cucconi.test <- function(x, y, method = c("permutation", "bootstrap")){
# Implementation of the Cucconi test for the two-sample location-scale problem
# A permutation/bootstrap distribution of the test statistic (C) under the
# null hypothesis is used to calculate the p-value.
# Reference: Marozzi (2013), p. 1302-1303
m <- length(x)
n <- length(y)
C <- cucconi.teststat(x = x, y = y, m = m, n = n)
if(method[1] == "permutation"){
h0dist <- cucconi.dist.perm(x = x, y = y)
}
if(method[1] == "bootstrap"){
h0dist <- cucconi.dist.boot(x = x, y = y)
}
p.value <- length(h0dist[h0dist >= C]) / length(h0dist)
cat("\nCucconi two-sample location-scale test\n")
cat("\nNull hypothesis: The locations and scales of the two population distributions are equal.\n")
cat("Alternative hypothesis: The locations and/or scales of the two population distributions differ.\n")
cat(paste("\nC = ", round(C, 3), ", p-value = ", round(p.value, 4), "\n\n", sep=""))
return(list(C = C,
method = method[1],
p.value = p.value))
}
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