[R] how to use mle with a defined function

[Lin Pan]

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 decomposition

C = chol(omega)

# transform data

U = t(C)%*%Y
WW=t(C)%*%X

beta = lm(U~W)$coef

Z=Y-X%*%beta
V=solve(t(C), Z)

0.5*odet + 0.5*(t(V)%*%V)

}

and I am trying to call mle() to calculate the maximum likelihood estimates
for function (0.5*odet+0.5*(t(V)%*%V)) by

result = mle(test, method="Nelder-Mead")

But I get the following error message:

Error in optim(start, f, method = method, hessian = TRUE, ...) : 
        (list) object cannot be coerced to 'double'

I am pretty sure that the matrices, parameters etc are numerical before
importing to the function. But why I still get such error message? Could
anybody give some help on this? thanks a lot.


Lin
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