calcAUC

This function computes the area under the receiver operating
characteristic curve.

A statistical learning method that tries to find the best set
of predictors and interactions between predictors for modeling binary or
quantitative response data in a decision tree. Several search algorithms
and ensembling techniques are implemented allowing for finetuning the
method to the specific problem. Interactions with quantitative
covariables can be properly taken into account by fitting local
regression models. Moreover, a variable importance measure for assessing
marginal and interaction effects is provided. Implements the
procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Michael Lau

logicDT

Identifying Interactions Between Binary Predictors

calcAUC function

<dl><dt>preds</dt>
<dd>Numeric vector of predicted scores</dd>
<dt>y</dt>
<dd>True binary outcomes coded as 0 or 1. Must be an integer
vector.</dd>
<dt>fast</dt>
<dd>Shall the computation be as fast as possible?</dd>
<dt>sorted</dt>
<dd>Are the predicted scores already sorted
increasingly? If so, this can slightly speed up the computation.</dd></dl>

Arguments

Fast computation of the AUC w.r.t. to the ROC — calcAUC

<dl>

<dt>preds</dt>
<dd>Numeric vector of predicted scores</dd>


<dt>y</dt>
<dd>True binary outcomes coded as 0 or 1. Must be an integer
vector.</dd>


<dt>fast</dt>
<dd>Shall the computation be as fast as possible?</dd>


<dt>sorted</dt>
<dd>Are the predicted scores already sorted
increasingly? If so, this can slightly speed up the computation.</dd>

</dl>

calcAUC: Fast computation of the AUC w.r.t. to the ROC

Description

Usage

Value

Arguments