cv.plsRmulti: Experimental k-fold cross-validation for...

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Experimental k-fold cross-validation for multivariate-response PLS2

Description

cv.plsRmulti() performs repeated k-fold cross-validation for the experimental complete-case linear plsRmulti workflow.

Usage

cv.plsRmulti(object, ...)

## Default S3 method:
cv.plsRmultiModel(
  object,
  dataX,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  K = 5,
  NK = 1,
  grouplist = NULL,
  random = TRUE,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepfolds = FALSE,
  keepdataY = TRUE,
  keepMclassed = FALSE,
  EstimXNA = FALSE,
  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'
cv.plsRmultiModel(
  object,
  data = NULL,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  K = 5,
  NK = 1,
  grouplist = NULL,
  random = TRUE,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepfolds = FALSE,
  keepdataY = TRUE,
  keepMclassed = FALSE,
  EstimXNA = FALSE,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights = NULL,
  subset = NULL,
  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 in each fold fit.
limQ2set	Threshold used by cvtable for the aggregated Q2 criterion.
modele	Only "pls" is supported.
family	Not supported in this experimental release.
K	Number of groups for each partition.
NK	Number of repeated partitions.
grouplist	Optional user-supplied partitions.
random	Should the folds be generated randomly?
scaleX	Should predictors be scaled?
scaleY	Should responses be scaled? Defaults to TRUE.
keepcoeffs	Should standardized coefficient vectors be stored for each fold fit?
keepfolds	Should training indices be stored for each fold fit?
keepdataY	Kept for interface compatibility. Observed fold responses are stored so that summaries can be computed.
keepMclassed	Not supported in this experimental release.
EstimXNA	Not supported in this experimental release.
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

Only the linear multivariate-response PLS2 mode is supported here. Missing values, weights, sparse extraction options, classification diagnostics, and GLM families remain out of scope for this experimental API.

Value

An object of class "cv.plsRmultiModel" with repeated fold predictions, observed fold responses, optional coefficient vectors and fold indices, and the reference full-data "plsRmultiModel" fit used for aggregated summary metrics.

See Also

plsRmulti, summary.cv.plsRmultiModel, cvtable, bootpls

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)
)

cv_fit <- cv.plsRmulti(Y, X, nt = 2, K = 3, NK = 1, verbose = FALSE)
summary(cv_fit, verbose = FALSE)

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