Describes Associations as in a Regression Model Analysis.

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

darma(y, x, weights = NULL, target = 1,
      na.rm.cat = FALSE, na.value.cat = "NAs", na.rm.cont = FALSE,
      correlation = "kendall",
      nperm = NULL, distrib = "asympt", dec = c(1,3,3))

Arguments

y

the response variable

x

the predictor variables

weights

numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used.

target

rank or name of the category of interest when y is categorical

na.rm.cat

logical, indicating whether NA values in the categorical variables should be silently removed before the computation proceeds. If FALSE (default), an additional level is added to the categorical variables (see na.value.cat argument).

na.value.cat

character. Name of the level for NA category. Default is "NAs". Only used if na.rm.cat = FALSE.

na.rm.cont

logical, indicating whether NA values in the continuous variables should be silently removed before the computation proceeds. Default is FALSE.

correlation

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 ("asympt", default) or via Monte Carlo resampling ("approx").

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).

Details

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.

Value

A data frame

Author

Nicolas Robette

See also

assoc.yx, assoc.twocat, assoc.twocont, assoc.catcont, condesc, catdesc

Examples

  data(iris)
  iris2 = iris
  iris2$Species = factor(iris$Species == "versicolor")
  darma(iris2$Species, iris2[,1:4], target=2, nperm=100)
#>       variable category percent association perm.pvalue
#> 1 Sepal.Length               NA       0.079       0.475
#> 2  Sepal.Width               NA      -0.468       0.000
#> 3 Petal.Length               NA       0.202       0.000
#> 4  Petal.Width               NA       0.118       0.230