cluspcamix()
output.Plotting function that creates a scatterplot of the objects, a correlation circle of the variables or a biplot of both objects and variables. Optionally, for metric variables, it returns a parallel coordinate plot showing cluster means and for categorical variables, a series of barplots showing the standardized residuals per attribute for each cluster.
# S3 method for cluspcamix
plot(x, dims = c(1, 2), cludesc = FALSE,
topstdres = 20, objlabs = FALSE, attlabs = NULL, attcatlabs = NULL,
subplot = FALSE, what = c(TRUE,TRUE), max.overlaps = 10, ...)
The function returns a ggplot2 scatterplot of the solution obtained via cluspcamix()
that can be further customized using the ggplot2 package. When cludesc = TRUE
, for metric variables, the function also returns a ggplot2 parallel coordinate plot and for categorical variables, a series of ggplot2 barplots showing the largest (or all) standardized residuals per attribute for each cluster.
Object returned by cluspcamix()
Numerical vector of length 2 indicating the dimensions to plot on horizontal and vertical axes respectively; default is first dimension horizontal and second dimension vertical
Vector of two logical values specifying the contents of the plots. First entry indicates whether a scatterplot of the objects and cluster centroids is displayed and the second entry whether a correlation circle of the variables is displayed. The default is c(TRUE, TRUE)
and the resultant plot is a biplot of both objects and variables
A logical value indicating if a parallel coordinate plot showing cluster means is produced (default = FALSE)
Number of largest standardized residuals used to describe each cluster (default = 20). Works only in combination with cludesc = TRUE
A logical value indicating whether a subplot with the full distribution of the standardized residuals will appear at the bottom left corner of the corresponding plots. Works only in combination with cludesc = TRUE
A logical value indicating whether object labels will be plotted; if TRUE
row names of the data matrix are used (default = FALSE
). Warning: when TRUE
, execution time of the plotting function will increase dramatically as the number of objects gets larger
Vector of custom labels of continuous attributes; if not provided, default labeling is applied
Vector of custom labels of categorical attributes (categories); if not provided, default labeling is applied
Maximum number of text labels allowed to overlap. Defaults to 10
Further arguments to be transferred to cluspcamix()
van de Velden, M., Iodice D'Enza, A., & Markos, A. (2019). Distance-based clustering of mixed data. Wiley Interdisciplinary Reviews: Computational Statistics, e1456.
Vichi, M., Vicari, D., & Kiers, H. A. L. (2019). Clustering and dimension reduction for mixed variables. Behaviormetrika. doi:10.1007/s41237-018-0068-6.
plot.clusmca, plot.cluspca
data(diamond)
#Mixed Reduced K-means solution with 3 clusters in 2 dimensions
#after 10 random starts
outmixedRKM = cluspcamix(diamond, 3, 2, method = "mixedRKM", nstart = 10)
#Scatterplot (dimensions 1 and 2)
plot(outmixedRKM, cludesc = TRUE)
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