```
context("combinedist")
test_that('combinedist works in a simple case', {
# simulate MVN, 100 individuals, 40 measurements (of which 20 are just noise)
V <- matrix(0.3, ncol=20, nrow=20) + diag(rep(0.5, 20))
D <- chol(V)
z <- matrix(rnorm(20*100), ncol=20) %*% D
# create three data matrices as z + noise
x <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))
y <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))
w <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))
# permute some rows of x
x[51:53,] <- x[c(52,53,51),]
# add column and row names
dimnames(x) <- dimnames(y) <- dimnames(w) <-
list(paste("ind", 1:100, sep=""), paste("gene", 1:40, sep=""))
# calculate correlations between cols of x and of the other two matrices
corxy <- corbetw2mat(x, y)
corxw <- corbetw2mat(x, w)
# using columns with corr > 0.75,
# calculate distance (using "correlation" as a measure...really similarity)
dxy <- distee(x[,corxy>0.75], y[,corxy>0.75], d.method="cor", labels=c("x", "y"))
dxw <- distee(x[,corxw>0.75], w[,corxw>0.75], d.method="cor", labels=c("x", "w"))
expect_equivalent(combinedist(dxy, dxw), (dxy+dxw)/2)
})
```

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