sPCA_rSVD: Sparse Principal Component Analysis via Regularized Singular...

In ltsspca: Sparse Principal Component Based on Least Trimmed Squares

Description

the function that computes sPCA_rSVD

Usage

sPCA_rSVD(x, k, method = "hard", center = FALSE, scale = FALSE,
  l.search = NULL, ls.min = 1)

Arguments

x	the input data matrix
k	the maximal number of PC's to seach for in the initial stage
method	threshold method used in the algorithm; If method = "hard" (defauls), the hard threshold function is used; if method = "soft", the soft threshold function is used; if method = "scad", the scad threshold function is used
center	if center = TRUE the data will be centered by the columnwise means; default is center = FALSE
scale	if scale = TRUE the data will be scaled by the columnwise standard deviations; default is scaled = FALSE
l.search	a list of length kmax which contains the search grids chosen by the user; default is NULL
ls.min	the smallest grid step when searching for the sparsity of each PC; default is 1

Value

an object of class "sPCA_rSVD" is returned

loadings	the sparse loading matrix estimated with sPCA_rSVD
scores	the estimated score matrix
eigenvalues	the estimated eigenvalues
spca.it	the list that contains the results of sPCA_rSVD when searching for the individual PCs
ls	the list that contains the final search grid for each PC direction

References

Shen, H. and Huang, J. (2008), “Sparse principal component anlysis via regularized low rank matrix decomposition”, Journal of Multivariate Analysis, 99, 1015–1034.

Shen, D., Shen, H., and Marron, J. (2013). “Consistency of sparse PCA in high dimensional low sample size context”, Journal of Multivariate Analysis, 115, 315–333.

Examples

## Not run: 
nonrobM <- sPCA_rSVD(x = x, k = 2, center =  T, scale = F)

## End(Not run)

ltsspca documentation built on Oct. 9, 2019, 5:06 p.m.