Types: logical
Distribution
— Generalized distribution informationThis property is readonly.
Generalized distribution information, specified as a structure with the following fields relating to the generalized distribution.
Field  Description 

Name  Name of the distribution, one of
'normal' ,
'binomial' ,
'poisson' ,
'gamma' , or 'inverse
gaussian' . 
DevianceFunction  Function that computes the components of the deviance as a function of the fitted parameter values and the response values. 
VarianceFunction  Function that computes the theoretical variance for the
distribution as a function of the fitted parameter values.
When DispersionEstimated is
true , Dispersion
multiplies the variance function in the computation of the
coefficient standard errors. 
Data Types: struct
Fitted
— Fitted response values based on input dataThis property is readonly.
Fitted (predicted) values based on the input data, specified as a table with one row for each observation and the following columns.
Field  Description 

Response  Predicted values on the scale of the response. 
LinearPredictor  Predicted values on the scale of the linear predictor.
These are the same as the link function applied to the
Response fitted values. 
Probability  Fitted probabilities (this column is included only with the binomial distribution). 
To obtain any of the columns as a vector, index into the property using
dot notation. For example, in the model mdl
, the vector
f
of fitted values on the response scale is
f = mdl.Fitted.Response
Use predict
to compute predictions
for other predictor values, or to compute confidence bounds on
Fitted
.
Data Types: table
Formula
— Model informationLinearFormula
objectThis property is readonly.
Model information, specified as a LinearFormula
object.
Display the formula of the fitted model mdl
using dot
notation:
mdl.Formula
Link
— Link functionThis property is readonly.
Link function, specified as a structure with the following fields.
Field  Description 

Name  Name of the link function, or '' if
you specified the link as a function handle rather than a
character vector. 
LinkFunction  The function that defines f, a function handle. 
DevianceFunction  Derivative of f, a function handle. 
VarianceFunction  Inverse of f, a function handle. 
The link is a function f that links the distribution parameter μ to the fitted linear combination Xb of the predictors:
f(μ) = Xb.
Data Types: struct
LogLikelihood
— Log likelihoodThis property is readonly.
Log likelihood of the model distribution at the response values, specified as a numeric value. The mean is fitted from the model, and other parameters are estimated as part of the model fit.
Data Types: single
 double
ModelCriterion
— Criterion for model comparisonThis property is readonly.
Criterion for model comparison, specified as a structure with these fields:
AIC
— Akaike information criterion.
AIC = –2*logL + 2*m
, where logL
is the
loglikelihood and m
is the number of estimated
parameters.
AICc
— Akaike information criterion corrected for
the sample size. AICc = AIC + (2*m*(m+1))/(n–m–1)
, where
n
is the number of observations.
BIC
— Bayesian information criterion.
BIC = –2*logL + m*log(n)
.
CAIC
— Consistent Akaike information criterion.
CAIC = –2*logL + m*(log(n)+1)
.
Information criteria are model selection tools that you can use to compare multiple models fit to the same data. These criteria are likelihoodbased measures of model fit that include a penalty for complexity (specifically, the number of parameters). Different information criteria are distinguished by the form of the penalty.
When you compare multiple models, the model with the lowest information criterion value is the bestfitting model. The bestfitting model can vary depending on the criterion used for model comparison.
To obtain any of the criterion values as a scalar, index into the property using dot
notation. For example, obtain the AIC value aic
in the model
mdl
:
aic = mdl.ModelCriterion.AIC
Data Types: struct
NumCoefficients
— Number of model coefficientsThis property is readonly.
Number of model coefficients, specified as a positive integer.
NumCoefficients
includes coefficients that are set to zero when
the model terms are rank deficient.
Data Types: double
NumEstimatedCoefficients
— Number of estimated coefficientsThis property is readonly.
Number of estimated coefficients in the model, specified as a positive integer.
NumEstimatedCoefficients
does not include coefficients that are
set to zero when the model terms are rank deficient.
NumEstimatedCoefficients
is the degrees of freedom for
regression.
Data Types: double
NumObservations
— Number of observationsThis property is readonly.
Number of observations the fitting function used in fitting, specified
as a positive integer. NumObservations
is the
number of observations supplied in the original table, dataset,
or matrix, minus any excluded rows (set with the
'Exclude'
namevalue pair
argument) or rows with missing values.
Data Types: double
NumPredictors
— Number of predictor variablesThis property is readonly.
Number of predictor variables used to fit the model, specified as a positive integer.
Data Types: double
NumVariables
— Number of variablesThis property is readonly.
Number of variables in the input data, specified as a positive integer.
NumVariables
is the number of variables in the original table or
dataset, or the total number of columns in the predictor matrix and response
vector.
NumVariables
also includes any variables that are not used to fit
the model as predictors or as the response.
Data Types: double
ObservationInfo
— Observation informationThis property is readonly.
Observation information, specified as an nby4 table, where
n is equal to the number of rows of input data. The
ObservationInfo
contains the columns described in this
table.
Column  Description 

Weights  Observation weight, specified as a numeric value. The default value
is 1 . 
Excluded  Indicator of excluded observation, specified as a logical value. The
value is true if you exclude the observation from the
fit by using the 'Exclude' namevalue pair
argument. 
Missing  Indicator of missing observation, specified as a logical value. The
value is true if the observation is missing. 
Subset  Indicator of whether or not a fitting function uses the observation,
specified as a logical value. The value is true if
the observation is not excluded or missing, meaning that fitting
function uses the observation. 
To obtain any of these columns as a vector, index into the property using dot
notation. For example, obtain the weight vector w
of the model
mdl
:
w = mdl.ObservationInfo.Weights
Data Types: table
ObservationNames
— Observation namesThis property is readonly.
Observation names, specified as a cell array of character vectors containing the names of the observations used in the fit.
If the fit is based on a table or dataset
containing observation names,
ObservationNames
uses those
names.
Otherwise, ObservationNames
is an empty cell array.
Data Types: cell
Offset
— Offset variableThis property is readonly.
Offset variable, specified as a numeric vector with the same length as the
number of rows in the data. Offset
is passed from
fitglm
or stepwiseglm
in the
Offset
namevalue pair. The fitting function used
Offset
as a predictor variable, but with the
coefficient set to exactly 1
. In other words, the formula
for fitting was
μ ~ Offset + (terms involving real
predictors)
with the Offset
predictor having coefficient
1
.
For example, consider a Poisson regression model. Suppose the number of
counts is known for theoretical reasons to be proportional to a predictor
A
. By using the log link function and by specifying
log(A)
as an offset, you can force the model to
satisfy this theoretical constraint.
Data Types: double
PredictorNames
— Names of predictors used to fit modelThis property is readonly.
Names of predictors used to fit the model, specified as a cell array of character vectors.
Data Types: cell
Residuals
— Residuals for fitted modelThis property is readonly.
Residuals for the fitted model, specified as a table with one row for each observation and the following columns.
Field  Description 

Raw  Observed minus fitted values. 
LinearPredictor  Residuals on the linear predictor scale, equal to the adjusted response value minus the fitted linear combination of the predictors. 
Pearson  Raw residuals divided by the estimated standard deviation of the response. 
Anscombe  Residuals defined on transformed data with the transformation chosen to remove skewness. 
Deviance  Residuals based on the contribution of each observation to the deviance. 
To obtain any of these columns as a vector, index into the property using
dot notation. For example, in a model mdl
, the ordinary
raw residual vector r
is:
r = mdl.Residuals.Raw
Rows not used in the fit because of missing values (in
ObservationInfo.Missing
) contain
NaN
values.
Rows not used in the fit because of excluded values (in
ObservationInfo.Excluded
) contain
NaN
values, with the following exceptions:
raw
contains the difference between the
observed and predicted values.
standardized
is the residual, standardized in
the usual way.
studentized
matches the standardized values
because this residual is not used in the estimate of the residual
standard deviation.
Data Types: table
ResponseName
— Response variable nameThis property is readonly.
Response variable name, specified as a character vector.
Data Types: char
Rsquared
— Rsquared value for the modelThis property is readonly.
Rsquared value for the model, specified as a structure with five fields:
Ordinary
— Ordinary (unadjusted)
Rsquared
Adjusted
— Rsquared adjusted for the number of
coefficients
LLR
— Loglikelihood ratio
Deviance
— Deviance
AdjGeneralized
— Adjusted generalized
Rsquared
The Rsquared value is the proportion of total sum of squares explained by the model.
The ordinary Rsquared value relates to the SSR
and
SST
properties:
Rsquared = SSR/SST = 1  SSE/SST
.
To obtain any of these values as a scalar, index into the property using dot notation.
For example, the adjusted Rsquared value in mdl
is
r2 = mdl.Rsquared.Adjusted
Data Types: struct
SSE
— Sum of squared errorsThis property is readonly.
Sum of squared errors (residuals), specified as a numeric value.
The Pythagorean theorem implies
SST = SSE + SSR
,
where SST
is the total sum of squares,
SSE
is the sum of squared errors, and SSR
is
the regression sum of squares.
Data Types: single
 double
SSR
— Regression sum of squaresThis property is readonly.
Regression sum of squares, specified as a numeric value. The regression sum of squares is equal to the sum of squared deviations of the fitted values from their mean.
The Pythagorean theorem implies
SST = SSE +
SSR
,
where SST
is the total sum
of squares, SSE
is the sum of squared errors,
and SSR
is the regression sum of
squares.
Data Types: single
 double
SST
— Total sum of squaresThis property is readonly.
Total sum of squares, specified as a numeric value. The total sum of squares is equal
to the sum of squared deviations of the response vector y
from the
mean(y)
.
The Pythagorean theorem implies
SST = SSE + SSR
,
where SST
is the total sum of squares,
SSE
is the sum of squared errors, and SSR
is
the regression sum of squares.
Data Types: single
 double
Steps
— Stepwise fitting informationThis property is readonly.
Stepwise fitting information, specified as a structure with the fields described in this table.
Field  Description 

Start  Formula representing the starting model 
Lower  Formula representing the lower bound model. The terms in
Lower must remain in the model. 
Upper  Formula representing the upper bound model. The model cannot contain
more terms than Upper . 
Criterion  Criterion used for the stepwise algorithm, such as
'sse' 
PEnter  Threshold for Criterion to add a term 
PRemove  Threshold for Criterion to remove a term 
History  Table representing the steps taken in the fit 
The History
table contains one row for each step, including the
initial fit, and the columns described in this table.
Column  Description 

Action  Action taken during the step:

TermName 

Terms  Model specification in a Terms Matrix 
DF  Regression degrees of freedom after the step 
delDF  Change in regression degrees of freedom from the previous step (negative for steps that remove a term) 
Deviance  Deviance (residual sum of squares) at the step (only for a generalized linear regression model) 
FStat  Fstatistic that leads to the step 
PValue  pvalue of the Fstatistic 
The structure is empty unless you fit the model using stepwise regression.
Data Types: struct
VariableInfo
— Information about variablesThis property is readonly.
Information about variables contained in Variables
, specified as a
table with one row for each variable and the columns described in this table.
Column  Description 

Class  Variable class, specified as a cell array of character vectors, such
as 'double' and
'categorical' 
Range  Variable range, specified as a cell array of vectors

InModel  Indicator of which variables are in the fitted model, specified as a
logical vector. The value is true if the model
includes the variable. 
IsCategorical  Indicator of categorical variables, specified as a logical vector.
The value is true if the variable is
categorical. 
VariableInfo
also includes any variables that are not used to fit
the model as predictors or as the response.
Data Types: table
VariableNames
— Names of variablesThis property is readonly.
Names of variables, specified as a cell array of character vectors.
If the fit is based on a table or dataset, this property provides the names of the variables in the table or dataset.
If the fit is based on a predictor matrix and response vector,
VariableNames
contains the values specified by the
'VarNames'
namevalue pair argument of the fitting
method. The default value of 'VarNames'
is
{'x1','x2',...,'xn','y'}
.
VariableNames
also includes any variables that are not used to fit
the model as predictors or as the response.
Data Types: cell
Variables
— Input dataThis property is readonly.
Input data, specified as a table. Variables
contains both predictor
and response values. If the fit is based on a table or dataset array,
Variables
contains all the data from the table or dataset array.
Otherwise, Variables
is a table created from the input data matrix
X
and response the vector y
.
Variables
also includes any variables that are not used to fit the
model as predictors or as the response.
Data Types: table
addTerms  Add terms to generalized linear model 
compact  Compact generalized linear regression model 
fit  (Not Recommended) Create generalized linear regression model 
plotDiagnostics  Plot diagnostics of generalized linear regression model 
plotResiduals  Plot residuals of generalized linear regression model 
removeTerms  Remove terms from generalized linear model 
step  Improve generalized linear regression model by adding or removing terms 
stepwise  (Not Recommended) Create generalized linear regression model by stepwise regression 
coefCI  Confidence intervals of coefficient estimates of generalized linear model 
coefTest  Linear hypothesis test on generalized linear regression model coefficients 
devianceTest  Analysis of deviance 
disp  Display generalized linear regression model 
feval  Evaluate generalized linear regression model prediction 
plotSlice  Plot of slices through fitted generalized linear regression surface 
predict  Predict response of generalized linear regression model 
random  Simulate responses for generalized linear regression model 
Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).
Fit a logistic regression model of probability of smoking as a function of age, weight, and sex, using a twoway interactions model.
Load the hospital
dataset array.
load hospital ds = hospital; % just to use the ds name
Specify the model using a formula that allows up to twoway interactions.
modelspec = 'Smoker ~ Age*Weight*Sex  Age:Weight:Sex';
Create the generalized linear model.
mdl = fitglm(ds,modelspec,'Distribution','binomial')
mdl = Generalized linear regression model: logit(Smoker) ~ 1 + Sex*Age + Sex*Weight + Age*Weight Distribution = Binomial Estimated Coefficients: Estimate SE tStat pValue ___________ _________ ________ _______ (Intercept) 6.0492 19.749 0.3063 0.75938 Sex_Male 2.2859 12.424 0.18399 0.85402 Age 0.11691 0.50977 0.22934 0.81861 Weight 0.031109 0.15208 0.20455 0.83792 Sex_Male:Age 0.020734 0.20681 0.10025 0.92014 Sex_Male:Weight 0.01216 0.053168 0.22871 0.8191 Age:Weight 0.00071959 0.0038964 0.18468 0.85348 100 observations, 93 error degrees of freedom Dispersion: 1 Chi^2statistic vs. constant model: 5.07, pvalue = 0.535
The large $$p$$value indicates the model might not differ statistically from a constant.
Create response data using just three of 20 predictors, and create a generalized linear model stepwise to see if it uses just the correct predictors.
Create data with 20 predictors, and Poisson response using just three of the predictors, plus a constant.
rng default % for reproducibility X = randn(100,20); mu = exp(X(:,[5 10 15])*[.4;.2;.3] + 1); y = poissrnd(mu);
Fit a generalized linear model using the Poisson distribution.
mdl = stepwiseglm(X,y,... 'constant','upper','linear','Distribution','poisson')
1. Adding x5, Deviance = 134.439, Chi2Stat = 52.24814, PValue = 4.891229e13 2. Adding x15, Deviance = 106.285, Chi2Stat = 28.15393, PValue = 1.1204e07 3. Adding x10, Deviance = 95.0207, Chi2Stat = 11.2644, PValue = 0.000790094
mdl = Generalized linear regression model: log(y) ~ 1 + x5 + x10 + x15 Distribution = Poisson Estimated Coefficients: Estimate SE tStat pValue ________ ________ ______ __________ (Intercept) 1.0115 0.064275 15.737 8.4217e56 x5 0.39508 0.066665 5.9263 3.0977e09 x10 0.18863 0.05534 3.4085 0.0006532 x15 0.29295 0.053269 5.4995 3.8089e08 100 observations, 96 error degrees of freedom Dispersion: 1 Chi^2statistic vs. constant model: 91.7, pvalue = 9.61e20
The default link function for a generalized linear model is the canonical link function.
Canonical Link Functions for Generalized Linear Models
Distribution  Link Function Name  Link Function  Mean (Inverse) Function 

'normal'  'identity'  f(μ) = μ  μ = Xb 
'binomial'  'logit'  f(μ) = log(μ/(1–μ))  μ = exp(Xb) / (1 + exp(Xb)) 
'poisson'  'log'  f(μ) = log(μ)  μ = exp(Xb) 
'gamma'  1  f(μ) = 1/μ  μ = 1/(Xb) 
'inverse gaussian'  2  f(μ) = 1/μ^{2}  μ = (Xb)^{–1/2} 
The hat matrix H is defined in terms of the data matrix X and a diagonal weight matrix W:
H = X(X^{T}WX)^{–1}X^{T}W^{T}.
W has diagonal elements w_{i}:
$${w}_{i}=\frac{{g}^{\prime}\left({\mu}_{i}\right)}{\sqrt{V\left({\mu}_{i}\right)}},$$
where
g is the link function mapping y_{i} to x_{i}b.
$${g}^{\prime}$$ is the derivative of the link function g.
V is the variance function.
μ_{i} is the ith mean.
The diagonal elements H_{ii} satisfy
$$\begin{array}{l}0\le {h}_{ii}\le 1\\ {\displaystyle \sum _{i=1}^{n}{h}_{ii}}=p,\end{array}$$
where n is the number of observations (rows of X), and p is the number of coefficients in the regression model.
Leverage is a measure of the effect of a particular observation on the regression predictions due to the position of that observation in the space of the inputs.
The leverage of observation i is the value of the ith diagonal term h_{ii} of the hat matrix H. The hat matrix H is defined in terms of the data matrix X:
H = X(X^{T}X)^{–1}X^{T}.
The hat matrix is also known as the projection matrix because it projects the vector of observations y onto the vector of predictions $$\widehat{y}$$, thus putting the "hat" on y.
Because the sum of the leverage values is p (the number of coefficients in the regression model), an observation i can be considered an outlier if its leverage substantially exceeds p/n, where n is the number of observations.
For more details, see Hat Matrix and Leverage.
The Cook’s distance D_{i} of observation i is
$${D}_{i}={w}_{i}\frac{{e}_{i}^{2}}{p\widehat{\phi}}\frac{{h}_{ii}}{{\left(1{h}_{ii}\right)}^{2}},$$
where
$$\widehat{\phi}$$ is the dispersion parameter (estimated or theoretical).
e_{i} is the linear predictor residual, $$g\left({y}_{i}\right){x}_{i}\widehat{\beta}$$, where
g is the link function.
y_{i} is the observed response.
x_{i} is the observation.
$$\widehat{\beta}$$ is the estimated coefficient vector.
p is the number of coefficients in the regression model.
h_{ii} is the ith diagonal element of the Hat Matrix H.
Deviance of a model M_{1} is twice the difference between the loglikelihood of that model and the saturated model, M_{S}. The saturated model is the model with the maximum number of parameters that can be estimated. For example, if there are n observations y_{i}, i = 1, 2, ..., n, with potentially different values for X_{i}^{T}β, then you can define a saturated model with n parameters. Let L(b,y) denote the maximum value of the likelihood function for a model. Then the deviance of model M_{1} is
$$2\left(\mathrm{log}L\left({b}_{1},y\right)\mathrm{log}L\left({b}_{S},y\right)\right),$$
where b_{1} are the estimated parameters for model M_{1} and b_{S} are the estimated parameters for the saturated model. The deviance has a chisquare distribution with n – p degrees of freedom, where n is the number of parameters in the saturated model and p is the number of parameters in model M_{1}.
If M_{1} and M_{2} are two different generalized linear models, then the fit of the models can be assessed by comparing the deviances D_{1} and D_{2} of these models. The difference of the deviances is
$$\begin{array}{l}D={D}_{2}{D}_{1}=2\left(\mathrm{log}L\left({b}_{2},y\right)\mathrm{log}L\left({b}_{S},y\right)\right)+2\left(\mathrm{log}L\left({b}_{1},y\right)\mathrm{log}L\left({b}_{S},y\right)\right)\\ \text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{1em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}=2\left(\mathrm{log}L\left({b}_{2},y\right)\mathrm{log}L\left({b}_{1},y\right)\right).\end{array}$$
Asymptotically, this difference has a chisquare distribution with degrees of freedom
v equal to the number of parameters that are estimated in one model
but fixed (typically at 0) in the other. It is equal to the difference in the number of
parameters estimated in M_{1} and M_{2}. You can get
the pvalue for this test using
1  chi2cdf(D,V)
, where D =
D_{2} –
D_{1}.
A terms matrix T
is a
tby(p + 1) matrix specifying terms in a model,
where t is the number of terms, p is the number of
predictor variables, and +1 accounts for the response variable. The value of
T(i,j)
is the exponent of variable j
in term
i
.
For example, suppose that an input includes three predictor variables A
,
B
, and C
and the response variable
Y
in the order A
, B
,
C
, and Y
. Each row of T
represents one term:
[0 0 0 0]
— Constant term or intercept
[0 1 0 0]
— B
; equivalently,
A^0 * B^1 * C^0
[1 0 1 0]
— A*C
[2 0 0 0]
— A^2
[0 1 2 0]
— B*(C^2)
The 0
at the end of each term represents the response variable. In
general, a column vector of zeros in a terms matrix represents the position of the response
variable. If you have the predictor and response variables in a matrix and column vector,
then you must include 0
for the response variable in the last column of
each row.
Usage notes and limitations:
When you fit a model by using fitglm
or stepwiseglm
, the following restrictions apply.
Code generation does not support categorical predictors. You cannot
supply training data in a table that contains a logical vector,
character array, categorical array, string array, or cell array of
character vectors. Also, you cannot use the 'CategoricalVars'
namevalue pair argument. To include categorical predictors
in a model, preprocess the categorical predictors by using dummyvar
before
fitting the model.
The Link
, Derivative
, and
Inverse
fields of the 'Link'
namevalue pair argument cannot be anonymous functions. That is, you
cannot generate code using a generalized linear model that was created
using anonymous functions for links. Instead, define functions for link
components.
For more information, see Introduction to Code Generation.
LinearModel
 NonLinearModel
 fitglm
 plotPartialDependence
 stepwiseglm
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