pcaSparse: Sparse Principal Components Analysis
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description Usage Arguments Value References Examples

This function is based on the original paper by Zou, Hastie, and Tibsharini (2006) where an elastic net formulation of principal components analysis was demonstrated.

pcaSparse(
  x,
  ncomp = min(nrow(x) - 1, ncol(x)),
  alpha = 0.75,
  lambda = 1e-04,
  scale = T,
  max.iter = 1000,
  tol = 1e-16
)

`x`	a data frame or matrix of numeric variables
`ncomp`	the number of components to extract.
`alpha`	the elastic net mixing parameter, which can take values of 0 ≤ α ≥ 1.
`lambda`	the shrinkage parameter. as in the elastic net, the L1 shrinkage penalty is λ_1 = α λ, and the L2 shrinkage penalty is λ_2 = (1-α) * λ*.
`scale`	should the variables be scaled prior to analysis? Defaults to TRUE.
`max.iter`	maximum number of iterations
`tol`	tolerance for convergence

an object of class PrincipalComp

Zou, H.; Hastie, T.; Tibshirani, R. (2006). Sparse principal component analysis. Journal of Computational and Graphical Statistics. 15 (2): 262–286. doi:10.1198/106186006x113430.