[R-sig-ME] Modelling with uncertain (but not missing) categorical random effect values
mrm|500 @end|ng |rom york@@c@uk
Wed Jul 14 16:09:57 CEST 2021
Yes I refer to the fitted values and getting the marginal averages of these
fitted values for each group within mum_sp.
con = 1 for all offspring of 8 out of 9 (mum_id) individuals in one of the
groups within mum_sp, so this may be creating the complete separation.
Could I specify a prior on the mum_sp fixed effect in INLA to deal with the
Overall the responses don't vary much within mum_id levels. Males vary a
lot in number of offspring and proportion of their offspring with
particular females and species, so the high variance is expected. I had
previously collapsed all offspring per mum_id to individual rows and run as
a simple binomial GLM with a cbind(no._conspecific_offspring /
no._heterospecific offspring) response, but this didn't seem ideal as it
doesn't account for variation among parents. To get an idea of the data
structure (and whether what I'm trying to do here makes any sense)...
overall there are 27 individuals in mum_id, 18 individuals in dad_id and 3
groups for mum_sp. There are about 200 rows of offspring (averaging ~ 7-8
offspring / mum).
Thanks, I'll check out that forum.
All the best,
On Wed, 14 Jul 2021 at 13:10, Thierry Onkelinx <thierry.onkelinx using inbo.be>
> Dear Michael,
> The model formulation seems reasonable to me. I assume you refer to the
> fitted values? You get them when fitting the model after adding the
> argument: control.predictor = list(compute = TRUE)
> Another option is to specify linear combinations. See
> You might want to do some reading on fitting models with INLA. I recommend
> Looking at the model output, I noticed a few things.
> 1) The effects for mum_sp are extreme. Do you have (quasi) complete
> 2) The precision for mum_id is large (small random effects). Does
> that make sense?
> 3) The precision fordad_id is small (very large random effects). Does that
> make sense?
> You probably want to specify the priors of the random effects yourself
> instead of using the default priors.
> Note that INLA has its dedicated forum:
> Best regards,
> ir. Thierry Onkelinx
> Statisticus / Statistician
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
> Op wo 14 jul. 2021 om 11:17 schreef Michael Lawson <mrml500 using york.ac.uk>:
>> Dear Thierry,
>> Many thanks for your email - that looks like what I am after. I have
>> never used INLA before, so thus far I have just made a basic model
>> without specifying any further arguments to the call. Am I on the
>> right lines? How would I go about extracting the predicted probability
>> of conspecific mating for each group within mum_sp?
>> values <- as.factor(unique(c(levels(dat$dad_1), levels(dat$dad_2),
>> levels(dat$dad_3), levels(dat$dad_4))))
>> formula <- con ~ mum_sp + f(mum_id, model = "iid") + f(dad_1, w_1,
>> values = values, model = "iid") + f(dad_2, w_2, values = values, copy
>> = "dad_1") + f(dad_3, w_3, values = values, copy = "dad_1") + f(dad_4,
>> w_4, values = values, copy = "dad_1")
>> model <- inla(formula, family="binomial", data=dat,
>> "inla(formula = formula, family = \"binomial\", data = dat,
>> control.family = list(link = \"logit\"))"
>> Time used:
>> Pre = 0.462, Running = 3.3, Post = 0.115, Total = 3.88
>> Fixed effects:
>> mean sd 0.025quant 0.5quant 0.975quant mode kld
>> (Intercept) 12.696 10.298 0.834 10.000 40.536 6.699 0.087
>> mum_spL 18.725 11.824 3.426 16.051 49.365 11.804 0.023
>> mum_spN -11.697 10.257 -38.926 -9.318 1.392 -6.208 0.031
>> Random effects:
>> Name Model
>> mum_id IID model
>> dad_1 IID model
>> dad_2 Copy
>> dad_3 Copy
>> dad_4 Copy
>> Model hyperparameters:
>> mean sd 0.025quant 0.5quant 0.975quant
>> Precision for mum_id 2.03e+04 1.97e+04 977.697 1.43e+04 7.21e+04
>> Precision for dad_1 9.20e-02 5.10e-02 0.025 8.20e-02 2.17e-01
>> Expected number of effective parameters(stdev): 25.62(0.441)
>> Number of equivalent replicates : 7.46
>> Marginal log-Likelihood: -81.32
>> Many thanks,
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