tclustfsda
An object of class tclustfsda.object
holds information about
the result of a call to tclustfsda
.
The functions print()
and summary()
are used to obtain and print a
summary of the results. An object of class tclustfsda
is a list containing at least the following components:
the matched call
a k-by-p matrix containing cluster centroid locations. Robust estimate of final centroids of the groups
a p-by-p-by-k array rray containing estimated constrained covariance for the k groups
a vector of length n containing assignment of each unit to each of the k groups. Cluster names are integer numbers from 1 to k. 0 indicates trimmed observations.
a matrix of size (k+1)-by-3. The 1st col is sequence from 0 to k (cluster name); the 2nd col is the number of observations in each cluster; the 3rd col is the percentage of observations in each cluster.
Remark: 0 denotes unassigned units.
n-by-k matrix containing posterior probabilities. postprob[i, j]
contains posterior probabilitiy of unit i
from component (cluster) j
. For the trimmed units posterior probabilities are 0.
"Empirical" statistics computed on final classification. When convergence is reached, emp=0
. When convergence is not obtained, this field is a list which contains the statistics of interest: idxemp
(ordered from 0 to k*, k* being the number of groups with at least one observation and 0 representing the possible group of outliers), muemp
, sigmaemp
and sizemp
, which are the empirical counterparts of idx
, muopt
, sigmaopt
and \codesize.
BIC which uses parameters estimated using the mixture loglikelihood and the maximized mixture likelihood as goodness of fit measure.
Remark: this output is present just if mixt > 0
.
BIC which uses parameters estimated using the mixture loglikelihood and the maximized mixture likelihood as goodness of fit measure.
Remark: this output is present just if mixt > 0
.
BIC which uses the classification likelihood based on parameters estimated using the classification likelihood.
Remark: this output is present just if mixt > 0
.
number of subsets without convergence
a vector of length k
containing the units forming initial subset associated with muopt.
value of the objective function which is minimized (value of the best returned solution).
if equalweights=TRUE
means that in the clustering procedure we (ideally) assumed equal cluster weights else (codeequalweitghts=FALSE means that we allowed for different cluster sizes.
number of observations that have determined the centroids (number of untrimmed units).
a vector of size nsamp
which contains the value of the objective function at the end of the iterative process for each extracted subsample.
the original data matrix X.
# NOT RUN {
# }
# NOT RUN {
data(hbk)
(out <- tclustfsda(hbk[, 1:3], k=2))
class(out)
summary(out)
# }
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