Description Usage Arguments Value Author(s) References Examples
the function that computes LTS-PCA
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x |
the input data matrix |
q |
the dimension of the PC subspace |
alpha |
the robust parameter which takes value between 0 to 0.5, default is 0.5 |
b.choice |
intial loading matrix; by default is NULL and the deterministic starting values will be computed by the algorithm |
tol |
convergence criterion |
N1 |
the number controls the updates for a without updating b in the concentration step |
N2 |
the number controls outer loop in the concentration step |
N2bis |
the number controls the outer loop for the selected b |
Npc |
the number controls the inner loop |
the object of class "ltspca" is returned
b |
the unnormalized loading matrix |
mu |
the center estimate |
ws |
if the observation in included in the h-subset |
best.cand |
the method which computes the best deterministic starting value in the concentration step |
Cevallos Valdiviezo
Cevallos Valdiviezo, H., Van Aelst, S. (2019), “ Fast computation of robust subspace estimators”, Computational Statistics & Data Analysis, 134, 171–185.
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