[R] numericDeriv

[Spencer Graves]

      optim(..., hessian=TRUE, ...) outputs a list with a component 
hessian, which is the second derivative of the log(likelihood) at the 
minimum.  If your objective function is (-log(likelihood)), then 
optim(..., hessian=TRUE)$hessian is the observed information matrix.  If 
eigen(...$hessian)$values are all positive with at most a few orders of 
magnitude between the largest and smallest, then it is invertable, and 
the square roots of the diagonal elements of the inverse give standard 
errors for the normal approximation to the distribution of parameter 
estimates.  With objective functions that may not always be well 
behaved, I find that optim sometimes stops short of the optimum.  I run 
it with method = "Nelder-Mead", "BFGS", and "CG", then restart the 
algorithm giving the best answer to one of the other algorithms.  Doug 
Bates and Brian Ripley could probably suggest something better, but this 
has produced acceptable answers for me in several cases, and I did not 
push it beyond that. 

      hope this helps. 

Jean Eid wrote:

>Dear All,
>I am trying to solve a Generalized Method of Moments problem which
>necessitate the gradient of moments computation to get the
>standard  errors of estimates.
>I know optim does not output the gradient, but I can use numericDeriv to
>get that. My question is: is this the best function to do this?
>
>Thank you
>Jean,
>
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