[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
Mon Jun 10 19:40:52 CEST 2019
I think that Sam is talking about “sampling” or “survey” weights (as compared to analytical or frequency weights, used by “normal” regression models).
The issue you’re referring to is referenced by another issue (https://github.com/glmmTMB/glmmTMB/issues/440), which in turn shows an example from Cross Validated:
https://stats.stackexchange.com/questions/57107/use-of-weights-in-svyglm-vs-glm
If I use that example, and add a third model fitted with glmmTMB, I get following result when comparing the weights from the fitted objects:
library(glmmTMB)
glm2 <- glmmTMB(re78 ~ treat, weights = w , data = lalonde)
cbind(glm1$weights, glm11$weights, glm2$frame$`(weights)`)
#> [,1] [,2] [,3]
#> 1 1.4682453 2.108394 2.108394
#> 2 0.9593877 1.377677 1.377677
#> 3 0.7489954 1.075554 1.075554
#> 4 0.7319955 1.051143 1.051143
#> 5 0.7283328 1.045883 1.045883
#> 6 0.7244569 1.040317 1.040317
As you can see, “glm” and “glmmTMB” produce the same weights, while the survey-package has different weights… I’m not sure that the weights implemented in glmmTMB are actually “sampling” weights (for surveys, as implemented in the survey package), or how to reproduce such weights using glmmTMB.
Von: Mollie Brooks <mollieebrooks using gmail.com>
Gesendet: Montag, 10. Juni 2019 19:04
An: Sam Crawley <sam_crawley using warpmail.net>; Help Mixed Models <r-sig-mixed-models using r-project.org>
Cc: d.luedecke using uke.de
Betreff: Re: [R-sig-ME] Predicted probabilites with CIs for multilevel logistic regression with prior weights
On 10Jun 2019, at 17:33, <d.luedecke using uke.de <mailto:d.luedecke using uke.de> > <d.luedecke using uke.de <mailto:d.luedecke using uke.de> > wrote:
mixed models in R do correctly not account for sampling weights
Should be: mixed models in R do *currently* not account for sampling weights
I’m still trying to get a handle of the different definitions of "weights" but I believe we implemented sampling weights in glmmTMB. We do this by weighting the log-likelihood contribution of each observation. I think this is different from prior weights if you mean Bayesian priors. There has been some discussion of the different implementations of "weights" in different R functions (link below) and we still need to update the documentation for glmmTMB
https://github.com/glmmTMB/glmmTMB/issues/285
Here’s a binomial example:
library(glmmTMB)
set.seed(123)
n=100
dat=data.frame(trials=rpois(n, lambda=50), rownum=1:n)
dat$success=rbinom(n, dat$trials, prob=.3)
dat$rep=sample(1:5, size=n, replace=TRUE) #each observation is repeated 1 to 5 times
rows=rep(dat$rownum, each=1, times=dat$rep)
dat_disaggregated=dat[rows, ]
summary(glmmTMB(cbind(success, trials-success)~1, weights=rep, dat, family=binomial))
summary(glmmTMB(cbind(success, trials-success)~1, dat_disaggregated, family=binomial))
and it works with non-integer weights
summary(glmmTMB(cbind(success, trials-success)~1, weights=rep/5, dat, family=binomial))
cheers,
Mollie
-----Ursprüngliche Nachricht-----
Von: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org <mailto:r-sig-mixed-models-bounces using r-project.org> > Im
Auftrag von d.luedecke using uke.de <mailto:d.luedecke using uke.de>
Gesendet: Montag, 10. Juni 2019 17:31
An: 'Sam Crawley' <sam_crawley using warpmail.net <mailto:sam_crawley using warpmail.net> >;
r-sig-mixed-models using r-project.org <mailto: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 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 <mailto: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 <mailto: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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