##### https://github.com/cran/CluMix

Tip revision:

**4fbb09ab94eb59bfa4196e2a4898f4e30c2845ab**authored by**Manuela Hummel**on**21 January 2019, 08:10 UTC****version 2.3.1** 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)
}
```