View source: R/ggcloud_variables.R

ggcloud_variables | R Documentation |

Plots a Multiple Correspondence Analysis cloud of variables, using ggplots functions.

ggcloud_variables(resmca, axes=c(1,2), points='all', min.ctr=NULL, max.pval=0.01, face="pp", shapes=TRUE, prop=NULL, textsize=3, shapesize=3, col=NULL, palette=NULL, col.by.group=TRUE, alpha=1, segment.alpha=0.5, vlab=TRUE, sep='.', legend='right')

`resmca` |
object of class |

`axes` |
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). |

`points` |
character string. If 'all' all categories are plotted (default); if 'besth' only those who contribute most to horizontal axis are plotted; if 'bestv' only those who contribute most to vertical axis are plotted; if 'best' only those who contribute most to horizontal or vertical axis are plotted. |

`min.ctr` |
Numerical value between 0 and 100. The minimum contribution (in percent) for a category to be displayed if the |

`max.pval` |
Numerical value between 0 and 100. The maximal p-value derived from test-values for a category to be displayed if the |

`face` |
character string. Changes the face of the category labels when their contribution is greater than Translated with www.DeepL.com/Translator (free version) |

`shapes` |
Logical. Should shapes be plotted for categories (in addition to labels) ? Default is TRUE. |

`prop` |
If NULL, the size of the labels (if shapes=FALSE) or the shapes (if shapes=TRUE) is constant. If 'n', the size is proportional the the weights of categories; if 'ctr1', the size is proportional to the contributions of the categories on the first dimension of the plot; if 'ctr2', the size is proportional to the contributions of the categories on the second dimension of the plot; if 'ctr.cloud', the size is proportional to the total contributions of the categories on the whole cloud; if 'cos1', the size is proportional to the cosines of the categories on the first dimension of the plot; if 'cos2', the size is proportional to the cosines of the categories on the second dimension of the plot; if 'cos12', the size is proportional to the total cosines of the categories on the two dimensions of the plot; if 'vtest1', the size is proportional to the test-values of the categories on the first dimension of the plot; if 'vtest2', the size is proportional to the test-values of the categories on the second dimension of the plot. |

`textsize` |
Size of the labels of categories if shapes=TRUE, or if shapes=FALSE and prop=NULL. Default is 3. |

`shapesize` |
Size if the shapes of categories if shapes=TRUE and prop=FALSE. Default is 3. |

`col` |
Character. A unique color for the shapes and labels of the categories. Default is NULL, which means a palette will be used instead of a unique color (see |

`palette` |
Character string or character vector. Only used if |

`col.by.group` |
Logical. If |

`alpha` |
Transparency of the shapes and labels of categories. Default is 1. |

`segment.alpha` |
Transparency of the line segment beside labels of categories. Default is 0.5. |

`vlab` |
Logical. Should the variable names be used as a prefix for the labels of the categories. Default is TRUE. |

`sep` |
Character string used as a separator if vlab=TRUE. |

`legend` |
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right. |

a ggplot object

Anton Perdoncin, Nicolas Robette

Le Roux B. and Rouanet H., *Multiple Correspondence Analysis*, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).

Le Roux B. and Rouanet H., *Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis*, Kluwer Academic Publishers, Dordrecht (June 2004).

`ggcloud_indiv`

, `ggadd_supvar`

, `ggadd_ellipses`

, `ggadd_corr`

, `ggadd_interaction`

, `ggadd_density`

## Performs a specific MCA on 'Music' example data set ## ignoring every 'NA' (i.e. 'not available') categories, ## and then draws the cloud of categories. data(Music) getindexcat(Music[,1:5]) mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15)) ggcloud_variables(mca) ggcloud_variables(mca, points='best', prop='n', palette='Set2') ggcloud_variables(mca, shapes=FALSE, legend="none", col="black", face="ui")

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