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, 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.

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`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 = |

`topstdres` |
Number of largest standardized residuals used to describe each cluster (default = 20). Works only in combination with |

`subplot` |
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 |

`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 `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.

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.

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