compact
Compact generalized linear regression model
Description
returns the compact generalized linear regression model
compactMdl
= compact(mdl
)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
17060 4384077
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
See Also
CompactGeneralizedLinearModel
| GeneralizedLinearModel
| fitglm
| stepwiseglm
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