Are you using optim() function in R?
Although I am not sure what your problem really is (as you haven't provided
sufficient information), it may be that the hessian computation in optim()
is not accurate enough for you.
Try the function hessian() in the "numDeriv" package. It is very accurate.
Here is an example:
require(numDeriv) # to compute hessian
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
ans <- optim(par=c(-1,1), fn=fr, method="BFGS")
hess <- hessian(x=ans$par, func=fr)
hess
Hope this helps,
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: rvarad...@jhmi.edu
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
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-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Marcel Rodrigues Lopes
Sent: Wednesday, April 29, 2009 10:42 AM
To: r-help@r-project.org
Subject: [R] Optim and hessian
Hi, my name is Marcel R. Lopes. My problem is,
I made a code to calculate the estimates of a Cox model with random effects.
Used to optimize the R command for this. The estimates were calculated
correctly, but the Hessian matrix does not have good values. The same thing
was done in SAS and gave good results for the Hessian Matrix. Where is the
problem in R? As the Hessian is calculated?. How could I solve this
problem?. I would be grateful if you could help me ....
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