compact

Compact generalized linear regression model

Syntax

compactMdl = compact(mdl)

Description

compactMdl = compact(mdl) returns the compact generalized linear regression model compactMdl, which is the compact version of the full, fitted generalized linear regression model mdl.

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

       15684     4382667

The compact model consumes less memory than the full model.

Input Arguments

mdl — Generalized linear regression model

GeneralizedLinearModel object

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

Output Arguments

compactMdl — Compact generalized linear regression model

CompactGeneralizedLinearModel object

Compact generalized linear regression model, returned as a CompactGeneralizedLinearModel object.

A CompactGeneralizedLinearModel object consumes less memory than a GeneralizedLinearModel object because a compact model does not store the input data used to fit the model or information related to the fitting process. You can still use a compact model to predict responses using new input data, but some GeneralizedLinearModel object functions that require the input data do not work with a compact model.

Extended Capabilities

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 R2016b