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, p

Answer 1

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.

Answer 2

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 3

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 4

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