[R-sig-ME] zero-inflated models in MCMCglmm
Gustaf Granath
gustaf.granath at gmail.com
Mon Nov 28 12:11:40 CET 2016
Jarrod,
singular.ok=TRUE was the problem. I did some copy-paste and didnt clean
up the code. However, Im still confused about the coefs. Say that X1 and
X2 each have two levels: no/yes and empty/full respectively. For a
"zipoisson" I get the coef table:
traity
traitzi_y
at.level(trait, 1):X1no
at.level(trait, 1):X2full
at.level(trait, 1):X1yes:X2full
But when running "zapiosson" I get:
traity
traitza_y
at.level(trait, 1):X1yes
at.level(trait, 1):X2full
at.level(trait, 1):X1yes:X2full
+ traitza_...
The "zapiosson" output makes sense to me but the "zipoisson" looks odd
to me.
Predictions: ok, estimates actually changed after I updated the package.
I didnt make a huge difference though and it doesnt clear things up
completely. The strange thing is that conditional predictions captures
the data really well - both for zi, za and hu. However, the estimated
marginal predictions are sensible for hu, and za (ie conditional and
marginal predictions are quite similar), while for zi they are way too
large (getting values much higher than you find in the observed data).
Yes, marginal predictions should be larger by definition, and may be
quite much higher if you have some sites with very high means that pulls
the expected "random site" prediction above the "average site" estimate.
However, this crazy large "blow up" effect that I see (predictions
outside data range), I guess can happen when random effects are large.
But what are actually "good" predictions here? If all classic model
diagnostics looks good and conditional predictions are close to raw
(pooled) treatments means but marginal predictions are way too high,
what is then "wrong" with the model? Where do you start looking to
identify whats going on? Rarely are predicted estimates reported in
papers (cond nor marginal) and Im trying to get my head around how they
can be better used in model checking.
Tom> Thanks for your input. I will look closer at your paper.
Gustaf
On 2016-11-26 08:59, Jarrod Hadfield wrote:
> Hi,
>
> If X1 was has two levels then it is odd that you have an intercept AND
> 2 X1 contrasts. Have you used singular.ok=TRUE? If not then are you
> sure there is not a third (rare) level to X1? Try
> unique(your_data$X1): trailing whitespace from excel spreadsheets is a
> common cause of such a problem.
>
> Regarding the predictions, the issue may be with the above. However,
> note that there was a bug with posterior="mean" in the call to
> predict.MCMCglmm (the first posterior iteration of the fixed effects
> was used rather than there mean). This bug is fixed in the current
> version, and there was no bug in the default posterior="all".
>
> Cheers,
>
> Jarrod
>
>
>
> On 25/11/2016 13:16, Gustaf Granath wrote:
>> Hi,
>> A few questions regarding zero-inflated models using MCMCglmm.
>>
>> 1. Odd contrasts are created when using, e.g. :
>> y ~ trait-1 + at.level(trait, 1):(X1+X2), family = "zipoisson" #X1
>> and X2 are factors with 2 levels
>> COEF TABLE
>> traity
>> traitzi_y
>> at.level(trait, 1):X1lev1
>> at.level(trait, 1):X1lev2
>> at.level(trait, 1):X2lev2
>>
>> It doesnt look like this in the course notes (what I can see). Is
>> at.level(trait, 1):X1lev1 the reference level for everything below? I
>> also get very high (>1000) estimates for traity, at.level(trait,
>> 1):X1lev1 and at.level(trait, 1):X1lev2.
>>
>> 2. I made four models
>> a) y ~ X1*X2, family = "poisson"
>> Large overdispersion (units ~ 5) but everything looks fine
>> (traceplots, random effects (including units) ). Predictive checks
>> show that the models predict too few zeros though. And alarming is
>> that predictions are really bad, e.g. a treatment mean of 40 is
>> predicted to have 300 counts.
>>
>> b) y ~ trait-1 + at.level(trait, 1):(X1+X2), family = "zipoisson"
>> Model looks OK, but again, predictions are way too high for the
>> higher tretament means.
>>
>> c:d) y ~ trait-1 + at.level(trait, 1):(X1+X2), family = "zapoisson"
>> or "hupoisson"
>> Predicted values are much better. But is it the same way get
>> predictions here? Possible to get predictions from the separate
>> models (poisson/binomial) in the hurdle case? (code available?)
>> #get e.g. treatment predictions?
>> predict.MCMCglmm(model, marginal = ~ random_factor, type="response",
>> posterior = "mean")
>>
>> The "zapoisson" model performs best but I dont understand why the
>> "zipoisson" is so bad. Any typical things to look for when it looks
>> like this?
>> Interpreting "zapoisson" isnt easy, any good literature/tutorials on
>> this model?
>>
>>
>> Cheers
>>
>> Gustaf
>>
>>
>
>
--
Gustaf Granath
Post doc
Swedish University of Agricultural Sciences
Department of Ecology
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