CRAN
nlcv 0.3.5
Nested Loop Cross Validation
Released Jun 29, 2018 by Laure Cougnaud
Dependencies
e1071 1.7-0 randomForest 4.6-14 ipred 0.9-7 ROCR 1.0-7 RColorBrewer 1.1-2 kernlab 0.9-27 MASS 7.3-50 pamr 1.55 xtable 1.8-2
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)