In tidymodels/plsmod: Model Wrappers for Projection Methods

Examples

For regression, let's use the Tecator data in the modeldata package:

library(tidymodels)
library(plsmod)
tidymodels_prefer()
theme_set(theme_bw())

data(meats, package = "modeldata")

Note that using tidymodels_prefer() will resulting getting parsnip::pls() instead of mixOmics::pls() when simply running pls().

Although plsmod can fit multivariate models, we'll concentration on a univariate model that predicts the percentage of protein in the samples.

meats <- meats %>% select(-water, -fat)

We define a sparse PLS model by setting the predictor_prop argument to a value less than one. This allows the model fitting process to set certain loadings to zero via regularization.

sparse_pls_spec <- 
  pls(num_comp = 10, predictor_prop = 1/3) %>% 
  set_engine("mixOmics") %>% 
  set_mode("regression")

The model is fit either with a formula or by passing the predictors and outcomes separately:

form_fit <- 
  sparse_pls_spec %>% 
  fit(protein ~ ., data = meats)
form_fit

# or 

sparse_pls_spec %>% 
  fit_xy(x = meats %>% select(-protein), y = meats$protein)