Meeseeks.RF: Easy Cross-Validated Random Forest Binary Classification

In Beirnaert/MetaboMeeseeks: Helper functions for metabolomics analysis

Description

This function quickly performs a cross-validated Random Forest classification on a data matrix.

Usage

Meeseeks.RF(FeatureMatrix, GroupLabels, SampleLabels = NULL,
  nFolds = 10, nSims = 20, plot.out = TRUE, plot.type = "ROC",
  nCPU = -1, plotcol = NULL, plottitle.extra = NULL,
  Nplotpoints = 501, ...)

Arguments

FeatureMatrix	The matrix of Features (obtained by using the xcms::groupval function). Matrix has to have columns for features and rows for samples.
GroupLabels	The group labels. If not a factor a conversion will be applied.
SampleLabels	(optional) unique sample identifier.
nFolds	Number of cross validation folds.
nSims	Number of simulations (every simulation has different folds)
plot.out	Whether to print the ROC curve (default is TRUE).
plot.type	Type of plot output. "ROC" for receiver operator characteristic (default) or "PR" for precision-recall.
nCPU	The number of cores to use (default is the maximum amount available minus 2)
plotcol	(optional) colour to use for the plot
plottitle.extra	Optional extra character string to be added to every plot title.
Nplotpoints	The amount of points used to construct the plot.
...	Extra paremeters to be passed along to randomForest

Value

A ROC plot (if plot.out = TRUE) and a list with 2 elements: 1) a data frame with the ROC plot data and 2) a matrix with the variable importance for each cross validated simulation (nFolds * nSims times).

Author(s)

Charlie Beirnaert, [e-mail]

Beirnaert/MetaboMeeseeks documentation built on May 20, 2019, 11:09 a.m.