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`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.

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

library(butcher)
do.call(library, list(package = "mixOmics"))
#> 
#> Loaded mixOmics 6.22.0
#> Thank you for using mixOmics!
#> Tutorials: http://mixomics.org
#> Bookdown vignette: https://mixomicsteam.github.io/Bookdown
#> Questions, issues: Follow the prompts at http://mixomics.org/contact-us
#> Cite us:  citation('mixOmics')
#> 
#> Attaching package: ‘mixOmics’
#> The following objects are masked from ‘package:caret’:
#> 
#>     nearZeroVar, plsda, splsda
#> The following objects are masked from ‘package:parsnip’:
#> 
#>     pls, tune

# 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)
#> # A tibble: 24 × 2
#>    object             size
#>    <chr>             <dbl>
#>  1 X              0.801   
#>  2 variates.X     0.801   
#>  3 variates.Y     0.801   
#>  4 Y              0.721   
#>  5 names.sample   0.640   
#>  6 input.X        0.161   
#>  7 call           0.00129 
#>  8 loadings.X     0.000776
#>  9 loadings.Y     0.000696
#> 10 loadings.star1 0.0006  
#> # … with 14 more rows
weigh(mod_res)
#> # A tibble: 24 × 2
#>    object              size
#>    <chr>              <dbl>
#>  1 X               0.801   
#>  2 variates.X      0.801   
#>  3 variates.Y      0.801   
#>  4 Y               0.721   
#>  5 loadings.X      0.000776
#>  6 loadings.Y      0.000696
#>  7 loadings.star1  0.0006  
#>  8 mat.c           0.0006  
#>  9 loadings.star2  0.00052 
#> 10 prop_expl_var.X 0.000352
#> # … with 14 more rows

new_data <- matrix(1:2, ncol = 2)
colnames(new_data) <- c("X1", "X2")
predict(mod_fit, new_data)
#> 
#> Call:
#>  predict.mixo_pls(object = mod_fit, newdata = new_data) 
#> 
#>  Main numerical outputs: 
#>  -------------------- 
#>  Prediction values of the test samples for each component: see object$predict 
#>  variates of the test samples: see object$variates 
predict(mod_res, new_data)
#> 
#> Call:
#>  predict.mixo_pls(object = mod_res, newdata = new_data) 
#> 
#>  Main numerical outputs: 
#>  -------------------- 
#>  Prediction values of the test samples for each component: see object$predict 
#>  variates of the test samples: see object$variates