rgcca_stability: Identify the most stable variables with SGCCA

In RGCCA: Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data

Identify the most stable variables with SGCCA

Description

This function can be used to identify the most stable variables identified as relevant by SGCCA. A Variable Importance in the Projection (VIP) based criterion is used to identify the most stable variables.

Usage

rgcca_stability(
  rgcca_res,
  keep = vapply(rgcca_res$a, function(x) mean(x != 0), FUN.VALUE = 1),
  n_boot = 100,
  n_cores = 1,
  verbose = TRUE,
  balanced = TRUE,
  keep_all_variables = FALSE
)

Arguments

rgcca_res	A fitted RGCCA object (see rgcca).
keep	A numeric vector indicating the proportion of variables per block to select.
n_boot	The number of bootstrap samples (default: 100).
n_cores	The number of cores for parallelization.
verbose	A logical value indicating if the progress of the procedure is reported.
balanced	A logical value indicating if a balanced bootstrap procedure is performed or not (default is TRUE).
keep_all_variables	A logical value indicating if all variables have to be kept even when some of them have null variance for at least one bootstrap sample (default is FALSE).

Value

A rgcca_stability object that can be printed and plotted.

top	A data.frame giving the indicator (VIP) on which the variables are ranked.
n_boot	The number of bootstrap samples, returned for further use.
keepVar	The indices of the most stable variables.
bootstrap	A data.frame with the block weight vectors computed on each bootstrap sample.
rgcca_res	An RGCCA object fitted on the most stable variables.

Examples

## Not run: 
 ###########################
 # stability and bootstrap #
 ###########################

 data("ge_cgh_locIGR", package = "gliomaData")
 blocks <- ge_cgh_locIGR$multiblocks
 Loc <- factor(ge_cgh_locIGR$y)
 levels(Loc) <- colnames(ge_cgh_locIGR$multiblocks$y)
 blocks[[3]] <- Loc

 fit_sgcca <- rgcca(blocks,
    sparsity = c(.071, .2, 1),
    ncomp = c(1, 1, 1),
    scheme = "centroid",
    verbose = TRUE, response = 3
)

 boot_out <- rgcca_bootstrap(fit_sgcca, n_boot = 100, n_cores = 1)

 fit_stab <- rgcca_stability(fit_sgcca,
   keep = sapply(fit_sgcca$a, function(x) mean(x != 0)),
   n_cores = 1, n_boot = 10,
   verbose = TRUE
 )

 boot_out <- rgcca_bootstrap(
   fit_stab, n_boot = 500, n_cores = 1, verbose = TRUE
 )

 plot(boot_out, block = 1:2, n_mark = 2000, display_order = FALSE)

## End(Not run)

RGCCA documentation built on Oct. 9, 2023, 5:09 p.m.