For those issues with optimization methods (optim, optimx, and others) I see, a good percentage are because the objective function (or gradient if user-supplied) is mis-coded. However, an almost equal number are due to functions getting into overflow or underflow territory and yielding quantities that the optimization tools cannot handle (NA or Inf etc.)
Two general approaches I find helpful: 1) even if there are no actual bounds on parameters, put in "reasonable" limits. They don't need to be too tight, just enough to keep the parameters from giving a silly objective function 2) do some evaluations of the objective to make sure it is really being properly calculated. Never hurts to have some "known" outcomes. Beyond this, we get into reparametrizations. Great idea, but far too much work for most of us, even if we work in the field. Best, JN On 01/17/2011 06:00 AM, r-help-requ...@r-project.org wrote: > From: Uwe Ligges <lig...@statistik.tu-dortmund.de> > To: Jinrui Xu <jinru...@umich.edu> > Cc: r-help@r-project.org > Subject: Re: [R] fgev_error_matrix_singular ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
