In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("pls", "mixOmics")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("num_comp", "predictor_prop"),
                 default = c("2L",           "see below"))

param <-
  pls() %>% 
  set_engine("mixOmics") %>% 
  set_mode("regression") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the underlying model call (regression)

r uses_extension("pls", "mixOmics", "regression")

library(plsmod)

pls(num_comp = integer(1), predictor_prop = double(1)) %>%
  set_engine("mixOmics") %>%
  set_mode("regression") %>%
  translate()

[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)

r uses_extension("pls", "mixOmics", "classification")

library(plsmod)

pls(num_comp = integer(1), predictor_prop = double(1)) %>%
  set_engine("mixOmics") %>%
  set_mode("classification") %>%
  translate()

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

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 Aug. 18, 2023, 1:07 a.m.