Based on arguments, this wrapper routes the data and arguments to the four
pls functions that are sparse/dense or regression/classification.
pls_fit(x, y, ncomp = NULL, predictor_prop = 1, ...)A data frame or matrix of predictors.
For classification, a factor. For regression, a matrix, vector, or data frame.
The number of PLS components. If left NULL, the maximum possible is used.
The maximum proportion of original predictors that can have non-zero coefficients for each PLS component (via regularization). This value is used for all PLS components for X.
A model object generated by mixOmics::pls(), mixOmics::plsda(),
mixOmics::spls(), or mixOmics::splsda().