RFmarkerDetector: Multivariate Analysis of Metabolomics Data using Random Forests

A collection of tools for multivariate analysis of metabolomics data, which includes several preprocessing methods (normalization, scaling) and various exploration and data visualization techniques (Principal Components Analysis and Multi Dimensional Scaling). The core of the package is the Random Forest algorithm used for the construction, optimization and validation of classification models with the aim of identifying potentially relevant biomarkers.

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AuthorPiergiorgio Palla, Giuliano Armano
Date of publication2016-02-29 01:29:57
MaintainerPiergiorgio Palla <piergiorgio.palla@diee.unica.it>

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Man pages

aucMCV: AUC multiple cross-validation

autoscale: Unit variance scaling method performed on the columns of the...

cachexiaData: Metabolite concentrations

combinatorialRFMCCV: Combinatorial Monte Carlo CV

forestPerformance: Characterizing the performance of a Random Forest model

getAvgAUC: Computing the average AUC

getBestRFModel: Extracting the best performing Random Forest model

lqvarFilter: Filtering 'low quality' variables from the original dataset

mccv: mccv class

mds: mds class

meanCenter: Mean centering performed on the columns of the data (i.e....

optimizeMTRY: Mtry Optimization

paretoscale: Pareto scaling method performed on the columns of the data...

pca: Principal Component Analysis

plotAUCvsCombinations: Plotting the average AUC as a function of the number of...

plot.mccv: Plotting single or multiple ROC curves of the cross-validated...

plot.mds: Multi-dimensional Scaling (MDS) Plot

plotOOBvsMTRY: Plotting the average OOB error and its 95% confidence...

plot.pca.loadings: PCA Loadings plot This function plots the relation between...

plot.pca.scores: PCA Scores plot This function creates a plot that graphically...

plotVarFreq: Variable Frequency Plot

rfMCCV: Monte Carlo cross-validation of Random Forest models

rfMCCVPerf: Extracting average accuracy and recall of a list of Random...

rsd: Computing relative standard deviation of a vector

rsdFilter: Filtering less informative variables

screeplot: Scree Plot

simpleData: simpleData

tuneMTRY: Tuning of the mtry parameter for a Random Forest model

tuneNTREE: Tuning of the ntree parameter (i.e. the number of trees) for...


aucMCV Man page
autoscale Man page
cachexiaData Man page
combinatorialRFMCCV Man page
forestPerformance Man page
getAvgAUC Man page
getBestRFModel Man page
lqvarFilter Man page
mccv Man page
mds Man page
meanCenter Man page
optimizeMTRY Man page
paretoscale Man page
pca Man page
plotAUCvsCombinations Man page
plot.mccv Man page
plot.mds Man page
plotOOBvsMTRY Man page
plot.pca.loadings Man page
plot.pca.scores Man page
plotVarFreq Man page
rfMCCV Man page
rfMCCVPerf Man page
rsd Man page
rsdFilter Man page
screeplot Man page
simpleData Man page
tuneMTRY Man page
tuneNTREE Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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