Hello R-help,
I need to compute matrices of first derivatives of a covariance matrix C
with entries given by c_ij=theta*exp(-0.5* sum(eta*(x[i,]-x[j,])^2)), wrt to
elements of eta, a m-dimensional vector of parameters, given a n*m data
matrix x. So far, I have been computing matrices for each parameter (given
by par[index]) analytically, using the following
kmatder<- function(x, par, index) {
## x: n*m matrix
## par: vector of parameters, m=length(par)=ncol(x)
## compute matrix of partial derivatives wrt parameter par[index]: Cder
= d C/d par[index]
theta<-1
eta<-par
n<-nrow(x)
Cder<-matrix(0,n,n)
for (i in 1:n) {
for (j in i:n) {
Cder[i,j]<-(-0.5*((x[i,index]-x[j,index])^2))*theta*exp(-0.5*
sum(eta*(x[i,]-x[j,])^2))
}
}
Cder<-0.5*(Cder+t(Cder))
Cder
}
I was wondering whether it might be possible to speed up things using
numDeriv (jacobian). If so, what would be the right way to implement a
suitable method ?
Cheers,
Gao Daomeng
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
