[R-sig-ME] glmer.nb warning and prediction problem

Adriaan de Jong Adr|@@n@de@Jong @end|ng |rom @|u@@e
Fri Mar 4 13:01:23 CET 2022

Dear list members,

What I’m trying to do is to model a linear trend over the years (Years = Year – 1992, 1993 was the starting year of the 29 year data series) of bird numbers (“Total”) with a var/mean ratio of 2.85. The variables “Dag” (= day of the study period starting on April 1 = 1, in this subset ranging from 31 to 80) and “Dec_Hour” (= decimal hour between 4 AM and 9 PM) are nested random effects. The subset for this analysis (Trend1) contains 993 rows (=counting trips).

The mod5<-glmer.nb(Total~  Years + (1|Dag/Dec_Hour),data=Trend1) command under lme4 gives me the following warning message:

In theta.ml(Y, mu, weights = object using resp$weights, limit = limit,  :
  iteration limit reached

I presume I need to provide glmer.nb with a lmerControl string, but have no clue what it should contain.

I guess that the Dec_Hour may be causing the iteration limit problem. The model seems to converge, though, and the summary looks like this

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [
 Family: Negative Binomial(798.6507)  ( log )
Formula: Total ~ Years + (1 | Dag/Dec_Hour)
   Data: Trend1

     AIC      BIC   logLik deviance df.resid
  4921.7   4946.2  -2455.8   4911.7      988

Scaled residuals:
    Min      1Q  Median      3Q     Max
-1.9784 -0.6199 -0.0885  0.4511  3.8851

Random effects:
 Groups       Name        Variance Std.Dev.
 Dec_Hour:Dag (Intercept) 0.15182  0.3896
 Dag          (Intercept) 0.09292  0.3048
Number of obs: 993, groups:  Dec_Hour:Dag, 945; Dag, 50

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  2.09539    0.06025   34.78   <2e-16 ***
Years       -0.04078    0.00248  -16.44   <2e-16 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
Years -0.594

This makes sense to me, but I would like to predict the 1993 and 2021 endpoint "Total" values of the yearly trend (numerically Years = 1 and 29) given the peak values of observed birds found to be Dag=34 and Dec_Hour=7. For this I tried:


This rendered the message:
Error in levelfun(r, n, allow.new.levels = allow.new.levels) :
  new levels detected in newdata

Could anyone give me a hint on (a) how to avoid the iteration problem and (b) how to adjust the predict function? Thanks in advance for your help.


Adriaan "Adjan" de Jong
Associate professor
Swedish University of Agricultural Sciences
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