| relevant.features.p {speaq} | R Documentation |
Identify features (columns in the datamatrix) which are significantly associated with the outcome.
Description
This function produces a p-value for every column in the datamatrix, corresponding to the null hypothesis that outcome/response is independent of that feature.
Usage
relevant.features.p(
datamatrix,
response,
p.adj = "BH",
POI = 1,
responsevector = NULL
)
Arguments
datamatrix |
The data matrix with a column for each feature. |
response |
A vector or matrix of outcomes/responses (e.g. class labels). the length of this vector or the amount of rows in this matrix should match the amount of rows in datamatrix. |
p.adj |
The adjustment method for the p-values. Any of 'holm', 'hochberg', 'hommel', 'bonferroni', 'BH' (default), 'BY', 'fdr' or 'none' are accepted. |
POI |
Only if 'response' is a matrix! The p values of interest. This is a number indicating which column of the 'response' matrix you are interested in. POI can range from 1 (default) to the number of columns in 'response'. |
responsevector |
(deprecated), please use the the more general 'response' variable instead. |
Value
data with the features and their (adjusted) p-values, one for every column in the datamatrix .
Author(s)
Charlie Beirnaert, charlie.beirnaert@uantwerpen.be
Examples
nSamples <- 10
nFeatures <- 20
data.matrix <- matrix( stats::runif(n=nFeatures*nSamples, min=0,max=100),
ncol = nFeatures, nrow = nSamples)
responseVec <- c( rep(0,nSamples/2), rep(1,nSamples/2) )
p_values <- relevant.features.p(datamatrix = data.matrix, response =
responseVec, p.adj = 'none')
p_values_adjusted <- relevant.features.p( datamatrix = data.matrix,
response = responseVec, p.adj = 'bonferroni')