This is a generic method, use `pcvpls()` or `pcvpcr()` instead.
pcvreg(
X,
Y,
ncomp = min(nrow(X) - 1, ncol(X), 30),
cv = list("ven", 4),
center = TRUE,
scale = FALSE,
funlist = list(),
cv.scope = "global"
)
matrix with predictors from the training set.
vector with response values from the training set.
number of components to use (more than the expected optimal number).
which split method to use for cross-validation (see description of method `pcvpls()` for details).
logical, to center or not the data sets
logical, to scale or not the data sets
list with functions for particular implementation
scope for center/scale operations inside CV loop: 'global' — using globally computed mean and std or 'local' — recompute new for each local calibration set.