Performs a within-cluster descriptive analysis of the variables after the
clustering process performed by the function miclust.
# S3 method for miclust
summary(object, k = NULL, quantilevars = NULL, ...)object of class miclust obtained with the function miclust.
number of clusters. The default value is the optimal number of clusters
obtained by miclust.
numeric. If a variable selection procedure was used, the
cut-off percentile in order to decide the number of selected variables in the
variable reduction procedure by decreasing order of presence along the imputations
results. The default value is quantilevars = 0.5, i.e., the number of
selected variables is the median number of selected variables along the imputations.
further arguments for the plot function.
An object with classes c("list", "summary.miclust") including the following items:
if imputations were analyzed, descriptive summary of the probability of cluster assignment.
if imputations were analyzed, the individual probabilities of cluster assignment.
if imputations were analyzed, the final individual cluster assignment.
if imputations were analyzed, size of the imputed cluster and between-imputations summary of the cluster size.
if a single data set (raw data set) has been clustered, a vector containing the individuals cluster assignments.
if imputed data sets have been clustered, the individual cluster assignment in each imputation.
if a single data set (raw data set) has been clustered, the percentage of complete cases in the data set.
number of clusters.
if imputations were analyzed, the Cohen's kappa values after comparing the cluster vector in the first imputation with the cluster vector in each of the remaining imputations.
a summary of kappas.
number of imputations used in the descriptive analysis which is the total number of imputations provided.
if variable selection was performed, the input value
of quantilevars.
search algorithm for the selection variable procedure.
if variable selection was performed, the selected
variables obtained considering quantilevars.
if imputations were analyzed and variable selection was performed, the presence of the selected variables along imputations.
within-cluster descriptive analysis of the selected variables.
indicator of imputations used in the clustering procedure.
# NOT RUN {
### see examples in miclust.
# }
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