[R] saddle points in optim

[Ravi Varadhan]

The hessian from `optim' is not as accurate as that from `numDeriv' (with the default of Richardson extrapolation), so I would trust the numDeriv's hessian result over that of optim.  However, without seeing what you actually did, this is only a surmise.

Ravi.

____________________________________________________________________

Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University

Ph. (410) 502-2619
email: rvaradhan at jhmi.edu


----- Original Message -----
From: Jonathan Phillips <994phij at gmail.com>
Date: Sunday, November 7, 2010 3:45 am
Subject: [R] saddle points in optim
To: R-help at r-project.org


> Hi,
>  I've been trying to use optim to minimise least squares for a
>  function, and then get a guess at the error using the hessian matrix
>  (calculated from numDeriv::hessian, which I read in some other r-help
>  post was meant to be more accurate than the hessian given in optim).
>  
>  To get the standard error estimates, I'm calculating
>  sqrt(diag(solve(x))), hope that's correct.
>  
>  I've found that using numDeriv's hessian gets me some NaNs for errors,
>  whereas the one from optim gets me numbers for all parameters.  If I
>  look for eigenvalues for numDeriv::hessian, I get two negative numbers
>  (and six positive - I'm fitting to eight parameters), so does this
>  mean that optim hasn't converged correctly, and has hit a saddle
>  point?  If so, is there any way I could assist it to find the minimum?
>  
>  Thanks,
>  Jon Phillips
>  
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