Sparsity of the LW-PLS local models

The sparsity threshold \(\eta\) of lwpls(): the weight vector of each component of a local model is soft-thresholded at \(\eta \max_j |w_j|\) (sparse NIPALS, Hoffmann et al., 2015). Zero gives ordinary local PLS models; larger values drop more predictors.

Usage

sparsity(range = c(0, 0.9), trans = NULL)

Arguments

range

A two-element vector holding the defaults for the smallest and largest possible values, respectively.

trans

A trans object from the scales package, or NULL (the default) for no transformation.

Value

A quant_param object (see dials::new_quant_param()).

References

Hoffmann, I., Serneels, S., Filzmoser, P. and Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50–59. doi:10.1016/j.chemolab.2015.09.019

Examples

sparsity()
#> Sparsity Threshold (quantitative)
#> Range: [0, 0.9]
dials::value_seq(sparsity(), 4)
#> [1] 0.0 0.3 0.6 0.9