[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 20:15:44 CEST 2019
> Feel free to follow up on issue 285 if you have more insight.
At least from the technical side, I don’t have more insights, I guess. I already noticed the discussion in #285 some time ago, so I’m lurking, but not actively following 😉
Terminology used in different papers or from method reports of different surveys also doesn’t seem always consistent to me. I think, “post-stratification weights” were requested by Sam, which are weights based on group (or stratum) characteristics (like the distribution of age or gender proportions). Ben Bolker also mentioned sample weights in #285 (“in addition to these two cases, there's also the case of sampling weights, which is difficult/a mess for complex regression models but worth discussing at least ...”).
The difference between the weights-argument in typical regression model functions and the survey-package is “The survey package not only allows for adjusting the composition of a sample to the characteristics of the general population. Most base packages would allow you to do that by specifying a weights argument. The survey package goes further by correcting the design effect introduced by the application of post-stratification weights.” (https://tophcito.blogspot.com/2014/04/social-science-goes-r-weighted-survey.html).
This of course only applies if you actually have survey-data.
Von: Mollie Brooks <mollieebrooks using gmail.com>
Gesendet: Montag, 10. Juni 2019 19:46
An: d.luedecke using uke.de
Cc: Sam Crawley <sam_crawley using warpmail.net>; Help Mixed Models <r-sig-mixed-models using r-project.org>
Betreff: Re: [R-sig-ME] Predicted probabilites with CIs for multilevel logistic regression with prior weights
On 10Jun 2019, at 19:40, <d.luedecke using uke.de <mailto:d.luedecke using uke.de> > <d.luedecke using uke.de <mailto:d.luedecke using uke.de> > wrote:
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> https://github.com/glmmTMB/glmmTMB/issues/440),
Yes, I (mebrooks) am the one who referenced it and the user (mmeierer) said it fit their needs for "sample weights".
which in turn shows an example from Cross Validated:
<https://stats.stackexchange.com/questions/57107/use-of-weights-in-svyglm-vs-glm> 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),
Ok. I don’t know the survey package and don’t have time to look into it now. Feel free to follow up on issue 285 if you have more insight.
cheers,
Mollie
or how to reproduce such weights using glmmTMB.
Von: Mollie Brooks <mollieebrooks using gmail.com <mailto:mollieebrooks using gmail.com> >
Gesendet: Montag, 10. Juni 2019 19:04
An: Sam Crawley <sam_crawley using warpmail.net <mailto:sam_crawley using warpmail.net> >; Help Mixed Models <r-sig-mixed-models using r-project.org <mailto:r-sig-mixed-models using r-project.org> >
Cc: d.luedecke using uke.de <mailto: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, < <mailto:d.luedecke using uke.de> d.luedecke using uke.de> < <mailto:d.luedecke using uke.de> 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> 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 < <mailto:r-sig-mixed-models-bounces using r-project.org> r-sig-mixed-models-bounces using r-project.org> Im
Auftrag von <mailto:d.luedecke using uke.de> d.luedecke using uke.de
Gesendet: Montag, 10. Juni 2019 17:31
An: 'Sam Crawley' < <mailto:sam_crawley using warpmail.net> sam_crawley using warpmail.net>;
<mailto:r-sig-mixed-models using r-project.org> 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/> 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> 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> 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> 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 < <mailto:r-sig-mixed-models-bounces using r-project.org> r-sig-mixed-models-bounces using r-project.org> Im
Auftrag von Sam Crawley
Gesendet: Montag, 10. Juni 2019 10:36
An: <mailto:r-sig-mixed-models using r-project.org> 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> 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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