fitglm

Create generalized linear regression model

Syntax

mdl = fitglm(tbl)

mdl = fitglm(tbl,ResponseVarName)

mdl = fitglm(tbl,y)

mdl = fitglm(X,y)

mdl = fitglm(___,modelspec)

mdl = fitglm(___,Name,Value)

Description

mdl = fitglm(tbl) returns a generalized linear regression model fit to the input data. For variables in the input table tbl, fitglm treats the last variable as the response.

mdl = fitglm(tbl,ResponseVarName) specifies which table variable contains the response data.

mdl = fitglm(tbl,y) uses the variables in tbl for the predictors and y for the response.

mdl = fitglm(X,y) returns a generalized linear regression model of the responses y, fit to the data matrix X.

mdl = fitglm(___,modelspec) returns a generalized linear regression model of the type you specify in modelspec.

mdl = fitglm(___,Name,Value) returns a generalized linear regression model with additional options specified by one or more 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.

Examples

Fit a Logistic Regression Model

Make a logistic binomial model of the probability of smoking as a function of age, weight, and gender, using a two-way interactions model.

Load the patients sample data and save the variables Age, Weight, Gender, and Smoker in a table.

load patients
tbl = table(Age,Weight,Gender,Smoker,VariableNames=["Age","Weight","Gender","Smoker"]);

Specify the model using a formula that allows up to two-way interactions between the variables age, weight, and sex. Smoker is the response variable.

modelspec = "Smoker ~ Age*Weight*Gender - Age:Weight:Gender";

Fit a logistic binomial model.

mdl = fitglm(tbl,modelspec,Distribution="binomial")

mdl = 
Generalized linear regression model:
    logit(Smoker) ~ 1 + Age*Weight + Age*Gender + Weight*Gender
    Distribution = Binomial

Estimated Coefficients:
                             Estimate         SE         tStat      pValue 
                            ___________    _________    ________    _______

    (Intercept)                 -8.3351        29.02    -0.28722    0.77394
    Age                         0.13764      0.70593     0.19498    0.84541
    Weight                     0.043269      0.15966     0.27101    0.78638
    Gender_Female                2.2859       12.424     0.18399    0.85402
    Age:Weight              -0.00071959    0.0038964    -0.18468    0.85348
    Age:Gender_Female         -0.020734      0.20681    -0.10025    0.92014
    Weight:Gender_Female       -0.01216     0.053168    -0.22871     0.8191


100 observations, 93 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 5.07, p-value = 0.535

All of the p-values (under pValue) are large. This means none of the coefficients are significant. The large $$p$$-value for the test of the model, 0.535, indicates that this model might not differ statistically from a constant model.

GLM for Poisson Response

Create sample data with 20 predictors, and Poisson response using just three of the predictors, plus a constant.

rng('default') % for reproducibility
X = randn(100,7);
mu = exp(X(:,[1 3 6])*[.4;.2;.3] + 1);
y = poissrnd(mu);

Fit a generalized linear model using the Poisson distribution.

mdl =  fitglm(X,y,'linear','Distribution','poisson')

mdl = 
Generalized linear regression model:
    log(y) ~ 1 + x1 + x2 + x3 + x4 + x5 + x6 + x7
    Distribution = Poisson

Estimated Coefficients:
                   Estimate        SE        tStat        pValue  
                   _________    ________    ________    __________

    (Intercept)      0.88723    0.070969      12.502    7.3149e-36
    x1               0.44413    0.052337      8.4858    2.1416e-17
    x2             0.0083388    0.056527     0.14752       0.88272
    x3               0.21518    0.063416      3.3932    0.00069087
    x4             -0.058386    0.065503    -0.89135       0.37274
    x5             -0.060824    0.073441     -0.8282       0.40756
    x6               0.34267    0.056778      6.0352    1.5878e-09
    x7               0.04316     0.06146     0.70225       0.48252


100 observations, 92 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 119, p-value = 1.55e-22

The p-values of 2.14e-17, 0.00069, and 1.58e-09 indicate that the coefficients of the variables x1, x3, and x6 are statistically significant.

Input Arguments

tbl — Input data

table

Input data including predictor and response variables, specified as a table. The predictor variables can be numeric, logical, categorical, character, or string. If Distribution is "binomial", the response variable can be numeric, logical, character, or string. Otherwise, the response variable must be numeric.

• By default, fitglm takes the last variable as the response variable and the others as the predictor variables.

• To set a different column as the response variable, use the ResponseVar name-value pair argument.

• To use a subset of the columns as predictors, use the PredictorVars name-value pair argument.

• To define a model specification, set the modelspec argument using a formula or terms matrix. The formula or terms matrix specifies which columns to use as the predictor or response variables.

The variable names in a table do not have to be valid MATLAB^® identifiers, but the names must not contain leading or trailing blanks. If the names are not valid, you cannot use a formula when you fit or adjust a model; for example:

• You cannot specify modelspec using a formula.

• You cannot use a formula to specify the terms to add or remove when you use the addTerms function or the removeTerms function, respectively.

• You cannot use a formula to specify the lower and upper bounds of the model when you use the step or stepwiseglm function with the name-value pair arguments 'Lower' and 'Upper', respectively.

You can verify the variable names in tbl by using the isvarname function. If the variable names are not valid, then you can convert them by using the matlab.lang.makeValidName function.

ResponseVarName — Name of variable to use as response

string scalar | character vector

Name of the variable to use as the response, specified as a string scalar or character vector. ResponseVarName indicates which variable in tbl contains the response data. When you specify ResponseVarName, you must also specify the tbl input argument.

Data Types: char | string

X — Predictor variables

matrix

Predictor variables, specified as an n-by-p 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, the model includes a constant term unless you explicitly remove it, so do not include a column of 1s in X.

Data Types: single | double

y — Response variable

vector | matrix

Response variable, specified as a vector or matrix.

• If 'Distribution' is not 'binomial', then y must be an n-by-1 vector, where n is the number of observations. Each entry in y is the response for the corresponding row of X or tbl. The data type must be single or double.

• If 'Distribution' is 'binomial', then y can be an n-by-1 vector or n-by-2 matrix with counts in column 1 and BinomialSize in column 2.

Data Types: single | double | logical | categorical

modelspec — Model specification

'linear' (default) | character vector or string scalar naming the model | t-by-(p + 1) terms matrix | character vector or string scalar formula in the form 'y ~ terms'

Model specification, specified as one of these values.

• A character vector or string scalar containing the model name.

  Value	Model Description
  "constant"	Model contains only a constant (intercept) term
  "linear"	Model contains an intercept and linear term for each predictor
  "interactions"	Model contains an intercept, linear term for each predictor, and all products of pairs of distinct predictors (no squared terms)
  "purequadratic"	Model contains an intercept term and linear and squared terms for each predictor
  "quadratic"	Model contains an intercept term, linear and squared terms for each predictor, and all products of pairs of distinct predictors
  "polyijk"	Model is a polynomial with all terms up to degree i in the first predictor, degree j in the second predictor, and so on. Specify the maximum degree for each predictor by using numerals 0 through 9. The model contains interaction terms, but the degree of each interaction term does not exceed the maximum value of the specified degrees. For example, "poly13" has an intercept and x1, x2, x22, x23, x1*x2, and x1*x22 terms, where x1 and x2 are the first and second predictors, respectively.

• A t-by-(p + 1) terms matrix that specifies the terms in the model, where t is the number of terms, p is the number of predictor variables, and +1 accounts for the response variable. A terms matrix is convenient when the number of predictors is large and you want to generate the terms programmatically. For more information, see Terms Matrix.

• A character vector or string scalar formula in the form

  "y ~ terms",

  where the terms are in Wilkinson Notation. The variable names in the formula must be variable names in tbl or variable names specified by VarNames. Also, the variable names must be valid MATLAB identifiers.

  The software determines the order of terms in a fitted model by using the order of terms in tbl or X. Therefore, the order of terms in the model can be different from the order of terms in the specified formula. For more information, see Formula.

When you specify modelspec, you cannot use the PredictorVars name-value argument to specify the predictor variables.

Example: 'quadratic'

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'Distribution','normal','link','probit','Exclude',[23,59] specifies that the distribution of the response is normal, and instructs fitglm to use the probit link function and exclude the 23rd and 59th observations from the fit.

BinomialSize — Number of trials for binomial distribution

1 (default) | numeric scalar | numeric vector | character vector | string scalar

Number of trials for binomial distribution, that is the sample size, specified as the comma-separated pair consisting of 'BinomialSize' and the variable name in tbl, a numeric scalar, or a numeric vector of the same length as the response. This is the parameter n for the fitted binomial distribution. BinomialSize applies only when the Distribution parameter is 'binomial'.

If BinomialSize is a scalar value, that means all observations have the same number of trials.

As an alternative to BinomialSize, you can specify the response as a two-column matrix with counts in column 1 and BinomialSize in column 2.

Data Types: single | double | char | string

B0 — Initial values for coefficient estimates

numeric vector

Initial values for the coefficient estimates, specified as a numeric vector. The default values are initial fitted values derived from the input data.

Data Types: single | double

CategoricalVars — Categorical predictor list

string array | cell array of character vectors | logical or numeric index vector

Categorical predictor list, specified as a string array or cell array of character vectors containing categorical predictor names in the table tbl, or a logical or numeric index vector indicating which predictor columns are categorical.

• If the predictor data is in a table tbl, then, by default, fitglm treats all categorical values, logical values, character arrays, string arrays, and cell arrays of character vectors as categorical predictors.

• If data is in a matrix X, then the default value of CategoricalVars is an empty matrix []. That is, no predictor is categorical unless you specify it as categorical.

For example, you can specify the second and third variables out of six as categorical using either of the following:

Example: 'CategoricalVars',[2,3]

Example: 'CategoricalVars',logical([0 1 1 0 0 0])

Data Types: single | double | logical | string | cell

DispersionFlag — Indicator to compute dispersion parameter

false for 'binomial' and 'poisson' distributions (default) | true

Indicator to compute dispersion parameter for 'binomial' and 'poisson' distributions, specified as the comma-separated pair consisting of 'DispersionFlag' and one of the following.

true	Estimate a dispersion parameter when computing standard errors. The estimated dispersion parameter value is the sum of squared Pearson residuals divided by the degrees of freedom for error (DFE).
false	Default. Use the theoretical value of 1 when computing standard errors.

The fitting function always estimates the dispersion for other distributions.

Example: 'DispersionFlag',true

Distribution — Distribution of the response variable

'normal' (default) | 'binomial' | 'poisson' | 'gamma' | 'inverse gaussian'

Distribution of the response variable, specified as the comma-separated pair consisting of 'Distribution' and one of the following.

'normal'	Normal distribution
'binomial'	Binomial distribution
'poisson'	Poisson distribution
'gamma'	Gamma distribution
'inverse gaussian'	Inverse Gaussian distribution

Example: 'Distribution','gamma'

Exclude — Observations to exclude

logical or numeric index vector

Observations to exclude from the fit, specified as a logical or numeric index vector indicating which observations to exclude from the fit.

For example, you can exclude observations 2 and 3 out of 6 using either of the following examples.

Example: Exclude=[2,3]

Example: Exclude=logical([0 1 1 0 0 0])

Data Types: single | double | logical

Intercept — Indicator for constant term

true (default) | false

Indicator for the constant term (intercept) in the fit, specified as true to include or 0 false to remove the constant term from the model.

Use Intercept only when specifying the model using a character vector or string scalar, not a formula or matrix.

Example: Intercept=false

LikelihoodPenalty — Penalty for likelihood estimate

"none" (default) | "jeffreys-prior"

Penalty for the likelihood estimate, specified as "none" or "jeffreys-prior".

• "none" — fitglm does not apply a penalty to the likelihood estimate.

• "jeffreys-prior" — fitglm uses Jeffreys prior to penalize the likelihood estimate.

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".

Example: LikelihoodPenalty="jeffreys-prior"

Data Types: char | string

Link — Link function

canonical link function (default) | scalar value | structure

Link function to use in place of the canonical link function, specified as the comma-separated pair consisting of 'Link' and one of the following.

Link Function Name	Link Function	Mean (Inverse) Function
'identity'	f(μ) = μ	μ = Xb
'log'	f(μ) = log(μ)	μ = exp(Xb)
'logit'	f(μ) = log(μ/(1–μ))	μ = exp(Xb) / (1 + exp(Xb))
'probit'	f(μ) = Φ–1(μ), where Φ is the cumulative distribution function of the standard normal distribution.	μ = Φ(Xb)
'comploglog'	f(μ) = log(–log(1 – μ))	μ = 1 – exp(–exp(Xb))
'reciprocal'	f(μ) = 1/μ	μ = 1/(Xb)
p (a number)	f(μ) = μp	μ = Xb1/p
S (a structure) with three fields. Each field holds a function handle that accepts a vector of inputs and returns a vector of the same size: S.Link — The link function S.Inverse — The inverse link function S.Derivative — The derivative of the link function	f(μ) = S.Link(μ)	μ = S.Inverse(Xb)

The link function defines the relationship f(μ) = X*b between the mean response μ and the linear combination of predictors X*b.

For more information on the canonical link functions, see Canonical Link Function.

Example: 'Link','probit'

Data Types: char | string | single | double | struct

Options — Optimization options

statset("fitglm") (default) | structure

Optimization options, specified as a structure. This argument determines the control parameters for the iterative algorithm used by fitglm.

Create optimization options by using the statset function or by creating a structure array containing the following fields.

Field Name	Value	Default Value
MaxIter	Maximum number of iterations allowed, specified as a positive integer	100
TolX	Termination tolerance for the parameters, specified as a positive scalar	1e-6

You can also enter statset("fitglm") in the Command Window to display the default values for the MaxIter and TolX fields.

Example: Options=statset(MaxIter=1000,TolX=1e-7) specifies to use a maximum of 1000 iterations and a termination tolerance of 1e-7.

Data Types: struct

Offset — Offset variable

[ ] (default) | numeric vector | character vector | string scalar

Offset variable in the fit, specified as the comma-separated pair consisting of 'Offset' and the variable name in tbl or a numeric vector with the same length as the response.

fitglm uses Offset as an additional predictor with a coefficient value fixed at 1. In other words, the formula for fitting is

f(μ) = Offset + X*b,

where f is the link function, μ is the mean response, and X*b is the linear combination of predictors X. The Offset predictor has 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: single | double | char | string

PredictorVars — Predictor variables

string array | cell array of character vectors | logical or numeric index vector

Predictor variables to use in the fit, specified as a string array or cell array of character vectors of the variable names in the table tbl, or a logical or numeric index vector indicating which columns are predictor variables.

The string values or character vectors should be among the names in tbl, or the names you specify using the VarNames name-value pair argument.

The default is all variables in X, or all variables in tbl except for ResponseVar.

For example, you can specify the second and third variables as the predictor variables using either of the following examples.

When you specify PredictorVars, you cannot use the modelspec input argument to specify a terms matrix.

Example: PredictorVars=[2,3]

Example: PredictorVars=logical([0 1 1 0 0 0])

Data Types: single | double | logical | string | cell

ResponseVar — Response variable

last column in tbl (default) | character vector or string scalar containing variable name | logical or numeric index vector

Response variable to use in the fit, specified as a character vector or string scalar containing the variable name in the table tbl, or a logical or numeric index vector indicating which column is the response variable. You typically need to use ResponseVar when fitting a table tbl.

For example, you can specify the fourth variable, say yield, as the response out of six variables, in one of the following ways.

Example: ResponseVar="yield"

Example: ResponseVar=[4]

Example: ResponseVar=logical([0 0 0 1 0 0])

Data Types: single | double | logical | char | string

VarNames — Names of variables

{'x1','x2',...,'xn','y'} (default) | string array | cell array of character vectors

Names of variables, specified as the comma-separated pair consisting of 'VarNames' and a string array or cell array of character vectors including the names for the columns of X first, and the name for the response variable y last.

'VarNames' is not applicable to variables in a table or dataset array, because those variables already have names.

The variable names do not have to be valid MATLAB identifiers, but the names must not contain leading or trailing blanks. If the names are not valid, you cannot use a formula when you fit or adjust a model; for example:

• You cannot use a formula to specify the terms to add or remove when you use the addTerms function or the removeTerms function, respectively.

• You cannot use a formula to specify the lower and upper bounds of the model when you use the step or stepwiseglm function with the name-value pair arguments 'Lower' and 'Upper', respectively.

Before specifying 'VarNames',varNames, you can verify the variable names in varNames by using the isvarname function. If the variable names are not valid, then you can convert them by using the matlab.lang.makeValidName function.

Example: 'VarNames',{'Horsepower','Acceleration','Model_Year','MPG'}

Data Types: string | cell

Weights — Observation weights

ones(n,1) (default) | n-by-1 vector of nonnegative scalar values

Observation weights, specified as an n-by-1 vector of nonnegative scalar values, where n is the number of observations.

Data Types: single | double

Output Arguments

mdl — Generalized linear regression model

GeneralizedLinearModel object

Generalized linear regression model, specified as a GeneralizedLinearModel object created using fitglm or stepwiseglm.

More About

Terms Matrix

A terms matrix T is a t-by-(p + 1) matrix that specifies the 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, x1, x2, and x3, and the response variable y in the order x1, x2, x3, and y. Each row of T represents one term:

• [0 0 0 0] — Constant term (intercept)

• [0 1 0 0] — x2; equivalently, x1^0 * x2^1 * x3^0

• [1 0 1 0] — x1*x3

• [2 0 0 0] — x1^2

• [0 1 2 0] — x2*(x3^2)

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 the predictor and response variables are in a matrix and column vector, respectively, then you must include 0 for the response variable in the last column of each row.

Formula

A formula for model specification is a character vector or string scalar of the form 'y ~ terms'.

• y is the response name.

• terms represents the predictor terms in a model using Wilkinson notation.

To represent predictor and response variables, use the variable names of the input argument tbl or the variable names specified by using the VarNames name-value argument.

For example, if VarNames is ["x1","x2",...,"xn","y"].:

• "y ~ x1 + x2 + x3" specifies a three-variable linear model with an intercept.

• "y ~ x1 + x2 + x3 – 1" specifies a three-variable linear model without an intercept. Note that formulas include a constant (intercept) term by default. To exclude a constant term from the model, you must include –1 in the formula.

Wilkinson Notation

Wilkinson notation describes the terms in a model. The notation relates to the terms included in the model, not to the multipliers (coefficients) of those terms.

Wilkinson notation uses these symbols:

• + means include the next variable.

• – means do not include the next variable.

• : defines an interaction, which is a product of the terms.

• * defines an interaction and all lower order terms.

• ^ raises the predictor to a power, exactly as in * repeated, so ^ includes lower order terms as well.

• () groups the terms.

This table shows typical examples of Wilkinson notation.

Wilkinson Notation	Terms in Standard Notation
1	Constant (intercept) term
x1^k, where k is a positive integer	x1, x12, ..., x1k
x1 + x2	x1, x2
x1*x2	x1, x2, x1*x2
x1:x2	x1*x2 only
–x2	Do not include x2
x1*x2 + x3	x1, x2, x3, x1*x2
x1 + x2 + x3 + x1:x2	x1, x2, x3, x1*x2
x1*x2*x3 – x1:x2:x3	x1, x2, x3, x1*x2, x1*x3, x2*x3
x1*(x2 + x3)	x1, x2, x3, x1*x2, x1*x3

For more details, see Wilkinson Notation.

Canonical Link Function

The default link function for a generalized linear model is the canonical link function.

Distribution	Canonical 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/μ2	μ = (Xb)–1/2

Tips

• 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.

Algorithms

• fitglm calculates the model coefficients using iteratively reweighted least squares (IRLS). If you specify observation weights using the Weights name-value argument, fitglm multiplies the weights in the IRLS algorithm by the observation weights.

• 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.

Alternative Functionality

• Use stepwiseglm to select a model specification automatically. Use step, addTerms, or removeTerms to adjust a fitted model.

References

[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.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

This function supports tall arrays for out-of-memory data with some limitations.

• If any input argument to fitglm is a tall array, then all of the other inputs must be tall arrays as well. This includes nonempty variables supplied with the 'Weights', 'Exclude', 'Offset', and 'BinomialSize' name-value pairs.

• You cannot specify the LikelihoodPenalty name-value argument.

• The default number of iterations is 5. You can change the number of iterations using the 'Options' name-value pair to pass in an options structure. Create an options structure using statset to specify a different value for MaxIter.

• For tall data, fitglm returns a CompactGeneralizedLinearModel object that contains most of the same properties as a GeneralizedLinearModel object. The main difference is that the compact object is sensitive to memory requirements. The compact object does not include properties that include the data, or that include an array of the same size as the data. The compact object does not contain these GeneralizedLinearModel properties:

  • Diagnostics

  • Fitted

  • Offset

  • ObservationInfo

  • ObservationNames

  • Residuals

  • Steps

  • Variables

  You can compute the residuals directly from the compact object returned by GLM = fitglm(X,Y) using

  RES = Y - predict(GLM,X);
  S = sqrt(GLM.SSE/GLM.DFE);
  histogram(RES,linspace(-3*S,3*S,51))
  

For more information, see Tall Arrays for Out-of-Memory Data.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2013b

R2024a: Specify penalized likelihood estimation

fitglm now supports penalized likelihood estimation by Jeffreys prior. You can use Jeffreys prior to reduce the coefficient estimate bias when fitting a generalized linear regression model to a separable data set or small number of samples. To penalize the likelihood estimate, set the LikelihoodPenalty name-value argument to "jeffreys-prior".