cv.softKv: cross-validation for softSVD

In mengchen18/omic3plus: Different methods for the integrative analysis multiple omics data

Description

This function use k-fold cross-valiation method to optimize the sparsity of right singular values

Usage

cv.softKv(x, nf = 1, kv.opt = c(0.3, 0.5, 0.8), wv = 1, wu = 1,
  pos = FALSE, maxiter = 50, tol = sqrt(.Machine$double.eps),
  verbose = FALSE, init = c("svd", "average")[2], ncores = 1, fold = 5,
  nstart = 1, seed = NULL, loorss = FALSE)

Arguments

x	input matrix
nf	number of component
kv.opt	optional value for sparsity on right singular value
wv	weight for columns
wu	weight for rows
pos	whether retein non-negative results
maxiter	maximum number of iteration
tol	convergence tolerance
verbose	if print the progress
init	how to initialize the algorithm. if no sparsity, svd is fast.
ncores	the number of cores used, passed to mclapply
fold	fold number in cross validation
nstart	how many time the k-fold cross validation should be done
seed	set seed
loorss	if the Leave-one-out procedure should be used in matrix reconstruction

See Also

cv.softSVD

mengchen18/omic3plus documentation built on May 6, 2019, 4:59 p.m.