splsda: Perform sPLS-DA

In xia-lab/MetaboAnalystR3.0: An R Package for Comprehensive Analysis of Metabolomics Data

Description

Sparse PLS functions (adapted from mixOmics package for web-based usage) this function is a particular setting of internal_mint.block the formatting of the input is checked in internal_wrapper.mint

Usage

splsda(
  X,
  Y,
  ncomp = 2,
  mode = c("regression", "canonical", "invariant", "classic"),
  keepX,
  keepX.constraint = NULL,
  scale = TRUE,
  tol = 1e-06,
  max.iter = 100,
  near.zero.var = FALSE,
  logratio = "none",
  multilevel = NULL
)

Arguments

X	numeric matrix of predictors
Y	a factor or a class vector for the discrete outcome
ncomp	the number of components to include in the model. Default to 2.
mode	Default set to c("regression", "canonical", "invariant", "classic")
keepX	Number of X variables kept in the model on the last components (once all keepX.constraint[[i]] are used).
keepX.constraint	A list containing which variables of X are to be kept on each of the first PLS-components.
scale	Boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE).
tol	Convergence stopping value.
max.iter	integer, the maximum number of iterations.
near.zero.var	boolean, see the internal nearZeroVar function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations
logratio	"None" by default, or "CLR"
multilevel	Designate multilevel design, "NULL" by default

Author(s)

Jeff Xia [e-mail] McGill University, Canada License: GNU GPL (>= 2)

xia-lab/MetaboAnalystR3.0 documentation built on May 6, 2020, 11:03 p.m.