qad (version 0.2.0)

pairwise.qad: Pairwise quantification of (asymmetric and directed) dependencies

Description

Pairwise computation of the function qad(). qad() is applied on each pair of variables of a numeric data.frame.

Usage

pairwise.qad(
  data_df,
  resolution = NULL,
  remove.00 = FALSE,
  min.res = 3,
  permutation = FALSE,
  nperm = 1000,
  DoParallel = FALSE,
  registerC = registerDoParallel,
  ncores = NULL
)

Arguments

data_df

a data frame containing numeric columns with the observations of the sample.

resolution

an integer indicating the number of strips for the checkerboard aggregation (see emp_c_copula()). Default (NULL) uses the optimal resolution, computed out of the sample size.

remove.00

a logical indicating whether double 0 entries should be excluded (default = FALSE)

min.res

an integer indicating the necessary minimum resolution of the checkerboard grid to compute qad, otherwise the result is NA (default = 3).

permutation

a logical indicating whether a p-value (based on permutations) is computed; (otherwise the p-value is computed by MC-simulation - see pqad()).

nperm

an integer indicating the number of permutation runs.

DoParallel

a logical value indicating whether the permutation test is computed parallelized.

registerC

function to register the parallel backend. It is recommended to use registerDoParallel() of the doParallel package (default). Other option is for example on a linux based system to install the doMC package and use registerDoMC

ncores

an integer indicating the number of cores used for parallelization. Default (NULL) uses the maximum number of cores minus 1.

Value

a list, containing 8 data.frames with the dependence measures, corresponding p.values, the resolution of the checkerboard aggregation and the number of removed double zero entries (only if remove.00 = TRUE). The output of pairwise.qad() can be illustrated using the function heatmap.qad().

Examples

# NOT RUN {
n <- 100
x <- runif(n, 0, 1)
y <- runif(n, 0, 1)
z <- runif(n, 0, 1)
sample_df <- data.frame(x,y,z)

#qad
model <- pairwise.qad(sample_df, permutation = FALSE)
heatmap.qad(model, select = "dependence", fontsize = 5, significance = TRUE, sign.level = 0.05)
# }