R: Principal Sensitivity Components

prinsens {pense}

R Documentation

Principal Sensitivity Components

Description

Compute Principal Sensitivity Components for Elastic Net Regression

Usage

prinsens(
  x,
  y,
  alpha,
  lambda,
  intercept = TRUE,
  penalty_loadings,
  en_algorithm_opts,
  eps = 1e-06,
  sparse = FALSE,
  ncores = 1L,
  method = deprecated()
)

Arguments

`x`	`n` by `p` matrix of numeric predictors.
`y`	vector of response values of length `n`.
`alpha`	elastic net penalty mixing parameter with `0 \le \alpha \le 1`. `alpha = 1` is the LASSO penalty, and `alpha = 0` the Ridge penalty. Can be a vector of several values, but `alpha = 0` cannot be mixed with other values.
`lambda`	optional user-supplied sequence of penalization levels. If given and not `NULL`, `nlambda` and `lambda_min_ratio` are ignored.
`intercept`	include an intercept in the model.
`penalty_loadings`	a vector of positive penalty loadings (a.k.a. weights) for different penalization of each coefficient. Only allowed for `alpha` > 0.
`en_algorithm_opts`	options for the LS-EN algorithm. See en_algorithm_options for details.
`eps`	numerical tolerance.
`sparse`	use sparse coefficient vectors.
`ncores`	number of CPU cores to use in parallel. By default, only one CPU core is used. Not supported on all platforms, in which case a warning is given.
`method`	defunct. PSCs are always computed for EN estimates. For the PY procedure for unpenalized estimation use package pyinit.

Value

a list of principal sensitivity components, one per element in lambda. Each PSC is itself a list with items lambda, alpha, and pscs.

References

Cohen Freue, G.V.; Kepplinger, D.; Salibián-Barrera, M.; Smucler, E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Ann. Appl. Stat. 13 (2019), no. 4, 2065–2090 doi:10.1214/19-AOAS1269