[R-sig-ME] making inferences about overdispersion in nbinom glmmabmb

[Joshua Wiley]

Hi Paul,

Sure you can estimate separate overdispersion parameters by some
grouping variable in MCMCglmm.  Then just plot those densities, or the
HPD intervals.

Actually, if you can put them together, you can go a step further,
because you can fit two models:

1) allow separate overdispersion parameters by group
2) do not allow separate overdispersion parameters by group

and then compare the DIC, which should put you on really solid footing
for drawing some inference about whether they are the same or
importantly different.

Cheers,

Josh



On Fri, Jun 21, 2013 at 1:43 AM, Paul Johnson
<paul.johnson at glasgow.ac.uk> wrote:
> Thanks very much for these suggestions and for the code, Josh and Dave. As far as I remember MCMCglmm allows the residual variance to differ between groups, so I guess I could estimate the separate variances in the same model (the only reason for fitting separate models using glmmadmb was because it doesn't allow this).
>
> Best wishes,
> Paul
>
>
> On 20 Jun 2013, at 03:52, Joshua Wiley <jwiley.psych at gmail.com> wrote:
>
>> Hi Paul,
>>
>> Standard errors on variance components do not provide a consistent
>> means to assess either significance or create confidence intervals.
>> In part because variances are fundamentally bounded, so you really
>> cannot expect symmetric intervals.  Of course if they are large
>> enough, that may not be an issue, but as a general principle,
>> encouraging the use of standard errors assuming a standard normal is
>> not going to work well.
>>
>> If you are bound to a frequentist world, a likelihood ratio test is
>> probably a good choice with a free and constrained estimate.  To
>> generate confidence intervals, a good approach can be used based on
>> profile likelihoods.  I am sure these are presented elsewhere, but
>> Terry Therneau has a good presentation of the technique and theory in
>> his text on survival models.
>>
>> Below is an alternative Bayesian approach.  The parameterization of
>> course is slightly different as this uses MCMCglmm and a non zero
>> residual variance rather than negative binomial model, but the
>> posterior allows valid examination and computation of confidence
>> intervals and presentation of the distribution.  Note for larger
>> values, it is not an issue, but in the last example (admittedly chosen
>> as a degenerate case) normal based confidence intervals would perform
>> quite poorly.
>>
>> Best,
>>
>> Josh
>>
>>
>> require(MASS)
>> require(MCMCglmm)
>>
>> set.seed(10)
>> d <- data.frame(x = rnorm(1000))
>>
>> d <- within(d, {
>>  y1 = rnegbin(1000, 2 + .5 * x, 1)
>>  y2 = rnegbin(1000, 2 + .5 * x, 4)
>>  y3 = rnegbin(1000, 2 + .5 * x, 16)
>> })
>>
>> m <- list(
>>  t1 = MCMCglmm(y1 ~ x, data = d, family = "poisson", nitt=1e5, verbose=FALSE),
>>  t4 = MCMCglmm(y2 ~ x, data = d, family = "poisson", nitt=1e5, verbose=FALSE),
>>  t16 = MCMCglmm(y3 ~ x, data = d, family = "poisson", nitt=1e5, verbose=FALSE))
>>
>> plot(m$t1$VCV)
>> plot(m$t4$VCV)
>> plot(m$t16$VCV)
>>
>> HPDinterval(m$t1$VCV)
>> HPDinterval(m$t4$VCV)
>> HPDinterval(m$t16$VCV)
>>
>> p <- function(x) {
>>  hist(x, freq=FALSE)
>>  lines(seq(min(x), max(x), length.out = 1000),
>>  dnorm(seq(min(x), max(x), length.out = 1000), mean= mean(x), sd = sd(x)),
>>  type = "l", col = "blue")
>> }
>>
>> p(m$t1$VCV[, 1])
>> p(m$t4$VCV[, 1])
>> p(m$t16$VCV[, 1])
>>
>>
>>
>> On Wed, Jun 19, 2013 at 5:17 AM, Paul Johnson
>> <paul.johnson at glasgow.ac.uk> wrote:
>>> Hi,
>>>
>>> I'm fitting negative binomial GLMMs in glmmadmb using  glmmabmb(…, family = "nbinom"). I'm interested in making inferences about the amount of overdispersion in each model, and comparing overdispersion between models. The outcome is mosquito count and the different models are different designs of mosquito trap. Each model fit gives an estimate of alpha (the [inverse] overdispersion parameter) and sd_alpha, the standard error of alpha. It's tempting to use the standard error of alpha to construct CIs, test for differences, etc, but I have no idea if this is justified. It seems over-optimistic to assume that sampling error in alpha is approximately normally distributed. Are there conditions (e.g large sample size) where this assumption is justified?
>>>
>>> Thanks for your help,
>>> Paul Johnson
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>>
>> --
>> Joshua Wiley
>> Ph.D. Student, Health Psychology
>> University of California, Los Angeles
>> http://joshuawiley.com/
>> Senior Analyst - Elkhart Group Ltd.
>> http://elkhartgroup.com
>



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
University of California, Los Angeles
http://joshuawiley.com/
Senior Analyst - Elkhart Group Ltd.
http://elkhartgroup.com