predict.gpe

of class <code><a rd-options="" href="/link/gpe?package=pre&version=1.0.3" data-mini-rdoc="pre::gpe">gpe</a></code>

object

optional new data to compute predictions for

newdata

argument passed to <code><a rd-options="glmnet" href="/link/predict.cv.glmnet?package=pre&version=1.0.3&to=glmnet" data-mini-rdoc="glmnet::predict.cv.glmnet">predict.cv.glmnet</a></code>

type

argument passed to <code>s</code> argument of <code><a rd-options="glmnet" href="/link/predict.cv.glmnet?package=pre&version=1.0.3&to=glmnet" data-mini-rdoc="glmnet::predict.cv.glmnet">predict.cv.glmnet</a></code>

penalty.par.val

Predict function for <code><a rd-options="" href="/link/gpe?package=pre&version=1.0.3" data-mini-rdoc="pre::gpe">gpe</a></code>

Derives prediction rule ensembles (PREs). Largely follows the
procedure for deriving PREs as described in Friedman & Popescu (2008;
<DOI:10.1214/07-AOAS148>), with adjustments and improvements. The
main function pre() derives prediction rule ensembles consisting of
rules and/or linear terms for continuous, binary, count, multinomial,
and multivariate continuous responses. Function gpe() derives
generalized prediction ensembles, consisting of rules, hinge and linear
functions of the predictor variables.

Marjolein Fokkema

Prediction Rule Ensembles

Benjamin Christoffersen

predict.gpe function

of class <code><a rd-options='' href='gpe'>gpe</a></code>

argument passed to <code><a rd-options='glmnet' href='predict.cv.glmnet'>predict.cv.glmnet</a></code>

argument passed to <code>s</code> argument of <code><a rd-options='glmnet' href='predict.cv.glmnet'>predict.cv.glmnet</a></code>

Predict function for <code><a rd-options='' href='gpe'>gpe</a></code>

predict.gpe: Predicted values based on gpe ensemble

Description

Usage

Arguments

Details

See Also