lme4 1.1-27.1 error: pwrssUpdate did not converge in (maxit) iterations

Question

Sorry that this error has been discussed before, each answer on stackoverflow seems specific to the data

I'm attempting to run the following negative binomial model in lme4:

Model5.binomial<-glmer.nb(countvariable ~ waves + var1 + dummycodedvar2 + dummycodedvar3 + (1|record_id), data=datadfomit) 

However, I receive the following error when attempting to run the model:

Error in f_refitNB(lastfit, theta = exp(t), control = control) :pwrssUpdate did not converge in (maxit) iterations

I first ran the model with only 3 predictor variables (waves, var1, dummycodedvar2) and got the same error. But centering the predictors fixed this problem and the model ran fine.

Now with 4 variables (all centered) I expected the model to run smoothly, but receive the error again.

Since every answer on this site seems to point towards a problem in the data, data that replicates the problem can be found here:

https://file.io/3vtX9RwMJ6LF

Answer 1

Your response variable has a lot of zeros:

I would suggest fitting a model that takes account of this, such as a zero-inflated model. The GLMMadaptive package can fit zero-inflated negative binomial mixed effects models:

## library(GLMMadaptive)
## mixed_model(countvariable ~ waves + var1 + dummycodedvar2 + dummycodedvar3, ##   random = ~ 1 | record_id, data = data,
##   family = zi.negative.binomial(), 
##   zi_fixed = ~ var1,
##   zi_random = ~ 1 | record_id) %>% summary()

Random effects covariance matrix:
                StdDev    Corr
(Intercept)     0.8029        
zi_(Intercept)  1.0607 -0.7287

Fixed effects:
               Estimate Std.Err z-value  p-value
(Intercept)      1.4923  0.1892  7.8870  < 1e-04
waves           -0.0091  0.0366 -0.2492 0.803222
var1             0.2102  0.0950  2.2130 0.026898
dummycodedvar2  -0.6956  0.1702 -4.0870  < 1e-04
dummycodedvar3  -0.1746  0.1523 -1.1468 0.251451

Zero-part coefficients:
            Estimate Std.Err z-value    p-value
(Intercept)   1.8726  0.1284 14.5856    < 1e-04
var1         -0.3451  0.1041 -3.3139 0.00091993

log(dispersion) parameter:
  Estimate Std.Err
    0.4942  0.2859

Integration:
method: adaptive Gauss-Hermite quadrature rule
quadrature points: 11

Optimization:
method: hybrid EM and quasi-Newton
converged: TRUE