[ADMB Users] Transformation of mcmc values

Håkon Holand hakon.holand at bio.ntnu.no
Wed Mar 21 03:10:15 PDT 2012

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

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

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.
> 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 ...


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