dda.vardist: Direction Dependence Analysis: Variable Distributions

Description

dda.vardist evaluates patterns of asymmetry of variable distributions for causally competing models (y ~ x vs. x ~ y).

print returns DDA test statistics associated with dda.vardist objects.

Usage

dda.vardist(
  formula,
  pred = NULL,
  data = list(),
  B = 200,
  boot.type = "perc",
  conf.level = 0.95
)
# S3 method for dda.vardist
print(x, ...)

Value

An object of class dda.vardist containing the results of direction dependence tests of variable distributions.

An object of class dda.vardist.

Arguments

formula: Symbolic formula of the model to be tested or a lmobject.
pred: Variable name of the predictor which serves as the outcome in the alternative model.
data: An optional data frame containing the variables in the model (by default variables are taken from the environment which dda.vardist is called from).
B: Number of bootstrap samples.
boot.type: A character indicating the type of bootstrap confidence intervals. Must be one of the two specifications c("perc", "bca"). boot.type = "perc" is the default.
conf.level: Confidence level for bootstrap confidence intervals.
x: An object of class dda.vardist when using print.
...: Additional arguments to be passed to the function.

References

Wiedermann, W., & von Eye, A. (2025). Direction Dependence Analysis: Foundations and Statistical Methods. Cambridge, UK: Cambridge University Press.

Examples

Run this code

set.seed(123)
n <- 500

x <- rchisq(n, df = 4) - 4
e <- rchisq(n, df = 3) - 3
y <- 0.5 * x + e
d <- data.frame(x, y)

result <- dda.vardist(y ~ x, pred = "x", data = d, B = 50)

print(result)