the function that computes sPCA_rSVD
sPCA_rSVD(x, k, method = "hard", center = FALSE, scale = FALSE,
l.search = NULL, ls.min = 1)the input data matrix
the maximal number of PC's to seach for in the initial stage
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
if center = TRUE the data will be centered by the columnwise means; default is center = FALSE
if scale = TRUE the data will be scaled by the columnwise standard deviations; default is scaled = FALSE
a list of length kmax which contains the search grids chosen by the user; default is NULL
the smallest grid step when searching for the sparsity of each PC; default is 1
an object of class "sPCA_rSVD" is returned
the sparse loading matrix estimated with sPCA_rSVD
the estimated score matrix
the estimated eigenvalues
the list that contains the results of sPCA_rSVD when searching for the individual PCs
the list that contains the final search grid for each PC direction
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.
# NOT RUN {
nonrobM <- sPCA_rSVD(x = x, k = 2, center = T, scale = F)
# }
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