relevant.features.p.extended: identify features (columns in the datamatrix) which are...

In Beirnaert/MetaboMeeseeks: Helper functions for metabolomics analysis

Description

This function produces a p-value for every column in the datamatrix, corresponding to the null hypothesis that outcome/responsevector is independent of that feature.

Usage

relevant.features.p.extended(datamatrix, responsevector,
  p.adj = "bonferroni")

Arguments

datamatrix	the data matrix with a column for each feature.
responsevector	the vector of outcomes/responses (e.g. class labels). the length of this vector should match the amount of rows in datamatrix.
p.adj	the adjustment method for the p-values. Any of 'holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr' or 'none' are accepted.

Value

data with the features, their (adjusted) p-values and the linear estimate for the response, one for every column in the datamatrix.

Author(s)

Charlie Beirnaert, [e-mail]

Examples

nSamples <- 10
nFeatures <- 20
data.matrix <- matrix( stats::runif(n=nFeatures*nSamples, min=0,max=100), 
ncol = nFeatures, nrow = nSamples)

response <- c( rep(0,nSamples/2), rep(1,nSamples/2) )
p_values <- relevant.features.p.extended(datamatrix = data.matrix, responsevector = 
response, p.adj = 'none')
p_values_adjusted <- relevant.features.p.extended( datamatrix = data.matrix, 
responsevector = response, p.adj = 'bonferroni')

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