predict.fast_logistic_regression_stepwise

Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the 'RcppNumerical' package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.

Adam Kapelner

fastLogisticRegressionWrap

Fast Logistic Regression Wrapper

Beau Walker

Gabriel Mayer

predict.fast_logistic_regression_stepwise function

<dl><dt>object</dt>
<dd>The object built using the <code>fast_logistic_regression</code> or <code>fast_logistic_regression_stepwise</code> wrapper functions</dd>
<dt>newdata</dt>
<dd>A matrix of observations where you wish to predict the binary response.</dd>
<dt>type</dt>
<dd>The type of prediction required. The default is <code>"response"</code> which is on the response scale (i.e. probability estimates) and the alternative is <code>"link"</code> which is the linear scale (i.e. log-odds).</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods</dd></dl>

Arguments

FastLR Wrapper Predictions — predict.fast_logistic_regression_stepwise

<dl>

<dt>object</dt>
<dd>The object built using the <code>fast_logistic_regression</code> or <code>fast_logistic_regression_stepwise</code> wrapper functions</dd>


<dt>newdata</dt>
<dd>A matrix of observations where you wish to predict the binary response.</dd>


<dt>type</dt>
<dd>The type of prediction required. The default is <code>"response"</code> which is on the response scale (i.e. probability estimates) and the alternative is <code>"link"</code> which is the linear scale (i.e. log-odds).</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods</dd>

</dl>

predict.fast_logistic_regression_stepwise: FastLR Wrapper Predictions

Description

Usage

Value

Arguments

Examples