Experimental multivariate-response PLS2 models

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.

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.

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.

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
#> Experimental multivariate PLS2 model
#> Number of responses:
#> [1] 2
#> Number of required components:
#> [1] 2
#> Number of successfully computed components:
#> [1] 2
#> Coefficient matrix:
#>               y1         y2
#> X.1  1.001797150 0.01301818
#> X.2 -0.483516920 0.32994042
#> X.3 -0.029327449 0.94052217
#> X.4 -0.004080383 0.18787469
head(predict(fit))
#>            y1         y2
#> 1 -0.72491996  0.0330750
#> 2  0.05416003 -0.8182979
#> 3  1.75767102 -0.6124818
#> 4  0.59341225 -0.7343838
#> 5  0.61387418  1.3420938
#> 6  1.61054712 -0.5235085