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) .


News

0.3.5 o nlcv: add seed parameter, return testScores in case method not glm or nlda o nlcv: use only training samples for feature selection with random forest 0.3.4 o add RUnit and ALL in Suggests 0.3.3 o remove ASCII characters in bib file vignette 0.3.1 o more extensive package description 0.3.0 o roxygen2 documentation, check imports for submission to CRAN 0.2-2 o fix startup message o better data compression 0.2-1 o fix storing of results in classify_bioc_2.3 0.2-0 o fix nlcv issue with limma feature selection 0.1-99 o small documentation and namespace fixes 0.1-98 o fix ordering by geneID in nlcv with limma feature selection 0.1-97 o small fixes related to removal of nlcv as a4 dependency, a.o. introduction of dependency on a4Core 0.1-96 o add y axis label to scores plot and improve main title 0.1-95 o further fix of layout in mcrPlot 0.1-94 o fix layout in mcrPlot 0.1-93 o fix layout in scoresPlot 0.1-92 o add randomForest explicitly to dependencies o fix bug in nlcv with limma feature selection, where incidentally the geneID column could be a factor and therefore cause erroneous extraction from an ExpressionSet 0.1-91 o rename topFeatures into topTable and turn the topFeatures function into a method (for 'nlcv' objects) o make use of sample names as x-axis labels of scoresPlot 0.1-90 o document nldaI 0.1-89 o confusionMatrix name space fix 0.1-88 o replace MLearn interface for randomForest feature selection by plain use of the randomForest function 0.1-87 o add geneID argument to nlcv to allow for different gene ID names in the fData of the expression set 0.1-86 o small fix for topTable in limma2Groups (which is introduced as alias for limmaTwoGroups) 0.1-85 o turned confusionMatrix into a generic function 0.1-84 o added sampling strategy for unbalanced class distributions; argument 'distclass' of functions inTrainingSample and nlcv 0.1-83 o fix too protective check in nlcv (randomForest feature selection) 0.1-82 o fix to prevent pamr to mess up error handling 0.1-81 o add fsMethod = "none" o add AUC for models with continuous outputs (nlda, glm) o store ROC curves (ROCR package) o add nldaI interface allowing to store testScores (for lda models) o add customization of ntree and mtry for feature selection using randomForest o check that mtry <= maximum of nFeatures 0.1-80 o regenerate example data sets o add check on tech in scoresPlot 0.1-79 o small tweak to scoresPlot o add qda (quadratic discriminant analysis) and glm (logistic regression) interfaces 0.1-78 o add rescale argument to mcrPlot function o add plain lda and ksvm classifiers 0.1-77 o quick extension of pamrML and predict.pamrML for ExpressionSet objects o added method argument to topFeatures; can be one of "percentage", "meanrank" or "medianrank" o fix limma feature selection (use training set, not full set) o added inst/generateData.R, a script used to generate the data in data/ 0.1-76 o integrate pamr as an MLearn interface 0.1-75 o remove featureSelectionRF wrapper and move code into nlcv function o fix randomForest feature selection code 0.1-74 o add check on feature selection methods o add check on fact that it should be a two-class problem o new argument classifMethods to select the appropriate classification methods o updates related to BioC 2.3 release (testPredictions instead of predLabels) 0.1-73 o nlcv again separate package (after temporary merge with a4) 0.1-72 o added feature selection by limma (implying an additional dependency on a4) o improved passing of further parameters to the feature selection method (argument fsPar) 0.1-71 o integration of final package vignette o xtable method for summary.mcrPlot objects added 0.1-70 o another rewrite of simulateData o artificial datasets changed accordingly 0.1-69 o artificial datasets updated (using updated nlcv) o fixed buglet in simulateData 0.1-68 o fixed bug in nlcv (testPredictions returned factor, whereas predLabels returned a character vector) o small fix in simulateData o added run-time tests directory 0.1-67 o added simulated datasets: nlcvRF, nlcvTT_SS, nlcvRF_WS, nlcvTT_WS, nlcvRF_WHS, nlcvTT_WHS, nlcvRF_R and nlcvTT_R o scoresPlot: added layout argument (in order to allow multiple plots on one panel) o rankDistributionPlot added 0.1-66 o scoresPlot: put information on classification technique and number of features into main (instead of sub) in order to avoid overlap with the sample names (x axis labels) o topFeatures: the percentages now represent presence in the top n, instead of presence in the top 2*n 0.1-65 o fixed simulateData (for nNoEffectCols == 0) o added layout argument to mcrPlot function (in order to allow multiple plots on one panel) 0.1-64 o added xtable method for confusionMatrix objects o added explicit dependencies to sma, ipred, pamr and xtable 0.1-63 o featureSelectionRF added to name space o fixed naming issue in nlcv (featureSelectionRF instead of variableSelectionRF) 0.1-62 o license information formatted according to rules: GPL (>=2) o recursiveRandomForestFS removed and replaced by featureSelectionRF; the nFeaturesFinal argument of the former function has been dropped accordingly in the latter function o simulateData gained additional arguments betweenClassDifference and withinClassSd 0.1-61 added function simulateData 0.1-60 added print method for confusionMatrix objects 0.1-59 confusionMatrix function added 0.1-58 mcrPlot and scoresPlot gain an argument plot (defaults to TRUE) to allow use of the functions for computational purposes 0.1-57 export topFeatures in NAMESPACE 0.1-56 o scoresPlot: added supplementary legend for plot characters used o scoresPlot: added explicit main and sub arguments 0.1-55 scoresPlot: draw grid first, then add dots 0.1-54 scoresPlot: vectorize drawing of class membership strip 0.1-53 scoresPlot: fix drawing of class membership strip 0.1-52 added dependency on RColorBrewer for class membership display on scoresPlot 0.1-51 added topFeatures function and help page 0.1-50 put legend for scoresPlot in comment 0.1-49 make use of testPredictions instead of predLabels (new version of MLInterfaces) 0.1-47 remove remnant "type"s and replace with the appropriate variable passed as arguments to the functions (classVar argument) 0.1-46 replace randomForestB by MLearn and the appropriate method 0.1-45 removed warnings from generateData (by making a real ExpressionSet)

Reference manual

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install.packages("nlcv")

0.3.5 by Laure Cougnaud, 10 months ago


Browse source code at https://github.com/cran/nlcv


Authors: Willem Talloen , Tobias Verbeke


Documentation:   PDF Manual  


GPL-3 license


Imports limma, MASS, methods, graphics, Biobase, multtest, RColorBrewer, pamr, randomForest, ROCR, ipred, e1071, kernlab

Depends on a4Core, MLInterfaces, xtable

Suggests RUnit, ALL


See at CRAN