assoc.yx | R Documentation |

Computes bivariate association measures between a response and predictor variables (and, optionnaly, between every pairs of predictor variables.)

assoc.yx(y, x, weights=rep(1,length(y)), xx = TRUE, twocont="kendall", nperm=NULL, distrib="asympt", dec=c(3,3))

`y` |
the response variable |

`x` |
the predictor variables |

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

`xx` |
whether the association measures should be computed for couples of predictor variables (default) or not. With a lot of predictors, consider setting xx to FALSE (for reasons of computation time). |

`twocont` |
character. The type of measure of correlation measure to use between two continuous variables : "pearson", "spearman" or "kendall" (default). |

`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 2 integers for number of decimals. The first value if for association measures, the second for permutation p-values. Default is c(3,3). |

The function computes an association measure : Pearson's, Spearman's or Kendall's correlation for pairs of numeric variables, Cramer's V for pairs of factors and eta-squared for pairs numeric-factor. It can also compute the p-value of a permutation test of association for each pair of variables.

A list of the following items :

`YX` |
: a table with the association measures between the response and predictor variables |

`XX` |
: a table with the association measures between every pairs of predictor variables |

In each table :

`measure` |
: name of the association measure |

`association` |
: value of the association measure |

`permutation.pvalue` |
: p-value from the permutation test |

Nicolas Robette

`darma`

, `assoc.twocat`

, `assoc.twocont`

, `assoc.catcont`

, `condesc`

, `catdesc`

data(iris) iris2 = iris iris2$Species = factor(iris$Species == "versicolor") assoc.yx(iris2$Species,iris2[,1:4],nperm=100)

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