selectiveInference (version 1.2.5)

manyMeans: Selective inference for many normal means

Description

Computes p-values and confidence intervals for the largest k among many normal means

Usage

manyMeans(y, alpha=0.1, bh.q=NULL, k=NULL, sigma=1, verbose=FALSE)

Arguments

y

Vector of outcomes (length n)

alpha

Significance level for confidence intervals (target is miscoverage alpha/2 in each tail)

bh.q

q parameter for BH(q) procedure

k

Number of means to consider

sigma

Estimate of error standard deviation

verbose

Print out progress along the way? Default is FALSE

Value

mu.hat

Vector of length n containing the estimated signal sizes. If a sample element is not selected, then its signal size estimate is 0

selected.set

Indices of the vector y of the sample elements that were selected by the procedure (either BH(q) or top-K). Labelled "Selind" in output table.

pv

P-values for selected signals

ci

Confidence intervals

method

Method used to choose number of means

sigma

Value of error standard deviation (sigma) used

bh.q

BH q-value used

k

Desired number of means

threshold

Computed cutoff

call

The call to manyMeans

Details

This function compute p-values and confidence intervals for the largest k among many normal means. One can specify a fixed number of means k to consider, or choose the number to consider via the BH rule.

References

Stephen Reid, Jonathan Taylor, and Rob Tibshirani (2014). Post-selection point and interval estimation of signal sizes in Gaussian samples. arXiv:1405.3340.

Examples

# NOT RUN {
set.seed(12345)
n = 100 
mu = c(rep(3,floor(n/5)), rep(0,n-floor(n/5))) 
y = mu + rnorm(n)
out = manyMeans(y, bh.q=0.1)
out
# }