Is there an R function that will compute the cosine dissimilarity matrix?

Question

I would like to make a heatmap with row clustering based on cosine distances. I'm using R and heatmap.2() for making the figure. I can see that there's a dist parameter in heatmap.2 but I cannot find a function to generate the cosine dissimilarity matrix. The builtin dist function doesn't support cosine distances, I also found a package called arules with a dissimilarity() function but it only works on binary data.

Answer 1

As @Max indicated in the comments (+1) it would be simpler to "write your own" than to spend time looking for it somewhere else. As we know, the cosine similarity between two vectors $A,B$ of length $n$ is

$$ C = \frac{ \sum \limits_{i=1}^{n}A_{i} B_{i} }{ \sqrt{\sum \limits_{i=1}^{n} A_{i}^2} \cdot \sqrt{\sum \limits_{i=1}^{n} B_{i}^2} } $$

which is straightforward to generate in R. Let X be the matrix where the rows are the values we want to compute the similarity between. Then we can compute the similarity matrix with the following R code:

cos.sim <- function(ix) 
{
    A = X[ix[1],]
    B = X[ix[2],]
    return( sum(A*B)/sqrt(sum(A^2)*sum(B^2)) )
}   
n <- nrow(X) 
cmb <- expand.grid(i=1:n, j=1:n) 
C <- matrix(apply(cmb,1,cos.sim),n,n)

Then the matrix C is the cosine similarity matrix and you can pass it to whatever heatmap function you like (the only one I'm familiar with is image()).