search for: optimhess

Hi I am trying to recover the hessian of a problem optimised with box-constraints. The problem is that in some cases, my estimates are very close to the boundary, which will make optim(..., hessian=TRUE) or optimHessian() fail, as they do not follow the box-constraints, and hence estimate the function in the unfeasible parameter space. As a simple example (my problem is more complex though, simple param transformations do not apply ashere), imagine estimating mu and sigma (restricted to be >0) of a simple n...

...ted by running some small simulations to generate some survival data (without any random effects), and analyse the data using the parfm package. I am using an exponential baseline hazard. When the baseline hazard rate drops to 0.001, I get the following error when trying to fit the model: Error in optimHess(par = ESTIMATE, fn = Mloglikelihood, obs = obsdata, : non-finite finite-difference value [1] In addition: Warning message: In log(pars) : NaNs produced Has anybody else come across this issue, or could suggest why parfm struggles with low event rates? Or could someone please run my code to see...

The R function "optim" fails when a function requires additional arguments, if the option "hessian=T" is specified. I am using R Version 1.0.0 on Windows 98. Here is an example of the Rosenbrock Banana function from the optim help example, with the function and gradient modified to take an additonal argument. Note that the call to optim works fine unless a hessian is

...ultimate speed is required. o The formerly internal function .package_dependencies() from package tools for calculating (recursive) (reverse) dependencies on package databases has been renamed to package_dependencies() and is now exported. o There is a new function optimHess() to compute the (approximate) Hessian for an optim() solution if hessian = TRUE was forgotten. o .filled.contour() is a 'bare-bones' function to add a filled-contour rectangular plot to an already prepared plot region. o The stepping in debugging and single-step...

...ultimate speed is required. o The formerly internal function .package_dependencies() from package tools for calculating (recursive) (reverse) dependencies on package databases has been renamed to package_dependencies() and is now exported. o There is a new function optimHess() to compute the (approximate) Hessian for an optim() solution if hessian = TRUE was forgotten. o .filled.contour() is a 'bare-bones' function to add a filled-contour rectangular plot to an already prepared plot region. o The stepping in debugging and single-step...

...Internal() calls). o Most of the .Internal() calls used in the stats package have been migrated to C code in that package. This means that a number of .Internal() calls which have been used by packages no longer exist, including .Internal(cor) .Internal(cov), .Internal(optimhess) and .Internal(update.formula). o Some .External() calls to the base package (really to the R executable or shared library) have been moved to more appropriate packages. Packages should not have been using such calls, but some did (mainly those used by integrate())....

...Internal() calls). o Most of the .Internal() calls used in the stats package have been migrated to C code in that package. This means that a number of .Internal() calls which have been used by packages no longer exist, including .Internal(cor) .Internal(cov), .Internal(optimhess) and .Internal(update.formula). o Some .External() calls to the base package (really to the R executable or shared library) have been moved to more appropriate packages. Packages should not have been using such calls, but some did (mainly those used by integrate())....