Is there a way of getting “marginal effects” from a `glmer` object

Question

I am estimating random effects logit model using glmer and I would like to report Marginal Effects for the independent variables. For glm models, package mfx helps compute marginal effects. Is there any package or function for glmer objects?

Thanks for your help.

A reproducible example is given below

mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
mydata$rank <- factor(mydata$rank) #creating ranks
id <- rep(1:ceiling(nrow(mydata)/2), times=c(2)) #creating ID variable
mydata <- cbind(mydata,data.frame(id,stringsAsFactors=FALSE)) 
set.seed(12345)
mydata$ran <- runif(nrow(mydata),0,1) #creating a random variable

library(lme4)
cfelr <- glmer(admit ~ (1 | id) + rank + gpa + ran + gre, data=mydata ,family = binomial)
summary(cfelr)

Answer 1

This is a much less technical answer, but perhaps provides a useful resource. I am a fan of the sjPlot package which provides plots of marginal effects of glmer objects, like so:

library(sjPlot)
sjp.glmer(cfelr, type = "eff")

The package provides a lot of options for exploring a glmer model's fixed and random effects as well. https://github.com/strengejacke/sjPlot

Cheers, Ben

Answer 2

You could use the ggeffects-package (examples in the package-vignettes). So, for your code this might look like this:

library(ggeffects)
# dat is a data frame with marginal effects
dat <- ggpredict(cfelr, term = "rank")
plot(dat)

or you use, as Benjamin described, the You could use the sjPlot-package, using the plot_model() function with plot-type "pred" (this simply wraps the ggeffects package for marginal effect plots):

library(sjPlot)
plot_model(cfelr, type = "pred", term = "rank")

Answer 3

Here's an approach using the margins() package:

library(margins)
library(lme4)

gm1 <- glmer(cbind(incidence, size - incidence) ~ period +
                 (1 | herd),
             data = cbpp,
             family = binomial)

m <- margins(gm1, data = cbpp)
m

Answer 4

My solution does not answer the question,

"Is there a way of getting “marginal effects” from a glmer object",

but rather,

"Is there a way of getting marginal logistic regression coefficients from a conditional logistic regression with one random intercept?"

I am only offering this write-up because the reproducible example provided was a conditional logistic regression with one random intercept and I'm intending to be helpful. Please do not downvote; I will take down if this answer is deemed too off topic.

The R-code is based on the work of Patrick Heagerty (click "View Raw" to see pdf), and I include a reproducible example below from my github version of his lnMLE package (excuse the warnings at installation -- I'm shoehorning Patrick's non-CRAN package). I'm omitting the output for all except the last line, compare, which shows the fixed effect coefficients side-by-side.

library(devtools)
install_github("lnMLE_1.0-2", "swihart")
library(lnMLE)
## run the example from the logit.normal.mle help page
## see also the accompanying document (click 'View Raw' on page below:)
## https://github.com/swihart/lnMLE_1.0-2/blob/master/inst/doc/lnMLEhelp.pdf
data(eye_race)
attach(eye_race)
marg_model <- logit.normal.mle(meanmodel = value ~ black,
                           logSigma= ~1,
                           id=eye_race$id,
                           model="marginal",
                           data=eye_race,
                           tol=1e-5,
                           maxits=100,
                           r=50)
marg_model
cond_model <- logit.normal.mle(meanmodel = value ~ black,
                           logSigma= ~1,
                           id=eye_race$id,
                           model="conditional",
                           data=eye_race,
                           tol=1e-5,
                           maxits=100,
                           r=50)
cond_model
compare<-round(cbind(marg_model$beta, cond_model$beta),2)
colnames(compare)<-c("Marginal", "Conditional")
compare

The output of the last line:

compare

            Marginal Conditional

(Intercept)    -2.43       -4.94

black           0.08        0.15

I attempted the reproducible example given, but had problems with both the glmer and lnMLE implementations; again I only include output pertaining to the comparison results and the warnings from the glmer() call:

##original question / answer... glmer() function gave a warning and the lnMLE did not fit well...
mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv")
mydata$rank <- factor(mydata$rank) #creating ranks
id <- rep(1:ceiling(nrow(mydata)/2), times=c(2)) #creating ID variable
mydata <- cbind(mydata,data.frame(id,stringsAsFactors=FALSE))
set.seed(12345)
mydata$ran <- runif(nrow(mydata),0,1) #creating a random variable

library(lme4)
cfelr <- glmer(admit ~ (1 | id) + rank + gpa + ran + gre, 
               data=mydata,
               family = binomial)

Which gave:

Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.00161047 (tol = 0.001, component 2)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?

but I foolishly went on without rescaling, trying to apply the logit.normal.mle to the example given. However, the conditional model doesn't converge or produce standard error estimates,

summary(cfelr)
library(devtools)
install_github("lnMLE_1.0-2", "swihart")
library(lnMLE)

mydata$rank2 = mydata$rank==2
mydata$rank3 = mydata$rank==3
mydata$rank4 = mydata$rank==4

cfelr_cond =  logit.normal.mle(meanmodel = admit ~ rank2+rank3+rank4+gpa+ran+gre, 
                               logSigma = ~1 , 
                               id=id, 
                               model="conditional", 
                               data=mydata, 
                               r=50, 
                               tol=1e-6, 
                               maxits=500)
cfelr_cond


cfelr_marg =  logit.normal.mle(meanmodel = admit ~ rank2+rank3+rank4+gpa+ran+gre,
                               logSigma = ~1 , 
                               id=id, 
                               model="marginal", 
                               data=mydata, 
                               r=50, 
                               tol=1e-6, 
                               maxits=500)
cfelr_marg


compare_glmer<-round(cbind(cfelr_marg$beta, cfelr_cond$beta,summary(cfelr)$coeff[,"Estimate"]),3)
colnames(compare_glmer)<-c("Marginal", "Conditional","glmer() Conditional")
compare_glmer

The last line of which reveals that the conditional model from cfelr_cond did not evaluate a conditional model but just returned the marginal coefficients and no standard errors.

>     compare_glmer

            Marginal Conditional glmer() Conditional

(Intercept)   -4.407      -4.407              -4.425

rank2         -0.667      -0.667              -0.680

rank3         -1.832      -1.833              -1.418

rank4         -1.930      -1.930              -1.585

gpa            0.547       0.548               0.869

ran            0.860       0.860               0.413

gre            0.004       0.004               0.002

I hope to iron out these issues. Any help/comments appreciated. I'll give status updates when I can.