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: |
0.3.5 |
| 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: |
2018-06-29 |
| Author: |
Willem Talloen, Tobias Verbeke |
| Maintainer: |
Laure Cougnaud <laure.cougnaud at openanalytics.eu> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
nlcv results |
Documentation:
Downloads:
Reverse dependencies:
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