the function that computes the reweighted LTS-SPCA
ltsspcaRw(x, obj, k = NULL, alpha = 0.5, co.sd = 0.25)the input data matrix
initial LTS-SPCA object given by ltsspca function
dimension of the PC subspace; by default is NULL then k takes the value of kmax in the initial LTS-SPCA
the robust parameter which takes value between 0 to 0.5, default is 0.5
cutoff value for score outlier weight, default is 0.25
the object of class "ltsspcaRw" is returned
the sparse loading matrix estimated with reweighted LTS-SPCA
the estimated score matrix
the estimated eigenvalues
the center estimate
the list that contains the results of sPCA_rSVD on the reduced data
the orthonal distances with respect to the initially estimated PC subspace with all the noisy variables removed
the cutoff value for the orthogonal distances
if the observation is outlying in the orthgonal complement of the initially estimated PC subspace ws.od=0; otherwise ws.od=1
the score outlier weight, which is compared with 0.25 (by default) to flag score outliers
the cutoff value for score outlier weight, default is 0.25
if the observation is outlying with the PC subspace ws.sd=0; otherwise ws.sd=1
the retruned object when computing the score outlier weights