Description Usage Arguments Details Value Author(s) References See Also Examples
Computes an estimator optimizing the Gaussian likelihood over a snipping and trimming set.
1 |
X |
Data. |
V |
Binary matrix of the same size as |
tol |
Tolerance for convergence. Default is |
maxiters |
Maximum number of iterations for the SM algorithm. Default is |
maxiters.S |
Maximum number of iterations of the inner greedy snipping algorithm. Default is |
print.it |
Logical; if |
This function combines computes the snipEM
estimator of Farcomeni
(2014) with trimming. Optimization over a trimming set is performed
via usual concentration steps (Rousseeuw and van Driessen, 1999).
It therefore provides a robust estimate of
location and scatter in presence of entry-wise and case-wise
outliers. The number of snipped entries and trimmed rows is kept
fixed throughout. V
must contain at least one row of zeros
(otherwise use snipEM
).
A list with the following elements:
mu | Estimated location. |
S | Estimated scatter matrix. |
V | Final (optimal) V matrix. |
lik | Gaussian log-likelihood at convergence. |
iter | Number of outer iterations before convergence. |
Alessio Farcomeni alessio.farcomeni@uniroma1.it, Andy Leung andy.leung@stat.ubc.ca
Farcomeni, A. (2014) Snipping for robust k-means clustering under component-wise contamination, Statistics and Computing, 24, 909-917
Farcomeni, A. (2014) Robust constrained clustering in presence of entry-wise outliers, Technometrics, 56, 102-111
Rousseeuw, P. J. and Van Driessen, K. (1999) A fast algorithm for the minimum covariance determinant estimator, Technometrics, 41, 212-223.
sclust
, snipEM
,
sumlog
,
ldmvnorm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | set.seed(1234)
X=matrix(rnorm(100*10),100,5)
X[1:5,]=50
X[6,1]=150
# initial V
V <- matrix(1, 100, 5)
V[1:5,]=0
Vtmp <- V[-c(1:5),]
# identify cells to be snipped
Vtmp[!is.na(match(as.vector(X[-c(1:5),]),boxplot(as.vector(X[-c(1:5),]),plot=FALSE)$out))] <- 0
V[-c(1:5),] <- Vtmp
resSTEM <- stEM(X, V)
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