Hi all,
sorry for the misleading in the previous email. here is my function to
calculate the maximum likelihood function for a multinormial distribution:
mymle <- function (sigmaX, sigmaY, constraints, env){
# build omega
omegaX = abs(sigmaX) * kin + abs(env) * diag(1.0, n, n)
Lin Pan yahoo.com> writes:
>
>
> Hi all,
>
> I am trying to use mle() to find a self-defined function. Here is my
> function:
>
> test <- function(a=0.1, b=0.1, c=0.001, e=0.2){
>
> # omega is the known covariance matrix, Y is the response vector, X is the
> explanatory matrix
>
> odet = un
Hi all,
I am trying to use mle() to find a self-defined function. Here is my
function:
test <- function(a=0.1, b=0.1, c=0.001, e=0.2){
# omega is the known covariance matrix, Y is the response vector, X is the
explanatory matrix
odet = unlist(determinant(omega))[1]
# do cholesky decompositio
