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Tip revision: **a006880878209b1a96d9cdde0332d96fa86036af** authored by ** Manuela Hummel ** on **03 June 2016, 18:47:22 UTC**

**version 1.1**

Tip revision: **a006880**

similarity.variables.R

```
similarity.variables <-
function(data, associationFun=association, check.psd=TRUE, make.psd=TRUE){
# data: data.frame of original data
# associationFun: function that calculates association measure for each pair of variables
# check.psd: check if resulting similarity matrix S is positive semi-definite?
# make.psd: if S is not p.s.d., shall it be transformed to be p.s.d.? (only done if also check.psd=TRUE)
#n <- nrow(data)
p <- ncol(data)
S <- matrix(0, nrow=p, ncol=p)
for(i in 1:p){
for(j in 1:p){
if(i > j){
# distance = sqrt(1 - association)
S[i,j] <- associationFun(data[,i], data[,j])
}
}
}
dimnames(S) <- list(names(data), names(data))
# make it symmetric (since only lower triangle was calculated)
S <- S + t(S)
diag(S) <- 1
# check if S is p.s.d.
if(check.psd){
psd <- all(eigen(S, only.values=TRUE)$values >= 0)
# if S is not p.s.d., get "nearest p.s.d. matrix"
if(!psd){
if(!make.psd)
warning("similarity matrix is not positive semidefinite")
else{
S <- Matrix::nearPD(S, keepDiag=TRUE, conv.norm.type="F")$mat
#warning("similarity matrix was adjusted to be positive semidefinite")
}
}
}
return(as.matrix(S))
}
```