permutation_filter: Runs permutation procedure

In svteichman/prelim.knockoffs: Implements the knockoff method by Barber & Candes

Description

Runs knockoff procedure with permuted variables instead of knockoff variables for Fixed-X from Barber & Candes (2015).

Usage

permutation_filter(X, y, fdr = 0.2, plus = TRUE)

Arguments

X	n-by-p design matrix, with n > p.
y	response vector of length n.
fdr	desired false discovery rate.
plus	True for knockoff+ procedure, false for knockoff procedure.

Details

This runs the knockoff procedure with permuted variables instead of knockoff variables for a given X matrix and y vector. This controls the false discovery rate at a given level.

Value

A list containing:

X	n-by-p design matrix, rescaled so that ||X_j||^2_2 = 1, and augmented if n < 2p.
X_perm	n-by-p matrix of knockoff variables.
y	vector of observed responses (augmented if n < 2p).
W	vector of test statistics.
thresh	threshold for variable selection.
Selected	list of selected variables.

References

Barber and Candes, Controlling the false discovery rate via knockoffs. Ann. Statist. 43 (2015), no. 5, 2055–2085. https://projecteuclid.org/euclid.aos/1438606853

Examples

p <- 50; n <- 100; k <- 5
X <- matrix(stats::rnorm(n*p), nrow = n)
true_covar <- sample(p, k)
beta <- 5 * (1:p %in% true_covar)
y <- X %*% beta + stats::rnorm(n, mean = 0, sd = 1)
results <- permutation_filter(X, y, 0.20, TRUE)

svteichman/prelim.knockoffs documentation built on May 28, 2020, 5:14 p.m.