Binomial glmer(): appropriateness of link and influential points
Thank you for the quick and informative reply.
1. Yes, I was aware of the plot(fitted_model, type=c("p", "smooth")) but is this appropriate for 0/1 outcome? I am asking since the plot would be just two lines for the 0 and 1 categories with a smoother in the middle.
2. I have tried influence.merMod() and it takes a very long time. So the Cook's distance is still appropriate for GLMM with logit link?
Best,
Hedyeh Ahmadi, Ph.D.
Statistician
Keck School of Medicine
Department of Preventive Medicine
University of Southern California
Postdoctoral Scholar
Institute for Interdisciplinary Salivary Bioscience Research (IISBR)
University of California, Irvine
LinkedIn
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From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on behalf of Ben Bolker <bbolker at gmail.com>
Sent: Thursday, April 22, 2021 8:21 AM
To: r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Binomial glmer(): appropriateness of link and influential points
Sent: Thursday, April 22, 2021 8:21 AM
To: r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] Binomial glmer(): appropriateness of link and influential points
On 4/22/21 11:12 AM, Hedyeh Ahmadi wrote:
> Hello all,
> I have two questions regarding GLMM with binomial/logit link. Here are some information about my model/data before I ask my questions:
>
> * My outcome is 0/1.
> * I have continuous and categorical predictor.
> * My data has 19000 rows with 2 observations per subject.
> * My model only has one random intercept for each subject.
> * I am using glmer() command in R.
>
> My questions are as follows and any sample R code would be appreciated:
>
> 1. What's the best way to evaluate the appropriateness of my link function?
> 2. What's the best way to find influential points? Can I still use Cook's distance?
> * If yes, with what package?
> * What would be the rule of thumb for glmer() with binomial link for Cook's distance?
>
An inappropriate link function will lead to nonlinearity of the
response on the linear-predictor scale, so the first thing to check is
the fitted vs. residual plot (with a smoothed line added so you can see
the trends): either
plot(fitted_model, type=c("p", "smooth"))
(maybe include pch="." since your data set is big)
or the analog via ggplot+broom.mixed: use broom.mixed::augment() to get
a data frame including .fitted and .resid, then plot it with
geom_point() and geom_smooth().
There are "goodness-of-link" tests that might be generalizable to
GLMMs, but I'm not too familiar with them.
2. There is an influence.merMod method for GLMM fits (it may be slow
for large data sets! You may want to set ncores>1). The 'car' package
has some additional functionality for plotting etc.
I'm not sure about rules of thumb.
If you are going to fit a mixed model with two binary observations
per cluster, you will be far from the range where PQL/Laplace/etc. are
going to be applicable; sonsider using nAGQ>1 to fit with Gauss-Hermite
quadrature.
> Thank you in advance for your time.
>
> Best,
>
> Hedyeh Ahmadi, Ph.D.
> Applied Statistician
> Keck School of Medicine
> Department of Preventive Medicine
> University of Southern California
>
> Postdoctoral Scholar
> Institute for Interdisciplinary Salivary Bioscience Research (IISBR)
> University of California, Irvine
>
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