Description Usage Arguments Value Author(s) References Examples
the function that computes the initial LTS-SPCA
1 2 3 |
x |
the input data matrix |
kmax |
the maximal number of PCs searched by the intial LTS-SPCA |
alpha |
the robust parameter which takes value between 0 to 0.5, default is 0.5 |
mu.choice |
the center estimate fixed by the user; by default, the center will be estimated automatically by the algorithm |
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 |
tol |
convergence criterion |
N1 |
the number controls the updates for a without updating b in the concentration step for LTS-PCA |
N2 |
the number controls outer loop in the concentration step for LTS-PCA |
N2bis |
the number controls the outer loop for the selected b for both LTS-PCA and LTS-SPCA |
Npc |
the number controls the inner loop for both LTS-PCA and LTS-SPCA |
the object of class "ltsspca" is returned
loadings |
the initially estimated loading matrix by LTS-SPCA |
mu |
the center estimates associated with each PC |
spca.it |
the list that contains the results of LTS-SPCA when searching for the individual PCs |
ls |
the list that contains the final search grid for each PC direction |
Yixin Wang
Wang, Y., Van Aelst, S. (2019), “ Sparse Principal Component Based On Least Trimmed Squares”, Technometrics, accepted.
1 2 3 4 5 6 7 | library(mvtnorm)
dataM <- dataSim(n = 200, p = 20, bLength = 4, a = c(0.9, 0.5, 0),
SD = c(10, 5, 2), eps = 0, seed = 123)
x <- dataM$data
ltsspcaMI <- ltsspca(x = x, kmax = 5, alpha = 0.5)
ltsspcaMR <- ltsspcaRw(x = x, obj = ltsspcaMI, k = 2, alpha = 0.5)
matplot(ltsspcaMR$loadings,type="b",ylab="Loadings")
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