spcaWrapper: Sparse PCA Wrapper
In PhilBoileau/scPCA: Sparse Contrastive Principal Component Analysis

spcaWrapper

R Documentation

Sparse PCA Wrapper

Description

This wrapper function specifies which implementation of sparse pricincipal component analysis (SPCA) is used to sparsify the loadings of the contrastive covariance matrix. Currently, the scPCA package supports the iterative algorithm detailed by \insertCitezou2006sparse;textualscPCA, and \insertCiteerichson2018sparse;textualscPCA's randomized and non-randomized versions of SPCA solved via variable projection. These methods are implemented in the elasticnet and sparsepca packages.

Usage

spcaWrapper(
  alg,
  contrast_cov,
  contrast,
  k,
  penalty,
  eigdecomp_tol,
  eigdecomp_iter
)

Arguments

`alg`	A `character` indicating the SPCA algorithm used to sparsify the contrastive loadings. Currently supports `iterative` for the \insertCitezou2006sparse;textualscPCA implemententation, `var_proj` for the non-randomized \insertCiteerichson2018sparse;textualscPCA solution, and `rand_var_proj` for the randomized \insertCiteerichson2018sparse;textualscPCA result.
`contrast_cov`	A contrastive covariance `matrix`.
`contrast`	A `numeric` contrastive parameter used to compute the contrastive covariance matrix.
`k`	A `numeric` indicating the number of eigenvectors (or sparse contrastive components) to be computed.
`penalty`	A `numeric` indicating the L1 penalty parameter applied to the loadings.
`eigdecomp_tol`	A `numeric` providing the level of precision used by eigendecompositon calculations.
`eigdecomp_iter`	A `numeric` indicating the maximum number of interations performed by eigendecompositon calculations.