[ADMB Users] Transformation of mcmc values

[Håkon Holand]

Thank you very much! That seems to have done it.

Cheers,
Håkon Holand
---------------------------------------------------------
Håkon Holand, PhD.Student
Centre for Conservation Biology
Department of Biology
Norwegian University of Science and Technology
NO-7491 Trondheim
Norway

________________________________________
Fra: users-bounces at admb-project.org [users-bounces at admb-project.org] på vegne av Ben Bolker [bbolker at gmail.com]
Sendt: 20. mars 2012 17:45
Til: users at admb-project.org
Emne: Re: [ADMB Users] Transformation of mcmc values

On 12-03-20 12:02 PM, Håkon Holand wrote:



> I was wondering if anyone knows a way of transforming values obtained from a HPD of mcmc runs of a model?
>
> I am running models with a binary response variable (0/1) (family=binomial) and using individual identity as a random variable. I am currently using R 2.14.1 (64-bit) and glmmadmb v.0.7.2.5.
>
> The intervals looks like this:
>




>> head(HPDinterval(m1))
>               lower      upper
> beta.1 -487.3376278 -389.89072
> beta.2   88.3658266  167.10142
> beta.3 -106.7078281  -46.53788
> beta.4    1.2919346   34.92751
> beta.5   -0.8259944   50.58856
> beta.6  -49.8802241  -12.91753



> First of all: I know that these values are from the parameters fitted internally, using an orthogonalized version of the original design matrix, not the original coefficients.
>


> The question is:  (if possible) How can I transform these values to, for example, Logit? I would like to have my estimated value and its limits on the same scale at least (and hopefully a "reader friendly" scale).
>

http://markmail.org/message/ga5lh6hbyhh2iqht should help, plus info in
the latest (upcoming?) version of the package vignette.

  You do need to watch out for back-transforming confidence intervals
on the logit scale, though.  Back transforming the *parameters* (i.e.
the differences in the logit per unit of each predictor) to the
probability scale often doesn't make sense. Usually, only
back-transforming *predictions* to the probability scale makes sense.
This is a fundamental conceptual problem that applies to GLMs on the
logit scale, not just to GLMMs ...

  Ben

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