CompactGeneralizedLinearModel
Compact generalized linear regression model class
Description
CompactGeneralizedLinearModel
is a compact version of a full
generalized linear regression model object GeneralizedLinearModel
. Because a compact model does not store the input
data used to fit the model or information related to the fitting process, a
CompactGeneralizedLinearModel
object consumes less memory than a
GeneralizedLinearModel
object. You can still use a compact model to
predict responses using new input data, but some GeneralizedLinearModel
object functions do not work with a compact model.
Creation
Create a CompactGeneralizedLinearModel
model from a full, trained
GeneralizedLinearModel
model by using compact
.
fitglm
returns CompactGeneralizedLinearModel
when you work with tall arrays, and returns GeneralizedLinearModel
when
you work with in-memory tables and arrays.
Properties
Coefficient Estimates
CoefficientCovariance
— Covariance matrix of coefficient estimates
numeric matrix
This property is read-only.
Covariance matrix of coefficient estimates, specified as a
p-by-p matrix of numeric values. p
is the number of coefficients in the fitted model, as given by
NumCoefficients
.
For details, see Coefficient Standard Errors and Confidence Intervals.
Data Types: single
| double
CoefficientNames
— Coefficient names
cell array of character vectors
This property is read-only.
Coefficient names, specified as a cell array of character vectors, each containing the name of the corresponding term.
Data Types: cell
Coefficients
— Coefficient values
table
This property is read-only.
Coefficient values, specified as a table.
Coefficients
contains one row for each coefficient and these
columns:
Estimate
— Estimated coefficient valueSE
— Standard error of the estimatetStat
— t-statistic for a two-sided test with the null hypothesis that the coefficient is zeropValue
— p-value for the t-statistic
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
NumCoefficients
— Number of model coefficients
positive integer
This property is read-only.
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 coefficients
positive integer
This property is read-only.
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
Summary Statistics
Deviance
— Deviance of fit
numeric value
This property is read-only.
Deviance of the fit, specified as a numeric value. The 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 chi-square distribution with degrees of freedom equal to the difference in the number of estimated parameters between the two models. For more information, see Deviance.
Data Types: single
| double
DFE
— Degrees of freedom for error
positive integer
This property is read-only.
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
Dispersion
— Scale factor of variance of response
numeric scalar
This property is read-only.
Scale factor of the variance of the response, specified as a numeric scalar.
If the 'DispersionFlag'
name-value pair argument of
fitglm
or stepwiseglm
is
true
, then the function estimates the
Dispersion
scale factor in computing the variance of the
response. The variance of the response equals the theoretical variance multiplied by the
scale factor.
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 factor
logical value
This property is read-only.
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 befalse
only for the binomial and Poisson distributions.Set
DispersionEstimated
by setting the'DispersionFlag'
name-value pair argument offitglm
orstepwiseglm
.
Data Types: logical
LikelihoodPenalty
— Penalty for likelihood estimate
"none"
(default) | "jeffreys-prior"
Penalty for the likelihood estimate, specified as "none"
or
"jeffreys-prior"
.
"none"
— The likelihood estimate is not penalized during model fitting."jeffreys-prior"
— The likelihood estimate is penalized using the Jeffreys prior.
For logistic models, setting LikelihoodPenalty
to
"jeffreys-prior"
is called Firth's
regression. To reduce the coefficient estimate bias when you have a small
number of samples, or when you are performing binomial (logistic) regression on a
separable data set, set LikelihoodPenalty
to
"jeffreys-prior"
during training.
Example: LikelihoodPenalty="jeffreys-prior"
Data Types: char
| string
LogLikelihood
— Loglikelihood
numeric value
This property is read-only.
Loglikelihood 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 comparison
structure
This property is read-only.
Criterion for model comparison, specified as a structure with these fields:
AIC
— Akaike information criterion.AIC = –2*logL + 2*m
, wherelogL
is the loglikelihood andm
is the number of estimated parameters.AICc
— Akaike information criterion corrected for the sample size.AICc = AIC + (2*m*(m + 1))/(n – m – 1)
, wheren
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 likelihood-based 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 best-fitting model. The best-fitting 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
Rsquared
— R-squared value for model
structure
This property is read-only.
R-squared value for the model, specified as a structure with five fields.
Field | Description | Equation |
---|---|---|
Ordinary | Ordinary (unadjusted) R-squared |
|
Adjusted | R-squared adjusted for the number of coefficients |
N is the number of
observations ( |
LLR | Loglikelihood ratio |
L is the loglikelihood of
the fitted model ( |
Deviance | Deviance R-squared |
D is the deviance of the
fitted model ( |
AdjGeneralized | Adjusted generalized R-squared |
R2AdjGeneralized is the Nagelkerke adjustment [2] to a formula proposed by Maddala [3], Cox and Snell [4], and Magee [5] for logistic regression models. |
To obtain any of these values as a scalar, index into the property using dot notation.
For example, to obtain the adjusted R-squared value in the model mdl
,
enter:
r2 = mdl.Rsquared.Adjusted
Data Types: struct
SSE
— Sum of squared errors
numeric value
This property is read-only.
Sum of squared errors (residuals), specified as a numeric value. If the model was
trained with observation weights, the sum of squares in the SSE
calculation is the weighted sum of squares.
Data Types: single
| double
SSR
— Regression sum of squares
numeric value
This property is read-only.
Regression sum of squares, specified as a numeric value.
SSR
is equal to the sum of the
squared deviations between the fitted values and the mean of the
response. If the model was trained with observation weights, the
sum of squares in the SSR
calculation is
the weighted sum of squares.
Data Types: single
| double
SST
— Total sum of squares
numeric value
This property is read-only.
Total sum of squares, specified as a numeric value. SST
is equal
to the sum of squared deviations of the response vector y
from the
mean(y)
. If the model was trained with observation weights, the
sum of squares in the SST
calculation is the weighted sum of
squares.
Data Types: single
| double
Input Data
Distribution
— Generalized distribution information
structure
This property is read-only.
Generalized distribution information, specified as a structure with the fields described in this table.
Field | Description |
---|---|
Name | Name of the distribution: '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 , the software multiplies the variance
function by Dispersion in the computation of the
coefficient standard errors. |
Data Types: struct
Formula
— Model information
LinearFormula
object
This property is read-only.
Model information, specified as a LinearFormula
object.
Display the formula of the fitted model mdl
using dot
notation:
mdl.Formula
Link
— Link function
structure
This property is read-only.
Link function, specified as a structure with the fields described in this table.
Field | Description |
---|---|
Name | Name of the link function, specified as a character vector. If you specify the link function
using a function handle, then Name is
'' . |
Link | Function f that defines the link function, specified as a function handle |
Derivative | Derivative of f, specified as a function handle |
Inverse | Inverse of f, specified as a function handle |
The link function 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
NumObservations
— Number of observations
positive integer
This property is read-only.
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'
name-value pair
argument) or rows with missing values.
Data Types: double
NumPredictors
— Number of predictor variables
positive integer
This property is read-only.
Number of predictor variables used to fit the model, specified as a positive integer.
Data Types: double
NumVariables
— Number of variables
positive integer
This property is read-only.
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
PredictorNames
— Names of predictors used to fit model
cell array of character vectors
This property is read-only.
Names of predictors used to fit the model, specified as a cell array of character vectors.
Data Types: cell
ResponseName
— Response variable name
character vector
This property is read-only.
Response variable name, specified as a character vector.
Data Types: char
VariableInfo
— Information about variables
table
This property is read-only.
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 variables
cell array of character vectors
This property is read-only.
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'
name-value 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
Object Functions
Predict Responses
Evaluate Generalized Linear Model
coefCI | Confidence intervals of coefficient estimates of generalized linear regression model |
coefTest | Linear hypothesis test on generalized linear regression model coefficients |
devianceTest | Analysis of deviance for generalized linear regression model |
partialDependence | Compute partial dependence |
Visualize Generalized Linear Model and Summary Statistics
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
plotSlice | Plot of slices through fitted generalized linear regression surface |
Gather Properties of Generalized Linear Model
gather | Gather properties of Statistics and Machine Learning Toolbox object from GPU |
Examples
Compact Generalized Linear Regression Model
Fit a generalized linear regression model to data and reduce the size of a full, fitted model by discarding the sample data and some information related to the fitting process.
Load the largedata4reg
data set, which contains 15,000 observations and 45 predictor variables.
load largedata4reg
Fit a generalized linear regression model to the data using the first 15 predictor variables.
mdl = fitglm(X(:,1:15),Y);
Compact the model.
compactMdl = compact(mdl);
The compact model discards the original sample data and some information related to the fitting process, so it uses less memory than the full model.
Compare the size of the full model mdl
and the compact model compactMdl
.
vars = whos('compactMdl','mdl'); [vars(1).bytes,vars(2).bytes]
ans = 1×2
17060 4384077
The compact model consumes less memory than the full model.
More About
Deviance
Deviance is a generalization of the residual sum of squares. It measures the goodness of fit compared to a saturated model.
The deviance of a model M1 is twice the difference between the loglikelihood of the model M1 and the saturated model Ms. A saturated model is a model with the maximum number of parameters that you can estimate.
For example, if you have n observations (yi, i = 1, 2, ..., n) with potentially different values for XiTβ, 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 with the parameters b. Then the deviance of the model M1 is
where b1 and bs contain the estimated parameters for the model M1 and the saturated model, respectively. The deviance has a chi-squared 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 the model M1.
Assume you have two different generalized linear regression models M1 and M2, and M1 has a subset of the terms in M2. You can assess the fit of the models by comparing their deviances D1 and D2. The difference of the deviances is
Asymptotically, the difference D has a chi-squared distribution with
degrees of freedom v equal to the difference in the number of parameters
estimated in M1 and
M2. You can obtain the
p-value for this test by using 1 —
chi2cdf(D,v)
.
Typically, you examine D using a model M2 with a constant term and no predictors. Therefore, D has a chi-squared distribution with p – 1 degrees of freedom. If the dispersion is estimated, the difference divided by the estimated dispersion has an F distribution with p – 1 numerator degrees of freedom and n – p denominator degrees of freedom.
References
[1] McFadden, Daniel. "Conditional logit analysis of qualitative choice behavior." in Frontiers in Econometrics, edited by P. Zarembka,105–42. New York: Academic Press, 1974.
[2] Nagelkerke, N. J. D. "A Note on a General Definition of the Coefficient of Determination." Biometrika 78, no. 3 (1991): 691–92.
[3] Maddala, Gangadharrao S. Limited-Dependent and Qualitative Variables in Econometrics. Econometric Society Monographs. New York, NY: Cambridge University Press, 1983.
[4] Cox, D. R., and E. J. Snell. Analysis of Binary Data. 2nd ed. Monographs on Statistics and Applied Probability 32. London; New York: Chapman and Hall, 1989.
[5] Magee, Lonnie. "R 2 Measures Based on Wald and Likelihood Ratio Joint Significance Tests." The American Statistician 44, no. 3 (August 1990): 250–53.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
When you fit a model by using
fitglm
orstepwiseglm
, you cannot specifyLink
,Derivative
, andInverse
fields of the'Link'
name-value pair argument as 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.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
The object functions of the
CompactGeneralizedLinearModel
model fully support GPU arrays.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2016b
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