Numerically deriving the MLEs of GLMM is difficult and, in practice, I know, we should not use brute force optimization (e.g., using optim in a simple way). But for my own educational purpose, I want to try it to make sure I correctly understand the model (see the code below). I found that I always get inconsistent results from glmer().

In particular, even if I use the MLEs from glmer as initial values, according to the likelihood function I wrote (negloglik), they are not MLEs (opt1$value is smaller than opt2). I think two potential reasons are:

  1. negloglik is not written well so that there is too much numerical error in it, and
  2. the model specification is wrong. For the model specification, the intended model is:

\begin{equation} L=\prod_{i=1}^{n} \left(\int_{-\infty}^{\infty}f(y_i|N,a,b,r_{i})g(r_{i}|s)dr_{i}\right) \end{equation} where $f$ is a binomial pmf and $g$ is a normal pdf. I am trying to estimate $a$, $b$, and $s$. In particular, I want to know if the model specification is wrong, what the correct specification is.

p <- function(x,a,b) exp(a+b*x)/(1+exp(a+b*x))

a <- -4  # fixed effect (intercept)
b <- 1   # fixed effect (slope)
s <- 1.5 # random effect (intercept)
N <- 8
x <- rep(2:6, each=20)
n <- length(x) 
id <- 1:n
r  <- rnorm(n, 0, s) 
y  <- rbinom(n, N, prob=p(x,a+r,b))

negloglik <- function(p, x, y, N){
  a <- p[1]
  b <- p[2]
  s <- p[3]

  Q <- 100  # Inf does not work well
  L_i <- function(r,x,y){
    dbinom(y, size=N, prob=p(x, a+r, b))*dnorm(r, 0, s)

  -sum(log(apply(cbind(y,x), 1, function(x){ 

(model <- glmer(cbind(y,N-y)~x+(1|id),family=binomial))

opt0 <- optim(c(fixef(model), sqrt(VarCorr(model)$id[1])), negloglik, 
                x=x, y=y, N=N, control=list(reltol=1e-50,maxit=10000)) 
opt1 <- negloglik(c(fixef(model), sqrt(VarCorr(model)$id[1])), x=x, y=y, N=N)
opt0$value  # negative loglikelihood from optim
opt1        # negative loglikelihood using glmer generated parameters
-logLik(model)==opt1 # but these are substantially different...

A simpler example

To reduce the possibility of having large numerical error, I created a simpler example.

y  <- c(0, 3)
N  <- c(8, 8)
id <- 1:length(y)

negloglik <- function(p, y, N){
  a <- p[1]
  s <- p[2]
  Q <- 100  # Inf does not work well
  L_i <- function(r,y){
    dbinom(y, size=N, prob=exp(a+r)/(1+exp(a+r)))*dnorm(r,0,s)
  -sum(log(sapply(y, function(x){
    integrate(L_i,lower=-Q, upper=Q, y=x, rel.tol=1e-14)$value

(model <- glmer(cbind(y,N-y)~1+(1|id), family=binomial))
MLE.glmer <- c(fixef(model), sqrt(VarCorr(model)$id[1]))
opt0 <- optim(MLE.glmer, negloglik, y=y, N=N, control=list(reltol=1e-50,maxit=10000)) 
MLE.optim <- opt0$par
MLE.glmer # MLEs from glmer
MLE.optim # MLEs from optim

L_i <- function(r,y,N,a,s) dbinom(y,size=N,prob=exp(a+r)/(1+exp(a+r)))*dnorm(r,0,s)

L1 <- integrate(L_i,lower=-100, upper=100, y=y[1], N=N[1], a=MLE.glmer[1], 
                s=MLE.glmer[2], rel.tol=1e-10)$value
L2 <- integrate(L_i, lower=-100, upper=100, y=y[2], N=N[2], a=MLE.glmer[1], 
                s=MLE.glmer[2], rel.tol=1e-10)$value

(log(L1)+log(L2)) # loglikelihood (manual computation)
logLik(model)     # loglikelihood from glmer 
  • $\begingroup$ are the MLEs (not the log-likelihoods themselves) comparable? That is, are you just off by a constant? $\endgroup$
    – Ben Bolker
    May 27, 2014 at 0:10
  • 1
    $\begingroup$ The estimated MLEs are clearly different (MLE.glmer and MLE.optim) especially for the random effect (see the new example), so it is not just based on some constant factor in likelihood values, I think. $\endgroup$
    – quibble
    May 27, 2014 at 5:46
  • 4
    $\begingroup$ @Ben Setting a high value of nAGQ in glmer made the MLEs comparable. The default precision of glmer was not very good. $\endgroup$
    – quibble
    May 28, 2014 at 0:23
  • 5
    $\begingroup$ Linking to a similar lme4 question that @Steve Walker helped me out with: stats.stackexchange.com/questions/77313/… $\endgroup$
    – Ben Ogorek
    Aug 2, 2015 at 21:31
  • 3
    $\begingroup$ As an older question w/ a lot of upvotes, this could probably be grandfathered. I don't see a need for this to be closed. $\endgroup$ Nov 25, 2016 at 14:23

1 Answer 1


Setting a high value of nAGQ in the glmer call made the MLEs from the two methods equivalent. The default precision of glmer was not very good. This settles the issue.


See @SteveWalker's answer here Why can't I match glmer (family=binomial) output with manual implementation of Gauss-Newton algorithm? for more details.

  • 1
    $\begingroup$ But the estimated loglikelihoods are very different (presumably by some constant), so the different methods should not be mixed. $\endgroup$
    – quibble
    May 28, 2014 at 0:35
  • 1
    $\begingroup$ hmm, interesting/surprising -- thanks for setting up this example, I'll try to find time to look into it. $\endgroup$
    – Ben Bolker
    May 28, 2014 at 1:50

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