nlcvRF_WHS

<p>This data set contains the <a rd-options="" href="/link/nlcv?package=nlcv&version=0.3.5" data-mini-rdoc="nlcv::nlcv">nlcv</a> results of 
selection of features with random forest on a weak signal dataset.</p>

Nested loop cross validation for classification purposes for misclassification error rate estimation.
The package supports several methodologies for feature selection: random forest, Student t-test, limma,
and provides an interface to the following classification methods in the 'MLInterfaces' package: linear,
quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized
linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
the classifier are included: plot of the ranks of the features, scores plot for a specific
classification algorithm and number of features, misclassification rate
for the different number of features and classification algorithms tested and ROC plot.
For further details about the methodology, please check:
Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)
<doi:10.2202/1544-6115.1078>.

Laure Cougnaud

nlcv

Nested Loop Cross Validation

nlcvRF_WHS function

Format

<p>This data set contains the <a rd-options='' href='nlcv'>nlcv</a> results of 
selection of features with random forest on a weak signal dataset.</p>

nlcvRF_WHS: nlcv results on weak signal data with random forest feature selection

Description

Usage

Arguments

Format