saddle points in optim

Ben Bolker · 2010-11-07T15:21:11Z

Jonathan Phillips gmail.com> writes: > 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

Ben Bolker

Sun, Nov 7, 2010 7:21 AM

Jonathan Phillips <994phij <at> gmail.com> writes:

It's hard to say very much more without a reproducible example.
  If you can invert the hessian, then you can see whether the trouble
is arising from particular parameters or parameter combinations.  
For each 'difficult' parameter, you can try varying its value while
holding the others fixed at the point that optim() has found and see
whether the curvature is indeed negative in these directions.
  You could try restarting from the current values, or trying other
optimization methods (possibly taking a look at the 'optimx' package
on R-forge), or using a stochastic optimization method such as SANN,
or somehow coming up with better starting parameters.

saddle points in optim

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