ltspca: Principal Component Analysis Based on Least Trimmed Squaers...

In ltsspca: Sparse Principal Component Based on Least Trimmed Squares

Description

the function that computes LTS-PCA

Usage

ltspca(x, q, alpha = 0.5, b.choice = NULL, tol = 1e-06, N1 = 3,
  N2 = 2, N2bis = 10, Npc = 10)

Arguments

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

Value

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 ws=1; otherwise ws=0
best.cand	the method which computes the best deterministic starting value in the concentration step

Author(s)

Cevallos Valdiviezo

References

Cevallos Valdiviezo, H., Van Aelst, S. (2019), “ Fast computation of robust subspace estimators”, Computational Statistics & Data Analysis, 134, 171–185.

Examples

## Not run: 
ltspcaM <- ltspca(x = x, q = 2, alpha = 0.5)

## End(Not run)

ltsspca documentation built on Oct. 9, 2019, 5:06 p.m.