darma | R Documentation |

Computes bivariate association measures between a response and predictor variables, producing a summary looking like a regression analysis.

darma(y, x, weights=rep(1,length(y)), target=1, twocont="kendall", nperm=NULL, distrib="asympt", dec=c(1,3,3))

`y` |
the response variable |

`x` |
the predictor variables |

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

`target` |
rank or name of the category of interest when y is categorical |

`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 3 integers for number of decimals. The first value if for percents or medians, the second for association measures, the third for permutation p-values. Default is c(1,3,3). |

The function computes association measures (phi, correlation coefficient, Kendall's correlation) between the variable of interest and the other variables. It can also compute the p-values permutation tests.

A data frame

Nicolas Robette

`assoc.yx`

, `assoc.twocat`

, `assoc.twocont`

, `assoc.catcont`

, `condesc`

, `catdesc`

data(iris) iris2 = iris iris2$Species = factor(iris$Species == "versicolor") darma(iris2$Species, iris2[,1:4], target=2, nperm=100)

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