Dear all,
I've sent this question 2 days ago and got response from Sarah. Thanks for
that. But unfortunately, it did not really solve our problem. The main issue
is that we want to use our own (manipulated) covariance matrix in the
calculation of the mahalanobis distance. Does anyone know how to vectorize
the below code instead of using a loop (which slows it down)?
I'd really appreciate any help on this, thank you all in advance!
Cheers,
Frank
This is what I posted 2 days ago:
We have a data frame x with n people as rows and k variables as columns.
Now, for each person (i.e., each row) we want to calculate a distance
between him/her and EACH other person in x. In other words, we want to
create a n x n matrix with distances (with zeros in the diagonal).
However, we do not want to calculate Euclidian distances. We want to
calculate Mahalanobis distances, which take into account the covariance
among variables.
Below is the piece of code we wrote ("covmat" in the function below is the
variance-covariance matrix among variables in Data that has to be fed into
mahalonobis function we are using).
mahadist = function(x, covmat) {
dismat = matrix(0,ncol=nrow(x),nrow=nrow(x))
for (i in 1:nrow(x)) {
dismat[i,] = mahalanobis(as.matrix(x), as.matrix(x[i,]), covmat)^.5
}
return(dismat)
}
This piece of code works, but it is very slow. We were wondering if it's at
all possible to somehow vectorize this function. Any help would be greatly
appreciated.
Thanks,
Frank
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