predict.boostrq

Model predictions for boosting regression quantiles

Boosting Regression Quantiles is a component-wise boosting algorithm,
that embeds all boosting steps in the well-established framework
of quantile regression. It is initialized with the corresponding quantile,
uses a quantile-specific learning rate, and uses quantile regression as its base learner.
The package implements this algorithm and allows cross-validation and stability selection.

Stefan Linner

boostrq

Boosting Regression Quantiles

predict.boostrq function

<dl><dt>object</dt>
<dd>a boostrq object</dd>
<dt>newdata</dt>
<dd>a data.frame (or data.table) including all covariates contained in the
baselearners</dd>
<dt>which</dt>
<dd>a subset of base-learners</dd>
<dt>aggregate</dt>
<dd>a character specifying how to aggregate coefficients of
single base learners. The default returns the coefficient for the final number
of boosting iterations. "cumsum" returns a list with matrices (one per base-learner)
with the cumulative coefficients for all iterations. "none" returns a list of
matrices where the jth columns of the respective matrix contains coefficients
of the base-learner of the jth boosting iteration.</dd>
<dt>...</dt>
<dd>additional arguments passed to callies</dd></dl>

Arguments

Model predictions for boosting regression quantiles — predict.boostrq

<dl>

<dt>object</dt>
<dd>a boostrq object</dd>


<dt>newdata</dt>
<dd>a data.frame (or data.table) including all covariates contained in the
baselearners</dd>


<dt>which</dt>
<dd>a subset of base-learners</dd>


<dt>aggregate</dt>
<dd>a character specifying how to aggregate coefficients of
single base learners. The default returns the coefficient for the final number
of boosting iterations. "cumsum" returns a list with matrices (one per base-learner)
with the cumulative coefficients for all iterations. "none" returns a list of
matrices where the jth columns of the respective matrix contains coefficients
of the base-learner of the jth boosting iteration.</dd>


<dt>...</dt>
<dd>additional arguments passed to callies</dd>

</dl>

predict.boostrq: Model predictions for boosting regression quantiles

Description

Usage

Value

Arguments

Examples