[Rd] Conditional means and variances of a multivariate normal distribution
Ravi Varadhan
rvaradhan at jhmi.edu
Mon Mar 5 06:07:14 CET 2012
Hi,
My previous version of the conditional MVN function had a bug in that it would not work when conditional distribution was required for a single variable. I fixed this and also made a few minor changes. Here is the new version.
condNormal <- function(x.given, mu, sigma, given.ind, req.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, drop=FALSE]
D <- sigma[given.ind, given.ind]
CDinv <- C %*% solve(D)
cMu <- c(mu[req.ind] + CDinv %*% (x.given - mu[given.ind]))
cVar <- B - CDinv %*% 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))
condNormal(x=c(1,1,0,0,-1), mu=rep(1,n), sigma=A, req=2, given=c(1,4,7,9,10))
As far as I know, there is nothing related to multivariate normal distributions in "stats". Hence, it seems like this function might be more useful in a contributed package such as "fMultivar" or "mvtnorm".
Best,
Ravi.
________________________________________
From: r-devel-bounces at r-project.org [r-devel-bounces at r-project.org] on behalf of Ravi Varadhan [rvaradhan at jhmi.edu]
Sent: Sunday, March 04, 2012 10:32 AM
To: r-devel at r-project.org
Subject: [Rd] Conditional means and variances of a multivariate normal distribution
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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