Model-X Gaussian knockoffs

Samples multivariate Gaussian model-X knockoff variables.

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

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.

Value

A n-by-p matrix of knockoff variables.

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.pc(), create.pls(), create.second_order(), create.seq(), create.shrink_Gaussian(), create.sparse_Gaussian(), create.sparse_seq(), create.zpls()