knockoff (version 0.3.6)

create.gaussian: Model-X Gaussian knockoffs

Description

Samples multivariate Gaussian model-X knockoff variables.

Usage

create.gaussian(X, mu, Sigma, method = c("asdp", "sdp", "equi"), diag_s = NULL)

Value

A n-by-p matrix of knockoff variables.

Arguments

X

n-by-p matrix of original variables.

mu

vector of length p, indicating the mean parameter of the Gaussian model for \(X\).

Sigma

p-by-p covariance matrix for the Gaussian model of \(X\).

method

either "equi", "sdp" or "asdp" (default: "asdp"). This determines the method that will be used to minimize the correlation between the original variables and the knockoffs.

diag_s

vector of length p, containing the pre-computed covariances between the original variables and the knockoffs. This will be computed according to method, if not supplied.

References

Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://web.stanford.edu/group/candes/knockoffs/index.html

See Also

Other create: create.fixed(), create.second_order()

Examples

set.seed(2022)
p=100; n=80; k=15
rho = 0.4
mu = rep(0,p); Sigma = toeplitz(rho^(0:(p-1)))
X = matrix(rnorm(n*p),n) %*% chol(Sigma)
nonzero = sample(p, k)
beta = 3.5 * (1:p %in% nonzero)
y = X %*% beta + rnorm(n)

# Basic usage with default arguments
knockoffs = function(X) create.gaussian(X, mu, Sigma)
result = knockoff.filter(X, y, knockoffs=knockoffs)
print(result$selected)

# Advanced usage with custom arguments
knockoffs = function(X) create.gaussian(X, mu, Sigma, method='equi')
result = knockoff.filter(X, y, knockoffs=knockoffs)
print(result$selected)