Description Usage Arguments Value References See Also
Samples multivariate Gaussian fixed-design knockoff variables for the original variables.
1 2 | MFKnockoffs.create.gaussian(X, mu, Sigma, method = c("asdp", "sdp", "equi"),
diag_s = NULL)
|
X |
normalized n-by-p realization of the design matrix |
mu |
mean vector of length p for X |
Sigma |
p-by-p covariance matrix for X |
method |
either 'equi', 'sdp' or 'asdp' (default:'asdp') |
diag_s |
pre-computed vector of covariances between the original variables and the knockoffs. This will be computed according to 'method', if not supplied |
n-by-p matrix of knockoff variables
Candes et al., Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection, arXiv:1610.02351 (2016). https://statweb.stanford.edu/~candes/MF_Knockoffs/index.html
Other methods for creating knockoffs: MFKnockoffs.create.approximate_gaussian
,
MFKnockoffs.create.fixed
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.