knockoff_filter: Runs knockoff procedure

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

Description

Runs knockoff procedure for Fixed-X from Barber & Candes (2015).

Usage

knockoff_filter(
  X,
  y,
  method = c("sdp", "equi"),
  fdr = 0.2,
  plus = TRUE,
  randomize = FALSE
)

Arguments

X	n-by-p design matrix, with n > p.
y	response vector of length n.
method	either "equi" or "sdp" (default: "sdp"). These are two different methods to generate the p-dimensional s vector, which is used to constuct the knockoff variables.
fdr	desired false discovery rate.
plus	True for knockoff+ procedure, false for knockoff procedure.
randomize	If true, U tilde is a random matrix. If false, U tilde is the second half of the matrix Q, where Q is part of the QR decomposition of an nx2p matrix [U\ 0], where X = UDV^T. default is false.

Details

This runs the knockoff filter 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.
Xk	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 <- knockoff_filter(X, y, 'equi', 0.20, TRUE)

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