plsRmulti: Experimental multivariate-response PLS2 models

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Experimental multivariate-response PLS2 models

Description

plsRmulti() implements an experimental complete-case linear PLS2 fit for multivariate numeric responses. It is intentionally separate from plsR so the current PLS1 API remains unchanged.

Usage

plsRmulti(object, ...)

## Default S3 method:
plsRmultiModel(
  object,
  dataX,
  nt = 2,
  limQ2set = 0.0975,
  dataPredictY,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
plsRmultiModel(
  object,
  data,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  subset,
  contrasts = NULL,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object	For the default method, a numeric multivariate response matrix or data frame with at least two columns. For the formula method, a formula of the form cbind(y1, y2, ...) ~ ..
...	Not used. Extra arguments are rejected in this experimental release.
dataX	Numeric predictor matrix or data frame.
nt	Number of components to extract.
limQ2set	Kept for interface compatibility. Not supported by plsRmulti.
dataPredictY	Kept for interface compatibility. Not supported by plsRmulti; fit first and then use predict.
modele	Only "pls" is supported.
family	Not supported in this experimental release.
typeVC	Only "none" is supported.
EstimXNA	Not supported in this experimental release.
scaleX	Should predictors be scaled?
scaleY	Should responses be scaled? Defaults to TRUE.
pvals.expli	Not supported in this experimental release.
alpha.pvals.expli	Not supported in this experimental release.
MClassed	Not supported in this experimental release.
tol_Xi	Tolerance used for degeneracy checks during component extraction.
weights	Not supported in this experimental release.
sparse	Not supported in this experimental release.
sparseStop	Not supported in this experimental release.
naive	Not supported in this experimental release.
verbose	Should informational messages be displayed?
data	An optional data frame for the formula method.
subset	An optional subset for the formula method.
contrasts	Optional contrasts for the formula method.

Details

This experimental release supports complete-case linear PLS2 fitting, prediction, repeated k-fold cross-validation via cv.plsRmulti, and bootstrap resampling via bootpls. It still does not support missing values, weights, sparse extraction, classification diagnostics, or GLM families.

Value

An object of class "plsRmultiModel" with multivariate analogues of the linear plsR outputs, including the extracted scores tt, X loadings pp, response score coefficients CoeffC, coefficient matrix Coeffs, intercept vector CoeffConstante, scaled response matrix RepY, and fitted response matrices YChapeau, Std.ValsPredictY, and ValsPredictY.

See Also

predict.plsRmultiModel, cv.plsRmulti, bootpls, plsR

Examples

set.seed(123)
X <- matrix(rnorm(60 * 4), ncol = 4)
Y <- cbind(
  y1 = X[, 1] - 0.5 * X[, 2] + rnorm(60, sd = 0.1),
  y2 = 0.3 * X[, 2] + X[, 3] + rnorm(60, sd = 0.1)
)

fit <- plsRmulti(Y, X, nt = 2, verbose = FALSE)
fit
head(predict(fit))

plsRglm documentation built on June 17, 2026, 5:06 p.m.