The mixOmics package can fit several different types of PLS models.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 2 tuning parameters:
predictor_prop: Proportion of Predictors (type: double, default: see below)num_comp: # Components (type: integer, default: 2L)
Translation from parsnip to the underlying model call (regression)
The plsmod extension package is required to fit this model.
library(plsmod)
pls(num_comp = integer(1), predictor_prop = double(1)) %>%
set_engine("mixOmics") %>%
set_mode("regression") %>%
translate()## PLS Model Specification (regression)
##
## Main Arguments:
## predictor_prop = double(1)
## num_comp = integer(1)
##
## Computational engine: mixOmics
##
## Model fit template:
## plsmod::pls_fit(x = missing_arg(), y = missing_arg(), predictor_prop = double(1),
## ncomp = integer(1))plsmod::pls_fit() is a function that:
Determines the number of predictors in the data.
Adjusts
num_compif the value is larger than the number of factors.Determines whether sparsity is required based on the value of
predictor_prop.Sets the
keepXargument ofmixOmics::spls()for sparse models.
Translation from parsnip to the underlying model call (classification)
The plsmod extension package is required to fit this model.
library(plsmod)
pls(num_comp = integer(1), predictor_prop = double(1)) %>%
set_engine("mixOmics") %>%
set_mode("classification") %>%
translate()## PLS Model Specification (classification)
##
## Main Arguments:
## predictor_prop = double(1)
## num_comp = integer(1)
##
## Computational engine: mixOmics
##
## Model fit template:
## plsmod::pls_fit(x = missing_arg(), y = missing_arg(), predictor_prop = double(1),
## ncomp = integer(1))In this case, plsmod::pls_fit() has the same role
as above but eventually targets mixOmics::plsda() or
mixOmics::splsda().
Installing mixOmics
This package is available via the Bioconductor repository and is not accessible via CRAN. You can install using:
if (!require("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_bioc("mixOmics")Preprocessing requirements
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via fit(), parsnip will
convert factor columns to indicators.
Variance calculations are used in these computations so zero-variance predictors (i.e., with a single unique value) should be eliminated before fitting the model.
Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.