ltsspca (version 0.1.0)

sPCA_rSVD: Sparse Principal Component Analysis via Regularized Singular Value Decompsition (sPCA-rSVD)

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)
# }