Description Usage Arguments Value References See Also Examples

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.

1 2 |

`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 |

`...` |
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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
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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