details_pls_mixOmics | R Documentation |
The mixOmics package can fit several different types of PLS models.
For this engine, there are multiple modes: classification and regression
This model has 2 tuning parameters:
predictor_prop
: Proportion of Predictors (type: double, default: see
below)
num_comp
: # Components (type: integer, default: 2L)
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_comp
if the value is larger than the number of factors.
Determines whether sparsity is required based on the value of
predictor_prop
.
Sets the keepX
argument of mixOmics::spls()
for sparse models.
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()
.
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")
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.
The underlying model implementation does not allow for case weights.
Rohart F and Gautier B and Singh A and Le Cao K-A (2017). “mixOmics: An R package for ’omics feature selection and multiple data integration.” PLoS computational biology, 13(11), e1005752.
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