assoc.twocat | R Documentation |

Cross-tabulation and measures of association between two categorical variables

assoc.twocat(x,y,weights=rep.int(1,length(x)),na_value=NULL,nperm=NULL,distrib="asympt")

`x` |
the first categorical variable (must be a factor) |

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

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

`na_value` |
character. Name of the level for NA category. If NULL (default), NA values are ignored. |

`nperm` |
numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. Default is 1000. |

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

A list with the following elements :

`freq` |
cross-tabulation |

`prop` |
percentages |

`rprop` |
row percentages |

`cprop` |
column percentages |

`expected` |
expected values |

`chi.squared` |
chi-squared value |

`cramer.v` |
Cramer's V between the two variables |

`permutation.pvalue` |
p-value from a permutation (so non-parametric) test of independence |

`pearson.residuals` |
the table of Pearson residuals, i.e. (observed - expected) / sqrt(expected). |

`phi` |
the table of the phi coefficients for each pair of levels |

`phi.perm.pval` |
the table of permutation p-values for each pair of levels |

`gather` |
a data frame gathering informations, with one row per cell of the cross-tabulation |

Nicolas Robette

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

`assoc.catcont`

, `assoc.twocont`

, `assoc.yx`

, `condesc`

,
`catdesc`

, `darma`

, `ggassoc_crosstab`

, `ggassoc_phiplot`

data(Music) assoc.twocat(Music$Jazz,Music$Age,nperm=100)

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