Confidence intervals for predicted probabilities in mixed-effect regression?

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I'm working with mixed-effect logistic regression models using a single random variable(using glmer), and I am struggling to find a way to produce predicted probabilities and the respective 95% CI's. I have been able to do this for fixed-effect models using the following type of code:

Call:
  glm(formula = survive/trials ~ class, family = binomial(logexp(vespdata$expos)), 
  data = vespdata)

Deviance Residuals: 
Min       1Q   Median       3Q      Max  
-2.6823   0.2621   0.4028   0.4540   0.6935  

Coefficients:
          Estimate Std. Error z value Pr(>|z|)    
(Intercept)   4.6774     0.5796   8.069 7.07e-16 ***
 class2       -1.3236     0.6957  -1.903   0.0571 .  
 class3       -0.5751     0.9170  -0.627   0.5306    
 class4       -1.0806     0.9217  -1.172   0.2411    
 class5       -1.2889     0.6564  -1.964   0.0496 *  
 class6       -1.5379     0.6508  -2.363   0.0181 *  
 class8       -1.2078     0.6957  -1.736   0.0825 .

vesppredict2 = with(vespdata, data.frame(class = gl(7,1))
vesppredict2 = cbind(vesppredict2, predict(vespclass.exp, newdata = vesppredict2, 
type = "link", se = TRUE))
vesppredict2 = within(vesppredict2,
{PredictedProb = (plogis(fit))^23 
LL = (plogis(fit - (1.96 * se.fit)))^23 
UL = (plogis(fit + (1.96 * se.fit)))^23
ErrorBar = (UL-PredictedProb)
})

The problem I'm having is that predict() cannot use the argument se = TRUE for mixed-effect models. I tried adding the argument re.form = NA but to no avail. I'd be grateful for any tips!

asked 47 secs ago

user3500114

3

Confidence intervals for predicted probabilities in mixed-effect regression?