RpartEVAL

This function evaluates the performance of the generated trees
 for error estimation by ten-fold cross validation assessment.

An open, multi-algorithmic pipeline for easy, fast and efficient
analysis of cellular sub-populations and the molecular signatures that
characterize them. The pipeline consists of four successive steps: data
pre-processing, cellular clustering with pseudo-temporal ordering, defining
differential expressed genes and biomarker identification. More details on
Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements
extensions of the work published by Ghannoum et. al. (2019)
<doi:10.1101/700989>.

Waldir Leoncio

DIscBIO

A User-Friendly Pipeline for Biomarker Discovery in Single-Cell
Transcriptomics

Salim Ghannoum

Alvaro Köhn-Luque

Damiano Fantini

RpartEVAL function

<dl><dt>data</dt>
<dd>The resulted data from running the function J48DT.</dd>
<dt>num.folds</dt>
<dd>A numeric value of the number of folds for the cross
validation assessment. Default is 10.</dd>
<dt>First</dt>
<dd>A string vector showing the first target cluster. Default is
"CL1"</dd>
<dt>Second</dt>
<dd>A string vector showing the second target cluster. Default is
"CL2"</dd>
<dt>quiet</dt>
<dd>If `TRUE`, suppresses intermediary output</dd></dl>

Arguments

Evaluating the performance of the RPART Decision Tree. — RpartEVAL

<dl>

<dt>data</dt>
<dd>The resulted data from running the function J48DT.</dd>


<dt>num.folds</dt>
<dd>A numeric value of the number of folds for the cross
validation assessment. Default is 10.</dd>


<dt>First</dt>
<dd>A string vector showing the first target cluster. Default is
"CL1"</dd>


<dt>Second</dt>
<dd>A string vector showing the second target cluster. Default is
"CL2"</dd>


<dt>quiet</dt>
<dd>If `TRUE`, suppresses intermediary output</dd>

</dl>

RpartEVAL: Evaluating the performance of the RPART Decision Tree.

Description

Usage

Value

Arguments