I would recommend using the new Bayesian package 'LaplacesDemon' available
on CRAN.
Ben Bolker <bbol...@gmail.com>
Sent by: r-help-boun...@r-project.org
08/29/2011 02:50 PM
To
<r-h...@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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