## -----------------------------------------------------------------------------
library(rankMANOVA)
data("marketing")
mymar <- marketing[, c("Sex", "Income", "Edu", "Language")]
mymar2 <- na.omit(mymar)

# introduce nicer labels
mymar2$Sex <- factor(mymar2$Sex, labels = c("M", "F"))
mymar2$Language <- factor(mymar2$Language, labels = c("English", "Spanish", "Other"))
test1 <- rankMANOVA(cbind(Income, Edu) ~ Sex*Language , data = mymar2,
                    iter=1000, resampling = "bootstrap", seed = 1409,
                    CPU =1)
summary(test1)

## -----------------------------------------------------------------------------
male <- mymar2[mymar2$Sex == "M", ]
ph <- univariate(test1, factor = "Language", data = male)
ph

## -----------------------------------------------------------------------------
# since we consider only male participants:
m1 <- rankMANOVA(cbind(Income, Edu) ~ Language, data = male, iter = 1000,
                 seed = 290,
                 CPU = 1)
pairwise(m1, type = "Tukey", factor = "Language", uni = TRUE)

## -----------------------------------------------------------------------------
if (requireNamespace("GFD", quietly = TRUE)) {
library(GFD)
data(curdies)
set.seed(123)
curdies$dug2 <- curdies$dugesia + rnorm(36)

fit1 <- rankMANOVA(cbind(dugesia, dug2) ~ season + season:site, data = curdies, iter = 1000, nested.levels.unique = TRUE,
                   seed = 123,
                   CPU = 1)
summary(fit1)
}