acca (version 0.2)

cc_inference: cc_inference

Description

For each pairs of components, it computes p-values to test the null hypothesis of no correlation between components. The p-values are computed following the resampling method developed in Winkler, A. M., Renaud, O., Smith, S. M., & Nichols, T. E. (2020). Permutation inference for canonical correlation analysis. NeuroImage, 220, 117065. https://doi.org/10.1016/j.neuroimage.2020.117065.

Usage

cc_inference(
  mod,
  B = 100,
  alpha_max = 0.5,
  numb_cc = NULL,
  resamp_type = "sign-flip",
  light = FALSE
)

Arguments

mod

an acca object.

B

(100 by default) number of random sampling

alpha_max

stop if p-value > alpha_max (alpha_max=0.5 by default).

numb_cc

stop after computing p-values for the first numb_cc are computed(numb_cc=NULL - the default - means compute all).

resamp_type

"sign-flip" (by default) or "permutation"

light

If TRUE the orthogonalization of the residuals of the projection on Z is not performed. For large sample size the two methods tend to overlap.

Value

It returns an acca object (see cc) with p-values for each pair of the numb_cc components.

Examples

# NOT RUN {
set.seed(1)
X=matrix(rnorm(500),100,5)
Y=matrix(rnorm(700),100,7)
Z=matrix(rnorm(200),100,2)
mod=cc(X,Y,Z)
mod

ccbiplot(mod)

mod=cc_inference(mod, B = 100, numb_cc = 3)
mod
# }