create.pls.knockoff.Rd
Generate a knockoff variable set with PLSKO using PLS regression
create.pls.knockoff(
X,
nb.list = NULL,
threshold.abs = NULL,
threshold.q = 0.9,
ncomp = NULL,
sparsity = 1
)
A numeric matrix or data frame. The original design data matrix with n observations as rows and p variables as columns.
Optional. A list of length p or adjacency matrix of p×p that defines the neighbourship of variables.
A list of length p should include the neighbours' index of each variable from X1 to Xp in order;
The ith element in the list includes the indices of the neighbour variables of Xi, or NULL
when no neighbours.
A adjacency matrix should be symmetric with only binary element and where Mij=1 when Xi and Xj are neighbours;
otherwise Mij=0 when not neighbour or on diagonal (i.e. i=j).
If not provided or NULL, the neighborhoods are determined based on correlations.
Optional. A value between 0 and 1. A numeric value specifying an absolute correlation threshold to define neighborhoods.
Optional. A numeric value between 0 and 1 indicating the quantile of the correlation values to use as a threshold. Default is 0.9.
Optional. An integer specifying the number of components to use in the PLS regression. Default is 2.
Optional. A numeric value between 0 and 1 specifying the sparsity level in the PLS regression. Default is 1 (no sparsity).
A matrix of generated knockoff variables of n×p.
Yang, Guannan, et al. "PLSKO: a robust knockoff generator to control false discovery rate in omics variable selection." bioRxiv (2024): 2024-08.
Other create:
create.fixed()
,
create.gaussian()
,
create.knockoff()
,
create.pc.knockoff()
,
create.second_order()
,
create.zpls.knockoff()