vimpclust: Variable Importance in Clustering

#' @title Mice Protein Expression Data Set
#'
#' @description The data set consists of the expression levels of 68 proteins that produced detectable signal in the nuclear fraction of 
#' cortex for a sample of 72 mice. There are 38 control mice and 34 trisomic mice. Several measurements were recorded for each protein
#' and for each mouse. The measurements containing missing observations in the original data were suppressed, so that one has between
#' 12 and 15 measurements per protein and per mouse. 
#' 
#' Mice may be further described based on the treatment they received (injected with memantine or saline), and on their behaviour 
#' (stimulated to learn or not). 
#'
#'@format A data frame of 72 rows (mice) and 905 columns (variables):
#'@field Protein_X_meas_Y Numerical. The expression level for protein X at measurement Y. X has values between 1 and 68, Y has values
#'   between 1 and 12 or 15, according to the number of measurements.
#'@field Genotype Categorical. Two values: "Control" and "Ts65Dn" (trisomic mouse).
#'@field Treatment Categorical. Two values: "Memantine" and "Saline".
#'@field Behaviour Categorical. Two values: "C/S" (stimulated to learn) and "S/C" (not stimulated to learn).
#'@field Class.mouse Categorical. This variables creates eight classes of mice, based on crossing the categories of \code{Genotype},
#'   \code{Behaviour} and \code{Treatment}.
#'@field MouseID Factor. The key variable identifying each mouse in the sample.
#' @source \url{https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression}
#' @references C. Higuera, K.J. Gardiner, and K.J. Cios (2015) Self-organizing feature maps identify proteins critical 
#' to learning in a mouse model of Down syndrome. PLoS ONE 10(6): e0129126.
"DataMice"