`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 mixo_pls
axe_call(x, verbose = FALSE, ...)
# S3 method for mixo_spls
axe_call(x, verbose = FALSE, ...)
# S3 method for mixo_pls
axe_data(x, verbose = FALSE, ...)
# S3 method for mixo_spls
axe_data(x, verbose = FALSE, ...)
# S3 method for mixo_pls
axe_fitted(x, verbose = FALSE, ...)
# S3 method for 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.

## 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)
}
```