[ADMB Users] Transformation of mcmc values
Ben Bolker
bbolker at gmail.com
Tue Mar 20 09:45:13 PDT 2012
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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