`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
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