Axing mixOmics models

mixo_pls (via pls()), mixo_spls (via spls()), and mixo_plsda (via plsda()) objects are created with the mixOmics package, leveraged to fit partial least squares models.

Usage

# S3 method for class 'mixo_pls'
axe_call(x, verbose = FALSE, ...)

# S3 method for class 'mixo_spls'
axe_call(x, verbose = FALSE, ...)

# S3 method for class 'mixo_pls'
axe_data(x, verbose = FALSE, ...)

# S3 method for class 'mixo_spls'
axe_data(x, verbose = FALSE, ...)

# S3 method for class 'mixo_pls'
axe_fitted(x, verbose = FALSE, ...)

# S3 method for class 'mixo_spls'
axe_fitted(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed mixo_pls, mixo_spls, or mixo_plsda object.

Details

The mixOmics package is not available on CRAN, but can be installed from the Bioconductor repository via remotes::install_bioc("mixOmics").

Examples

if (FALSE) { # rlang::is_installed("mixOmics") && !butcher:::is_cran_check()
library(butcher)
do.call(library, list(package = "mixOmics"))

# pls ------------------------------------------------------------------
fit_mod <- function() {
  boop <- runif(1e6)
  pls(matrix(rnorm(2e4), ncol = 2), rnorm(1e4), mode = "classic")
}

mod_fit <- fit_mod()
mod_res <- butcher(mod_fit)

weigh(mod_fit)
weigh(mod_res)

new_data <- matrix(1:2, ncol = 2)
colnames(new_data) <- c("X1", "X2")
predict(mod_fit, new_data)
predict(mod_res, new_data)
}