print.bootstrap: Print a rgcca_bootstrap object

In Tenenhaus/RGCCA: Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) for multi-block data analysis

Print a rgcca_bootstrap object

Description

Print a fitted rgcca_bootstrap object. Parameters of the analysis and bootstrap statistics are displayed.

Usage

## S3 method for class 'bootstrap'
print(
  x,
  block = seq_along(x$rgcca$call$blocks),
  comp = 1,
  type = "weights",
  empirical = TRUE,
  display_order = FALSE,
  adj.method = "fdr",
  ...
)

Arguments

x	A fitted rgcca_bootstrap object (see rgcca_bootstrap)
block	A numeric corresponding to the block(s) to plot.
comp	A numeric vector indicating the component(s) to consider.
type	Character string indicating the bootstrapped object to plot: block-weight vectors ("weights", default) or block-loading vectors ("loadings").
empirical	A logical value indicating if the bootstrap confidence intervals and p-values are derived from the empirical distribution. (defaut: TRUE)
display_order	A logical value for ordering the variables. If TRUE, variables are ordered from highest to lowest absolute value. If FALSE, the block order is used. Default is TRUE.
adj.method	Character string indicating the method used to adjust for p-values. It must be a method handled by the p.adjust function. Default is "fdr".
...	Other parameters used in print (for the displaying of matrices) the means, 95% intervals, bootstrap ratio, p-values and other statistics.

Value

none

Examples

data("Russett")
blocks <- list(
  agriculture = Russett[, seq(3)],
  industry = Russett[, 4:5],
  politic = Russett[, 6:11]
)
fit.rgcca <- rgcca(blocks, ncomp = c(2, 1, 2))
boot.out <- rgcca_bootstrap(fit.rgcca, n_boot = 20, n_cores = 2)
print(boot.out)

Tenenhaus/RGCCA documentation built on March 16, 2023, 2:04 p.m.