details_pls_mixOmics: Partial least squares via mixOmics

In parsnip: A Common API to Modeling and Analysis Functions

Partial least squares via mixOmics

Description

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_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.

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.

Case weights

The underlying model implementation does not allow for case weights.

References

• 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.

parsnip documentation built on June 24, 2024, 5:14 p.m.