mgcca_bd: Generalized canonical correlation with missing individuals...

In isglobal-brge/GCCA: Generalized Canonical Correlation Analysis having missing individuals

Description

Generalized canonical correlation with missing individuals for big data

Usage

mgcca_bd(
  x,
  nfac = 2,
  scale = TRUE,
  pval = TRUE,
  scores = FALSE,
  method = "solve",
  lambda,
  mc.cores = 1,
  outfile = NULL,
  ...
)

Arguments

x	list of matrices. Each matrix should have the ids in the rownames. Missing is not allowed (see details)
nfac	...
scale	...
pval	should p-values of correlation between variables and shared canonical variates be computed? Default is TRUE.
scores	should canonical variables be computed for each table? Default is FALSE. See details
method	...
lambda	...
mc.cores	...
outfile	filename for output file, if filename is not NULL, results are saved in hdf5 file

Details

More dtails in vignette

Value

a list consisting of

Y	canonical components for the shared space
corsY	correlation between variables and shared canonical components
scores	canonical componets for each table
p.values	p-values of correlation between variables and shared canonical components
AVE	indicators of model quality based on the Average Variance Explained (AVE): AVE for each table, AVE_outer (average accross tables), AVE_inner(for the shared component).

Examples

see vignette

isglobal-brge/GCCA documentation built on Feb. 19, 2022, 9:21 a.m.