predict.superlearner

Obtains predictions for ensemble model or individual learners.

Various methods for targeted and semiparametric inference including
augmented inverse probability weighted (AIPW) estimators for missing data and
causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
variable importance and conditional average treatment effects (CATE)
(van der Laan (2006) <doi:10.2202/1557-4679.1008>),
estimators for risk differences and relative risks (Richardson et al. (2017)
<doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Klaus Holst

targeted

Targeted Inference

Klaus K. Holst

Benedikt Sommer

Andreas Nordland

predict.superlearner function

<dl><dt>object</dt>
<dd>(superlearner) Fitted superlearner object.</dd>
<dt>newdata</dt>
<dd>(data.frame) Data in which to look for variables with which to
predict.</dd>
<dt>all.learners</dt>
<dd>(logical) If FALSE (default), then return the predictions
from the ensemble model. Otherwise, return predictions of from all individual
learners.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Predict Method for superlearner Fits — predict.superlearner

<dl>

<dt>object</dt>
<dd>(superlearner) Fitted superlearner object.</dd>


<dt>newdata</dt>
<dd>(data.frame) Data in which to look for variables with which to
predict.</dd>


<dt>all.learners</dt>
<dd>(logical) If FALSE (default), then return the predictions
from the ensemble model. Otherwise, return predictions of from all individual
learners.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

predict.superlearner: Predict Method for superlearner Fits

Description

Usage

Value

Arguments