nlcv: Nested Loop Cross Validation

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>.

Version: 0.3.4
Depends: R (≥ 2.10), a4Core, MLInterfaces (≥ 1.22.0), xtable
Imports: limma, MASS, methods, graphics, Biobase, multtest, RColorBrewer, pamr, randomForest, ROCR, ipred, e1071, kernlab
Suggests: RUnit, ALL
Published: 2018-05-18
Author: Willem Talloen, Tobias Verbeke
Maintainer: Laure Cougnaud <laure.cougnaud at openanalytics.eu>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: nlcv results

Downloads:

Reference manual: nlcv.pdf
Vignettes: nlcv
Package source: nlcv_0.3.4.tar.gz
Windows binaries: r-devel: nlcv_0.3.4.zip, r-release: nlcv_0.3.4.zip, r-oldrel: nlcv_0.3.4.zip
OS X binaries: r-release: nlcv_0.3.4.tgz, r-oldrel: nlcv_0.3.4.tgz
Old sources: nlcv archive

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