plot.cluspca | R Documentation |
cluspca()
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, it returns a parallel coordinate plot showing cluster means.
## S3 method for class 'cluspca' plot(x, dims = c(1, 2), cludesc = FALSE, what = c(TRUE,TRUE), attlabs, max.overlaps=10, ...)
x |
Object returned by |
dims |
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 |
what |
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 |
cludesc |
A logical value indicating if a parallel coordinate plot showing cluster means is produced (default = |
attlabs |
Vector of custom attribute labels; if not provided, default labeling is applied |
max.overlaps |
Maximum number of text labels allowed to overlap. Defaults to 10 |
... |
Further arguments to be transferred to |
The function returns a ggplot2 scatterplot of the solution obtained via cluspca()
that can be further customized using the ggplot2 package. When cludesc = TRUE
the function also returns a ggplot2 parallel coordinate plot.
De Soete, G., and Carroll, J. D. (1994). K-means clustering in a low-dimensional Euclidean space. In Diday E. et al. (Eds.), New Approaches in Classification and Data Analysis, Heidelberg: Springer, 212-219.
Vichi, M., and Kiers, H.A.L. (2001). Factorial K-means analysis for two-way data. Computational Statistics and Data Analysis, 37, 49-64.
plot.clusmca, plot.cluspcamix
data("macro") #Factorial K-means (3 clusters in 2 dimensions) after 100 random starts outFKM = cluspca(macro, 3, 2, method = "FKM", rotation = "varimax") #Scatterplot (dimensions 1 and 2) and cluster description plot plot(outFKM, cludesc = TRUE) data("iris", package = "datasets") #Compromise solution between PCA and Reduced K-means #on the iris dataset (3 clusters in 2 dimensions) after 100 random starts outclusPCA = cluspca(iris[,-5], 3, 2, alpha = 0.3, rotation = "varimax") table(outclusPCA$cluster,iris[,5]) #Save the ggplot2 scatterplot map = plot(outclusPCA)$map #Customization (adding titles) map + ggtitle(paste("A compromise solution between RKM and FKM on the iris: 3 clusters of sizes ", paste(outclusPCA$size, collapse = ", "),sep = "")) + xlab("Dimension 1") + ylab("Dimension 2") + theme(plot.title = element_text(size = 10, face = "bold", hjust = 0.5))
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