Hi,
Let X = (x_1, x_2, ... , x_p) be multivariate normal with mean, mu = (mu_1,
... , mu_p) and covariance = Sigma. I was looking for an R function to compute
conditional mean and conditional variance of a given subset of X given another
subset of X. While this is trivially easy to do, there is nothing in "base"
for doing this, at least nothing that I am aware of. I am also not aware of
anything in the contributed packages (although my search was not
comprehensive). I feel that this would be a useful addition, if it is not
already there. I have written this following function, which I am sure can be
improved a lot (including better argument names!). I would like to hear your
thought on this.
condNormal <- function(x.given, mu, sigma, req.ind, given.ind){
# Returns conditional mean and variance of x[req.ind]
# Given x[given.ind] = x.given
# where X is multivariate Normal with
# mean = mu and covariance = sigma
#
B <- sigma[req.ind, req.ind]
C <- sigma[req.ind, given.ind]
D <- sigma[given.ind, given.ind]
cMu <- drop(mu[req.ind] + C %*% solve(D) %*% (x.given - mu[given.ind]))
cVar <- B - C %*% solve(D) %*% t(C)
list(condMean=cMu, condVar=cVar)
}
n <- 10
A <- matrix(rnorm(n^2), n, n)
A <- A %*% t(A)
condNormal(x=c(1,1,0,0,-1), mu=rep(1,n), sigma=A, req=c(2,3,5),
given=c(1,4,7,9,10))
Best regards,
Ravi
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