CalcGroupPsiCWS: The Psi CWS statistic for aggregated group data

In EricMcKinney77/distdiffR: Two-Sample Tests of Distributional Equality for Bivariate Data

The Psi CWS statistic for aggregated group data

Description

This statistic computes the complementary weighted squared (CWS) differences between the averaged subject empirical cumulative distribution functions for the two samples. For more information, see \insertCitemckinney2022extensions;textualdistdiffR and \insertCitemckinney2021extensions;textualdistdiffR.

Usage

CalcGroupPsiCWS(data, groups, subjects)

Arguments

data	A two column matrix of the bivariate pooled samples
groups	A numeric vector of sample (or group) labels (use either 1 or 2)
subjects	A numeric vector of subject labels

Value

The Psi CWS statistic for aggregated group data

References

\insertRef

mckinney2022extensionsdistdiffR

\insertRef

mckinney2021extensionsdistdiffR

Examples

# Randomly assign all three species to two samples
data(iris)
iris$Species <- rep(1:3, each = 50) # Species will serve as the subject label
irisPermuted <- iris[sample.int(nrow(iris)), ]
sample1 <- as.matrix(irisPermuted[1:75, c(1:2, 5)])
sample2 <- as.matrix(irisPermuted[76:150, c(1:2, 5)])
pooled_data <- rbind(cbind(sample1, 1), cbind(sample2, 2))

CalcGroupPsiCWS(pooled_data[, 1:2], pooled_data[, 4], pooled_data[, 3])

EricMcKinney77/distdiffR documentation built on April 24, 2022, 9:03 p.m.