catdesc | R Documentation |

Measures the association between a categorical variable and some continuous and/or categorical variables

catdesc(y, x, weights=rep(1,length(y)), min.phi=NULL, robust=TRUE, nperm=NULL, distrib="asympt", dec=c(3,3,3,3,1,3))

`y` |
the categorical variable to describe (must be a factor) |

`x` |
a data frame with continuous and/or categorical variables |

`weights` |
an optional numeric vector of weights (by default, a vector of 1 for uniform weights) |

`min.phi` |
for the relationship between y and a categorical variable, only associations higher or equal to min.phi will be displayed. If NULL (default), they are all displayed. |

`robust` |
logical. If FALSE, mean and standard deviation are used instead of median and mad. Default is TRUE. |

`nperm` |
numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. |

`distrib` |
the null distribution of permutation test of independence can be approximated by its asymptotic distribution ( |

`dec` |
vector of 6 integers for number of decimals. The first value if for association measures, the second for permutation p-values, the third for percents, the fourth for phi coefficients, the fifth for medians and mads, the sixth for point biserial correlations. Default is c(3,3,3,3,1,3). |

A list of the following items :

`variables` |
associations between y and the variables in x |

`bylevel` |
a list with one element for each level of y |

Each element in bylevel has the following items :

`categories` |
a data frame with categorical variables from x and associations measured by phi |

`continuous.var` |
a data frame with continuous variables from x and associations measured by correlation coefficients |

Nicolas Robette

Rakotomalala R., 'Comprendre la taille d'effet (effect size)', [http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf]

`catdes`

, `condesc`

, `assoc.yx`

, `darma`

data(Movies) catdesc(Movies$ArtHouse, Movies[,c("Budget","Genre","Country")])

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