I use the following function which does not uses loops and seems to be
pretty fast:
dmvnorm <- function (x, mu, Sigma, df, log = FALSE) {
if (!is.matrix(x))
x <- rbind(x)
p <- nrow(Sigma)
ed <- eigen(Sigma, symmetric = TRUE)
ev <- ed$values
if (!all(ev >= -1e-06 * abs(ev[1])))
stop("'Sigma' is not positive definite")
ss <- if (!is.matrix(mu)) {
x - rep(mu, each = nrow(x))
} else {
x - mu
}
inv.Sigma <- ed$vectors %*% (t(ed$vectors)/ev)
quad <- 0.5 * rowSums((ss %*% inv.Sigma) * ss)
fact <- -0.5 * (p * log(2 * pi) + sum(log(ev)))
if (log)
as.vector(fact - quad)
else
as.vector(exp(fact - quad))
}
I hope it helps.
Best,
Dimitris
On 6/25/2011 3:58 PM, zerfetzen wrote:
Does anyone know of a package that uses C code to calculate a multivariate
normal density?
My goal is to find a faster way to calculate MVN densities and avoid R loops
or apply functions, such as when X and mu are N x K matrices, as opposed to
vectors, and in this particular case, speed really matters. I would like to
be able to use .C or .Call to pass X, mu, Sigma, and N to a C program and
have it return a vector of log densities to R.
I'm new to putting C in R, but am sure I'll figure it out. Thanks for any
suggestions.
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
Web: http://www.erasmusmc.nl/biostatistiek/
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