mProbesParallel: mProbes feature selection algorithm parallelised

In jjvalletta/featselectRF: Feature selection using Random Forests

Description

Same as mProbes but parallelised

Usage

mProbesParallel(x, y, nRepeat = 100, nThread = parallel::detectCores() - 1,
  ...)

Arguments

x	N x D predictors data frame where N - no. of samples, D - no. of features
y	a vector of factors of length N, the target class (e.g as.factor("A", "A", "B", etc.))
nRepeat	no. of times features are permuted (this is the sample size used when comparing importance score for permuted vs real features)
nThread	no. of threads to use to run it in parallel (default: parallel::detected cores - 1)
...	arguments passed to the Random Forest classifier (e.g ntree, sampsize, etc.)

Value

A list with the following components:

impMetric	2D x nRepeat matrix of variable importance measures for each predictor (permuted and not) for every repeat (Note: the permuted variables have the suffix "Perm")
FWER	a numeric vector of length D with the family wise error rate computed for every feature

References

Huynh-Thu VA et al. Bioinformatics 2012

Examples

bWant <- iris$Species %in% c("versicolor", "virginica")
x <- iris[bWant, 1:4]
y <- droplevels(as.factor(iris$Species[bWant]))

out <- mProbesParallel(x, y, 100, 4)

jjvalletta/featselectRF documentation built on May 7, 2019, 2:55 a.m.