pip {jackstraw}

Compute posterior inclusion probabilities (PIPs)

Description

From a set of p-values, computes posterior probabilities that a feature should be truly included. For example, membership inclusion in a given cluster can be improved by filtering low quality members. In using PCA and related methods, it helps select variables that are truly associated with given latent variables.

Usage

pip(pvalue, group = NULL, pi0 = NULL, verbose = TRUE, ...)

Arguments

pvalue

a vector of p-values.

group

a vector of group indicators (optional). If provided, PIP analysis is stratified. Assumes groups are in 1:k where k is the number of unique groups.

pi0

a vector of pi0 values (optional). Its length has to be either 1 or equal the number of groups.

verbose

If TRUE, reports information.

...

optional arguments for lfdr to control a local FDR estimation.

Value

pip returns a vector of posterior inclusion probabilities

Author(s)

Neo Christopher Chung [e-mail]