SL.glm

Wrapper for generalized linear models via glm().
Note that for outcomes bounded by [0, 1] the binomial family can be used in
addition to gaussian.

Implements the super learner prediction method and contains a
library of prediction algorithms to be used in the super learner.

Eric Polley

SuperLearner

Super Learner Prediction

Erin LeDell

Chris Kennedy

Sam Lendle

Mark van der Laan

SL.glm function

<dl><dt>Y</dt>
<dd>Outcome variable</dd>
<dt>X</dt>
<dd>Training dataframe</dd>
<dt>newX</dt>
<dd>Test dataframe</dd>
<dt>family</dt>
<dd>Gaussian or binomial</dd>
<dt>obsWeights</dt>
<dd>Observation-level weights</dd>
<dt>model</dt>
<dd>Whether to save model.matrix of data in fit object. Set to FALSE
to save memory.</dd>
<dt>...</dt>
<dd>Any remaining arguments</dd></dl>

Arguments

Wrapper for glm — SL.glm

<dl>

<dt>Y</dt>
<dd>Outcome variable</dd>


<dt>X</dt>
<dd>Training dataframe</dd>


<dt>newX</dt>
<dd>Test dataframe</dd>


<dt>family</dt>
<dd>Gaussian or binomial</dd>


<dt>obsWeights</dt>
<dd>Observation-level weights</dd>


<dt>model</dt>
<dd>Whether to save model.matrix of data in fit object. Set to FALSE
to save memory.</dd>


<dt>...</dt>
<dd>Any remaining arguments</dd>

</dl>

SL.glm: Wrapper for glm

Description

Usage

Arguments

See Also