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
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)numeric matrix of predictors
a factor or a class vector for the discrete outcome
the number of components to include in the model. Default to 2.
Default set to c("regression", "canonical", "invariant", "classic")
Number of \(X\) variables kept in the model on the last components (once all keepX.constraint[[i]] are used).
A list containing which variables of X are to be kept on each of the first PLS-components.
Boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE).
Convergence stopping value.
integer, the maximum number of iterations.
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
"None" by default, or "CLR"
Designate multilevel design, "NULL" by default