[R-sig-dyn-mod] FME Package -help

[Daniel Kaschek]

Hi Adriele,

I think, it is not possible to send attachments over the help list.
Perhaps you can copy paste the code.

I am not an expert on Bayesian statistics, but to my knowledge parameter
estimation in a Bayesian and a frequentist setting differ especially for
parameters in combination with uninformative data, i.e. parameters that
cannot be determined from the data. In the Bayesian setting you
basically get the parameter distribution back that you set as prior. In
the frequentist setting where you maximize the log-likelihood, you find
flat directions in parameter space, i.e. no curvature of the
log-likelihood -> large variance/covariance. Might this be the reason?

Best regards,
Daniel

On Mo, 2015-09-28 at 13:32 -0300, Adriele Giaretta Biase wrote:
>        I am working on my thesis, at the University of São Paulo – Brazil,
> I am using the delayed rejection and adaptative Metropolis (included in the
> FME Package) and Bootstrapping (using the Nelder-Mead algorithm)  to
> estimate the best set and parameter distributions for an EDO model and a
> very small dataset (n = 27, divided in four groups of n = [4,5,9,9].
> Besides, I have estimated the parameters for each individual animal
> (minimizing quadratic deviation).
> 
>       Although the estimate of parameter values are similar between those
> methods, the covariance was extremely different. The parameters have
> Gaussian distribution. In the larger groups, the Bootstrap and individual
> based covariances estimates seems to converge. However, it is not the case
> for the Bayesian.
> 
>       Please, could you help me making sure that I am using the FME package
> correctly to estimate the Bayesian covariances. Would those differences be
> expected? I include the code for your information.
> 
> 
>        Thanks in advance,
> 
>        Adriele.
>