dI {pARI}

Lower bound for the number of true discoveries

Description

Calculates (1-alpha) lower confidence bounds for the set-wise of false null hypotheses.

Usage

dI(ix, cv, pvalues, iterative, approx, ncomb, ...)

Arguments

ix

numeric vector. It refers to the set-wise hypotheses considered.

cv

numeric vector. It refers to the critical vector computed by criticalVector.

pvalues

matrix of pvalues with dimensions m \times B.

iterative

Boolean value. If iterative = TRUE, the iterative method for improvement of confidence envelopes is applied. Default @FALSE.

approx

Boolean value. Default @TRUE. If you are treating high dimensional data, we suggest to put approx = TRUE to speed up the computation time. Default @TRUE

ncomb

Numeric value. If approx = TRUE, you must decide how many random subcollections (level of approximation) considered. Default 100.

...

further arguments for the iterative approach, i.e., iterative = TRUE.

Value

numeric value: the lower confidence bound for the number of true discoveries concerning the cluster ix specified.

Author(s)

Angela Andreella

Examples

db <- simulateData(pi0 = 0.7, m = 100, n = 20, rho = 0)
out <- signTest(X = db)
pv <- cbind(out$pv, out$pv_H0)
cv <- criticalVector(pvalues = pv, family = "simes", lambda = 0.1, alpha = 0.1)
dI(ix = c(1:100), cv = cv, pvalues = pv)