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##### https://github.com/cran/CluMix
Tip revision: 4fbb09a
association.R
``````## calculate the respective association measure between two variables of arbitrary types
association <- function(x, y){

if(data.class(x) == "character")
x <- factor(x)
if(data.class(y) == "character")
y <- factor(y)

types <- c(data.class(x), data.class(y))

# get binary variables (can be any of numeric, factor, ordered, logic)
types[1] <- ifelse(length(na.omit(unique(x))) == 2, "binary", types[1])
types[2] <- ifelse(length(na.omit(unique(y))) == 2, "binary", types[2])

## if (at least) one variable is continuous
if(any(types == "numeric")){
first <- which(types == "numeric")[1]
second <- types[-first]
if(first == 2){
tmp <- x
x <- y
y <- tmp
}

# continuous - continuous/ordinal
if(second == "numeric" | second == "ordered")
res <- abs(cor(x, as.numeric(y), method="spearman", use="complete.obs"))

# continuous - categorical
else if(second == "factor")
res <- assoc.rank.cat(x, y)

# continuous - binary
else if(second == "binary")
res <- abs(myGKgamma(x, y))
}

## otherwise, if (at least) one variable is ordinal
else if(any(types == "ordered")){
first <- which(types == "ordered")[1]
second <- types[-first]
if(first == 2){
tmp <- x
x <- y
y <- tmp
}

# ordinal - ordinal
if(second == "ordered")
res <- abs(DescTools::GoodmanKruskalGamma(x, y))

# ordinal - categorical
else if(second == "factor")
res <- assoc.rank.cat(x, y)

# ordinal - binary
else if(second == "binary")
res <- abs(DescTools::GoodmanKruskalGamma(x, y))
}

## if both variables are categorical/binary
else
res <- assoc.cat.cat(x, y)

return(res)
}

``````