mht.1sample: Multiple hypothesis testing based on p value distribution for...
In mhtboot: Multiple Hypothesis Test Based on Distribution of p Values

Description Usage Arguments Details Value Examples

Implements multiple hypothesis testing based on bootstrap distribution of p values.

1 2	mht.1sample(X, B = 100, test = t.test, nbx = NROW(X), ncpus = 8, rbuff = 25, h = 30, qi = 0.9)

`X`	matrix of data
`B`	bootstrap sample size, default is 100
`test`	one sample test. by default t.test(), user can provide own function, must return p values in $p.value
`nbx`	size of the bootstrap sample
`ncpus`	number of cpu to use
`rbuff`	right buffer for change detection
`h`	window size for change detection
`qi`	the quantile to use for change detection

This function takes the dataset and produces the bootstrap distribtution of the transformed and ordered p values using the user given parameters. Then detects the change in the bootstrap distribution using the corner detection method. This method requires the user to specify the quantile to use for change detection. The change point is an estimate of the location of change from alternative to null and used to get the coordinates of the true signals.

list with two elements. cutoff: the location of corner, signal: the index of the detected coordinates.

n = 50;m = 100;m0 = 20;
sigeff = 1;
Sigma <- 0.25*diag(m)
X <- datgen(n,m,m0,sigeff,Sigma = Sigma)
out1 <- mht.1sample(X,B=100,ncpus = 1)
out1$cutoff
out1$signal