binomialRF {binomialRF} R Documentation

## random forest feature selection based on binomial exact test

### Description

binomialRF is the R implementation of the feature selection algorithm by (Zaim 2019)

### Usage

binomialRF(X,y, fdr.threshold = .05,fdr.method = 'BY',
ntrees = 2000, percent_features = .5,
keep.both=FALSE, user_cbinom_dist=NULL,
sampsize=round(nrow(X)*.63))


### Arguments

 X design matrix y class label fdr.threshold fdr.threshold for determining which set of features are significant fdr.method how should we adjust for multiple comparisons (i.e., p.adjust.methods =c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none")) ntrees how many trees should be used to grow the randomForest? percent_features what percentage of L do we subsample at each tree? Should be a proportion between (0,1) keep.both should we keep the naive binomialRF as well as the correlated adjustment user_cbinom_dist insert either a pre-specified correlated binomial distribution or calculate one via the R package correlbinom. sampsize how many samples should be included in each tree in the randomForest

### Value

a data.frame with 4 columns: Feature Name, Frequency Selected, Probability of Selecting it randomly, Adjusted P-value based on fdr.method

### References

Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.

### Examples

set.seed(324)

###############################
### Generate simulation data
###############################

X = matrix(rnorm(1000), ncol=10)
trueBeta= c(rep(10,5), rep(0,5))
z = 1 + X %*% trueBeta
pr = 1/(1+exp(-z))
y = as.factor(rbinom(100,1,pr))

###############################
### Run binomialRF
###############################
require(correlbinom)

rho = 0.33
ntrees = 250
cbinom = correlbinom(rho, successprob =  calculateBinomialP(10, .5), trials = ntrees,
precision = 1024, model = 'kuk')

binom.rf <-binomialRF(X,y, fdr.threshold = .05,fdr.method = 'BY',
ntrees = ntrees,percent_features = .5,
keep.both=FALSE, user_cbinom_dist=cbinom,
sampsize=round(nrow(X)*rho))

print(binom.rf)


[Package binomialRF version 0.1.0 Index]