condesc | R Documentation |

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

condesc(y, x, weights=rep(1,length(y)), min.cor=NULL, robust=TRUE, nperm=NULL, distrib="asympt", dec=c(3,3,0,3))

`y` |
the continuous variable to describe |

`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.cor` |
for the relationship between y and a categorical variable, only associations higher or equal to min.cor 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 4 integers for number of decimals. The first value if for association measures, the second for permutation p-values, the third for medians and mads, the fourth for point biserial correlations. Default is c(3,3,0,3). |

A list of the following items :

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

`categories` |
a data frame with categorical variables from x and associations measured by point biserial correlation |

Nicolas Robette

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

`condes`

, `catdesc`

, `assoc.yx`

, `darma`

data(Movies) condesc(Movies$BoxOffice, Movies[,c("Budget","Genre","Country")])

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