qad (version 0.1.2)

pairwise.qad: Pairwise measures of (asymmetric) 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,
  permutation = FALSE,
  nperm = 10,
  DoParallel = TRUE,
  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.

permutation

a logical indicating whether a permutated p-value is computed.

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 6 data.frames with the dependence measures and corresponding p.values. The output of pairwise.qad can be illustrated with 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 (Not Run)
#model <- pairwise.qad(sample_df, permutation = TRUE, nperm = 20, DoParallel = TRUE)
#heatmap.qad(model, select = "dependence", fontsize = 20, significance = TRUE)
# }