tune.splslevel: Parallelized Tuning function for multilevel sPLS method using...

In mixOmicsTeam/mixOmics: Omics Data Integration Project

Parallelized Tuning function for multilevel sPLS method using BiocParallel

Description

For a multilevel spls analysis, the tuning criterion is based on the maximisation of the correlation between the components from both data sets

Usage

tune.splslevel(
  X,
  Y,
  multilevel,
  ncomp = NULL,
  mode = "regression",
  test.keepX = rep(ncol(X), ncomp),
  test.keepY = rep(ncol(Y), ncomp),
  already.tested.X = NULL,
  already.tested.Y = NULL,
  BPPARAM = BiocParallel::SerialParam(),
  seed = NULL
)

Arguments

X	numeric matrix of predictors. NAs are allowed.
Y	if(method = 'spls') numeric vector or matrix of continuous responses (for multi-response models) NAs are allowed.
multilevel	Design matrix for multilevel analysis (for repeated measurements) that indicates the repeated measures on each individual, i.e. the individuals ID. See Details.
ncomp	the number of components to include in the model.
mode	character string. What type of algorithm to use, (partially) matching one of "regression", "canonical", "invariant" or "classic".
test.keepX	numeric vector for the different number of variables to test from the X data set
test.keepY	numeric vector for the different number of variables to test from the Y data set
already.tested.X	Optional, if ncomp > 1 A numeric vector indicating the number of variables to select from the X data set on the firsts components.
already.tested.Y	Optional, if ncomp > 1 A numeric vector indicating the number of variables to select from the Y data set on the firsts components.
BPPARAM	BiocParallelParam object to manage parallelization
seed	set a number here if you want the function to give reproducible outputs. Not recommended during exploratory analysis. Note if RNGseed is set in 'BPPARAM', this will be overwritten by 'seed'.

Value

cor.value

correlation between latent variables

mixOmicsTeam/mixOmics documentation built on Dec. 3, 2024, 11:15 p.m.