SL.speedglm

Speedglm is a fast version of glm()

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.speedglm 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>maxit</dt>
<dd>Maximum number of iterations before stopping.</dd>
<dt>k</dt>
<dd>numeric, the penalty per parameter to be used; the default k = 2 is
the classical AIC.</dd>
<dt>...</dt>
<dd>Any remaining arguments, not used.</dd></dl>

Arguments

Wrapper for speedglm — SL.speedglm

<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>maxit</dt>
<dd>Maximum number of iterations before stopping.</dd>


<dt>k</dt>
<dd>numeric, the penalty per parameter to be used; the default k = 2 is
the classical AIC.</dd>


<dt>...</dt>
<dd>Any remaining arguments, not used.</dd>

</dl>

SL.speedglm: Wrapper for speedglm

Description

Usage

Arguments

References

See Also