calculate posterior group-level means

[Ramona]

Hi all,

I am completely new to R INLA and currently trying to calculate posterior group-level means  and CrI's of the posterior predictive distribution.

Is there a function like fitted() in brms? (https://rdrr.io/cran/brms/man/fitted.brmsfit.html)

And if nut, what is the best way to calculate it?

Thanks a lot!!!

[Helpdesk]

Hi

I would check out
https://www.r-inla.org/learnmore/books

and especially the book by Virgilo Gomez-Rubio which is also avaialble
online.

Best
H

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Håvard Rue
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[Ramona]

Dear Havard Rue,

thank you for this advice! Before buying books, I would like to be sure that there is a solution for my problem to be found.

To be more concrete, here my model structure:

formula <- y ~ treatment*season +        

  f(spatial.field2, model = spde.pc) +     

  f(start,model = "rw2") +                  

  f(individual,model = "iid")  +            

  f(treatment_n,model = "rw2")           

Mod <- inla(formula,

                   data = inla.stack.data(Stack, spde=spde.pc),

                   family = "lognormal",

                   control.compute = list(cpo = TRUE, config = TRUE),

                   control.predictor = list(A = inla.stack.A(Stack),

                                            compute = T))

What I would like to get is the means with CrI's like shown below. To get an impression I calculated them out of group means of the dataset from fitted values (e.g. fit<-aggregate(mean ~ treatment + season, data = bar_dist, FUN= "mean" ).

Thanks!

Ramona