arrayspc: Sparse PCs of Microarrays
In elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA

Description Usage Arguments Details Value Author(s) References See Also

View source: R/enet_funcs.R

Sparse PC by iterative SVD and soft-thresholding

1	arrayspc(x,K=1,para,use.corr=FALSE, max.iter=200,trace=FALSE,eps=1e-3)

`x`	The microarray matrix.
`K`	Number of components. Default is 1.
`para`	The thresholding parameters. A vector of length K.
`use.corr`	Perform PCA on the correlation matrix? This option is only effective when the argument type is set "data".
`max.iter`	Maximum number of iterations.
`trace`	If TRUE, prints out its progress.
`eps`	Convergence criterion.

The function is equivalent to a special case of spca() with the quadratic penalty=infinity. It is specifically designed for the case p>>n, like microarrays.

A "arrayspc" object is returned.

Hui Zou and Trevor Hastie

Zou, H., Hastie, T. and Tibshirani, R. (2006) "Sparse principal component analysis" Journal of Computational and Graphical Statistics, 15 (2), 265–286.

spca, princomp

elasticnet documentation built on July 1, 2020, 11:48 p.m.