Man pages for pjpalla/RFmarkerDetector
Multivariate Analysis of Metabolomics Data using Random Forests

aucMCVAUC multiple cross-validation
autoscaleUnit variance scaling method performed on the columns of the...
cachexiaDataMetabolite concentrations
combinatorialRFMCCVCombinatorial Monte Carlo CV
forestPerformanceCharacterizing the performance of a Random Forest model
getAvgAUCComputing the average AUC
getBestRFModelExtracting the best performing Random Forest model
lqvarFilterFiltering 'low quality' variables from the original dataset
mccvmccv class
mdsmds class
meanCenterMean centering performed on the columns of the data (i.e....
optimizeMTRYMtry Optimization
paretoscalePareto scaling method performed on the columns of the data...
pcaPrincipal Component Analysis
plotAUCvsCombinationsPlotting the average AUC as a function of the number of...
plot.mccvPlotting single or multiple ROC curves of the cross-validated...
plot.mdsMulti-dimensional Scaling (MDS) Plot
plotOOBvsMTRYPlotting the average OOB error and its 95% confidence...
plot.pca.loadingsPCA Loadings plot This function plots the relation between...
plot.pca.scoresPCA Scores plot This function creates a plot that graphically...
plotVarFreqVariable Frequency Plot
rfMCCVMonte Carlo cross-validation of Random Forest models
rfMCCVPerfExtracting average accuracy and recall of a list of Random...
rsdComputing relative standard deviation of a vector
rsdFilterFiltering less informative variables
screeplotScree Plot
simpleDatasimpleData
tuneMTRYTuning of the mtry parameter for a Random Forest model
tuneNTREETuning of the ntree parameter (i.e. the number of trees) for...
pjpalla/RFmarkerDetector documentation built on May 25, 2019, 8:19 a.m.