[R-sig-ME] Modelling with uncertain (but not missing) categorical random effect values

[Thierry Onkelinx]

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
https://www.r-inla.org/faq#h.dwc64vjwo03.
You might want to do some reading on fitting models with INLA. I recommend
http://www.highstat.com/index.php/beginner-s-guide-to-regression-models-with-spatial-and-temporal-correlation

Looking at the model output, I noticed a few things.
1) The effects for mum_sp are extreme. Do you have (quasi) complete
separation?
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:
https://groups.google.com/g/r-inla-discussion-group?pli=1

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

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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,
> control.family=list(link='logit'))
> summary(model)
>
> Call:
>    "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
>  mode
> Precision for mum_id 2.03e+04 1.97e+04    977.697 1.43e+04   7.21e+04
> 2331.739
> Precision for dad_1  9.20e-02 5.10e-02      0.025 8.20e-02   2.17e-01
> 0.061
>
> Expected number of effective parameters(stdev): 25.62(0.441)
> Number of equivalent replicates : 7.46
>
> Marginal log-Likelihood:  -81.32
>
> Many thanks,
> Mike
>

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