I would recommend using the new Bayesian package 'LaplacesDemon' available 
on CRAN.




Ben Bolker <bbolker@gmail.com> 
Sent by: r-help-bounces@r-project.org
08/29/2011 02:50 PM

To
<r-help@stat.math.ethz.ch>
cc

Subject
Re: [R] Bayesian functions for mle2 object






Billy.Requena <billy.requena <at> gmail.com> writes:

> 
> Hi everybody,
> 
> I'm interested in evaluating the effect of a continuous variable on the 
mean
> and/or the variance of my response variable. I have built functions
> expliciting these and used the 'mle2' function to estimate the 
coefficients,
> as follows:
> 
> func.1 <- function(m=62.9, c0=8.84, c1=-1.6)
>                {
>                s <- c0+c1*(x)
>                -sum(dnorm(y, mean=m, sd=s,log=T))
>                }
> 
> m1 <- mle2(func.1, method="SANN")
> 
> However, the estimation of the effect of x on the variance of y usually 
has
> dealt some troubles, resulting in no convergencies or sd of estimates
> extremely huge. I tried using different optimizers, but I still faced 
the
> some problems.
> 
> When I had similar troubles in 'GLMM' statistical universe, I used 
bayesian
> functions to solve this problem, enjoyning the flexibility of different
> start points to reach the maximum likelihood estimates. However, I have 
no
> idea which package or which function to use to solve the specific 
problem
> I'm facing now.
> Does anyone have a clue?
> Thanks in advance

  Unless I'm missing something, you can fit this model
(more easily) in gls() from the nlme package, which allows models
for heteroscedasticity.  See ?nlme::varConstPower

  gls(y~1,weights=varPower(power=1,form=~x),data)

 This gives you a standard deviation proportional to (t1+|v|);
that is, if the baseline residual standard deviation is S, then
the standard deviation is S*(t1+|v|), so S would correspond to
your c1 and S*t1 would correspond to your c0.

   Ben Bolker

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