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Tip revision: **dd414a45be07f9c033c980461d041111b898f0a7** authored by ** Manuela Hummel ** on **29 December 2016, 10:52:10 UTC**

**version 1.3.1**

Tip revision: **dd414a4**

dist.subjects.R

```
dist.subjects <-
function(data, weights){
#function(data, type=list()){
# !! to be done: allow also asymmetric binary variables
# variable classes (binary can be any of numeric, factor, ordered, logic)
#dc <- sapply(data, function(x) ifelse(length(na.omit(unique(x))) == 2, "binary", data.class)
# if all variables are numeric, use Euclidean distance
dc <- sapply(data, data.class)
if(all(dc == "numeric"))
D <- dist(data)
# if not, use Gower's distance with Podani's extension
else{
# !! depending on type, define asymmetric binary variables for parameter asym.bin
# binary variables have to be numeric
K <- sapply(data[,dc == "factor", drop=FALSE], function(x) length(levels(x)))
bin <- names(K)[K == 2]
data[,bin] <- sapply(data[,bin], function(x) as.numeric(x) - 1)
# in case there are logical variables
if(any(dc == "logical"))
data[,dc == "logical"] <- sapply(data[,dc == "logical"], as.numeric)
D <- FD::gowdis(x=data, w=weights, ord="metric") # asym.bin=!!
D <- sqrt(D) # gowdis calculates D = 1-S, but we want D = sqrt(1-S)
}
return(D)
}
```