calcMis

Computation of the misclassification rate for risk
estimates in a binary classification problem.

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

calcMis function

<dl><dt>preds</dt>
<dd>Numeric vector of predictions</dd>
<dt>y</dt>
<dd>True outcomes</dd>
<dt>cutoff</dt>
<dd>Classification cutoff. By default,
scores above 50
otherwise.</dd></dl>

Arguments

Calculate the misclassification rate — calcMis

<dl>

<dt>preds</dt>
<dd>Numeric vector of predictions</dd>


<dt>y</dt>
<dd>True outcomes</dd>


<dt>cutoff</dt>
<dd>Classification cutoff. By default,
scores above 50
otherwise.</dd>

</dl>

calcMis: Calculate the misclassification rate

Description

Usage

Value

Arguments