[R-sig-ME] Predicted probabilites with CIs for multilevel logistic regression with prior weights
d@iuedecke m@iii@g oii uke@de
d@iuedecke m@iii@g oii uke@de
Tue Jun 18 08:30:16 CEST 2019
> However, should I be using the type = "re" parameter?
This depends on whether you would like to have confidence or prediction intervals. I'm not sure if there's a clear definition, or a consensus on how to obtain prediction intervals. There's a section about this in Ben Bolker's GLMM-FAQ:
http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#predictions-andor-confidence-or-prediction-intervals-on-predictions
He states that you want to add the residual variance to compute prediction intervals. However, there are also some caveats: https://stackoverflow.com/questions/14358811/extract-prediction-band-from-lme-fit
Ben also has a comment in his examples: "## must be adapted for more complex models". I'm not sure if this refers only to make sure to compute sigma properly, or if you also need to take the random effect variances into account. ggeffects currently computes the standard error for type = "re" based on the *random effects variances* (see details here: https://easystats.github.io/insight/reference/get_variance.html). This seems to be even a bit more conservative than just taking the residual variance. But again, any comments on this are welcome. I've been in email exchange with Russell Lenth, maintainer of the emmeans-package, and we were also talking about if it's actually straightforward to obtain prediction intervals, or not. In the latest emmeans-release, there is a vignette on this topic (https://cran.r-project.org/web/packages/emmeans/vignettes/predictions.html).
In short: you don't need to specify type = "re" if you want to get predictions from your mixed models. Predictions for both type = "fe" and type = "re" are always on a population-level. However, you need type = "re" if you want to make predictions at each level of the random effects (group factor), see https://strengejacke.github.io/ggeffects/articles/randomeffects.html#marginal-effects-for-each-level-of-random-effects (though here you don't get any intervals).
Best
Daniel
-----Ursprüngliche Nachricht-----
Von: Sam Crawley <sam_crawley using warpmail.net>
Gesendet: Dienstag, 18. Juni 2019 03:44
An: d.luedecke <d.luedecke using uke.de>; r-sig-mixed-models using r-project.org
Betreff: Re: [R-sig-ME] Predicted probabilites with CIs for multilevel logistic regression with prior weights
Hi again Daniel (and list),
Thanks again for the below. I have been using the ggpredict() function, and it works well. However, should I be using the type = "re" parameter? Or is this only required when attempting to predict values for each group? (I have read the ggeffects documentation on this, but it's still not entirely clear to me).
When adding type="re", the confidence intervals become very wide, which is obviously not ideal.
Thanks,
Sam Crawley.
On Tue, 11 Jun 2019, at 03:30, d.luedecke using uke.de wrote:
> Hi Sam,
>
> you could the "ggeffects" package
> (https://strengejacke.github.io/ggeffects/), and there is also an example
> for a logistic mixed effects model
> (https://strengejacke.github.io/ggeffects/articles/practical_logisticmixedmo
> del.html), which might help you.
>
> For binomial models, using weights often results in the following warning:
> #> non-integer #successes in a binomial glm!
>
> However, CIs for the predicted probabilities can be calculated nevertheless
> (at least in my quick example). Note that afaik, mixed models in R do
> correctly not account for sampling weights. However, Thomas Lumley, author
> of the survey-package, works on a survey-function for mixed models
> (https://github.com/tslumley/svylme), probably the GitHub version is quite
> stable (haven't tested yet).
>
> An alternative would be the "scale_weights()" function from the
> sjstats-package
> (https://strengejacke.github.io/sjstats/articles/mixedmodels-statistics.html
> #rescale-model-weights-for-complex-samples ), which rescales sampling
> weights so they can be used as "weights" for the mixed models function you
> have in R (lme4, lme, ...).
>
> Based on that function, I have a small example that demonstrates how to
> compute predicted probabilities for mixed models with (sampling) weights
> (ignore the warnings, this is just for demonstration purposes):
>
> library(lme4)
> library(sjstats) # for scale_weights() and sample data
> library(ggeffects) # for ggpredict()
>
> data(nhanes_sample)
> set.seed(123)
> nhanes_sample$bin <- rbinom(nrow(nhanes_sample), 1, prob = .3)
> nhanes_sample <- scale_weights(nhanes_sample, SDMVSTRA, WTINT2YR)
>
> m <- glmer(
> bin ~ factor(RIAGENDR) * age + factor(RIDRETH1) + (1 | SDMVPSU),
> family = binomial(),
> data = nhanes_sample,
> weights = svywght_a
> )
>
> ggpredict(m, c("age", "RIAGENDR")) %>% plot()
>
>
> Best
> Daniel
>
> -----Ursprüngliche Nachricht-----
> Von: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> Im
> Auftrag von Sam Crawley
> Gesendet: Montag, 10. Juni 2019 10:36
> An: r-sig-mixed-models using r-project.org
> Betreff: [R-sig-ME] Predicted probabilites with CIs for multilevel logistic
> regression with prior weights
>
> Hello all,
>
> I am doing a multilevel binomial logistic regression using lme4, and the
> survey data I am using requires weights to be used. I would like to
> calculate various predicted probabilities with confidence intervals based on
> the estimated model. The predict function obviously doesn't give me standard
> errors, and the recommended method to get these is to use the bootMer
> function.
>
> However, in my case, the weights provided are not integers, and the bootMer
> function exits with an error if the weights are not integers (I raised a
> GitHub issue about this, and was pointed to this list:
> https://github.com/lme4/lme4/issues/524 ).
>
> So my question is, what is the best way to calculate the predicted
> probabilities (with confidence intervals) in my case?
>
> I would appreciate any help you can give me, and I'm happy to provide more
> details if required.
>
> Thanks,
> Sam Crawley.
>
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