Package 'rFDP'

Multi-resolution FDX control based on resampling

Description

Provides a threshold that guarantees multi-resolution FDX control, given a matrix of resampled test statistics

Usage

mr.FDX(teststats,alpha,gamma,sequential=FALSE,ncombs=25)

Arguments

teststats	A matrix of resampled test statistics. The first row corrresponds to the original data.
alpha	The desired FDX rate
gamma	The target (maximal) FDP
sequential	Set to TRUE if the (slower) sequential method should be used
ncombs	The nr. of random combinations that the sequential method checks.

Value

The rejection threshold that guarantees multi-resolution FDX control.

Examples

set.seed(123)

n=10   #samle size per group
m=100  #nr. of hypotheses
w=50   #nr of random permutations

X <- matrix(rnorm((2*n)*m), 2*n, m) #make data matrix (m columns, i.e., m hypotheses)
y <- c(numeric(n)+1,numeric(n)-1)   #group labels

Y <- t(replicate(w, sample(y, size=2*n, replace=FALSE)))  #make matrix with permuted group labels
Y[1,] <- y

#Add some signal to the first 50 columns, so that 50 hypotheses are false:
X[1:n,1:50] <- X[1:n,1:50] + 1.5


#Make matrix with test statistics for permuted versions of the data:
tstats <- matrix(nr=w,nc=m) #Matrix with resampled test statistics
for(j in 1:w){
  for(i in 1:m){
    # Compute t-statistic for j-th permutation for i-th hypothesis:
    avg1 <- (sum(X[Y[j,]==1,i]))/n; avg2 <- (sum(X[Y[j,]==-1,i]))/n
    s <- sqrt( (sum( (X[Y[j,]==1,i]-avg1)^2 ) + sum( (X[Y[j,]==-1,i]-avg2)^2 )  )/(2*n-2) )
    tstats[j,i] <- abs( (avg1-avg2)/ ( sqrt(2/n) * s  )) 
  }
}


q = mr.FDX(teststats=tstats,alpha=0.1,gamma=0.1,sequential=FALSE)

#Check which hypotheses are rejected (for the maximum threshold that provides FDX control):
which( tstats[1,] > q )

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