set.seed(12)
## generate 10-dimensional data with 10% casewise contamination
n <- 100
p <- 10
A <- matrix(0.9, p, p)
diag(A) <- 1
x <- generate.casecontam(n, p, cond=100, contam.size=10, contam.prop=0.1, A=A)$x
## introduce 5% missingness
pmiss <- 0.05
nmiss <- matrix(rbinom(n*p,1,pmiss), n,p)
x[ which( nmiss == 1 ) ] <- NA
## Using EMVE as initial
res.emve <- GSE(x)
slrt( getScatter(res.emve), A) ## LRT distances to the true covariance
## Using QC as initial
res.qc <- GSE(x, init="qc")
slrt( getScatter(res.qc), A) ## in general performs worse than if EMVE used as initials
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