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I tried to create mixed-effect logistic regression model using glmer() function, however the model does not converge. Firstly, I changed categorical variables to from vectors to factors.

schwa_completed_2$Outcome <- as.factor(schwa_completed_2$Outcome)
schwa_completed_2$frequency_grouped <- as.factor(schwa_completed_2$frequency_grouped)
schwa_completed_2$sonority_grouped <- as.factor(schwa_completed_2$sonority_grouped)
schwa_completed_2$participant_gender <- as.factor(schwa_completed_2$participant_gender) 
schwa_completed_2$participant_age_group <- as.factor(schwa_completed_2$participant_age_group)
schwa_completed_2$Speaker <- as.factor(schwa_completed_2$Speaker)

Also there is one more continuous variable. Then I created a model

model <- glmer(Outcome ~ frequency_grouped + sonority_grouped + syl_sec_EN +
                         participant_gender + participant_age_group + 1|Speaker,
           data = schwa_completed_2, family = binomial, optimizer = "bobyqa")

Unfortunately, the model does not converge. If I got rid off "Speaker" effect the model works just fine, however, the results probably are skewed.

Warning messages:
1: In commonArgs(par, fn, control, environment()) :
maxfun < 10 * length(par)^2 is not recommended.
2: In optwrap(optimizer, devfun, start, rho$lower, control = control, :
convergence code 1 from bobyqa: bobyqa -- maximum number of function
evaluations exceeded
3: In (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
failure to converge in 10000 evaluations
4: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0785481 (tol = 0.001, component 1) 

Generalized linear mixed model fit by maximum likelihood (Laplace 
Approximation) ['glmerMod']
Family: binomial  ( logit )
Formula: Outcome ~ frequency_grouped + sonority_grouped + syl_sec_EN +  
participant_gender + participant_age_group + 1 | Speaker
Data: schwa_completed_2

 AIC      BIC   logLik deviance df.resid 
1820.8   2066.1   -864.4   1728.8     1486 

Scaled residuals: 
Min      1Q  Median      3Q     Max 
-2.5957 -0.6255 -0.3987  0.7714  3.4432 

Random effects:
Groups  Name                                 Variance Std.Dev. Corr                         
Speaker (Intercept)                          2.08476  1.4439                                
        frequency_groupedmoderately_frequent 0.78914  0.8883   -0.15                        
        frequency_groupedvery_frequent       3.07514  1.7536   -0.90  0.35                  
        sonority_groupedsonorants            1.33795  1.1567    0.82 -0.44 -0.91            
        sonority_groupedstops                1.76849  1.3298    0.02 -0.42 -0.36  0.51      
        sonority_groupedvowels               2.97690  1.7254    0.23  0.02 -0.32  0.55  0.77
        syl_sec_EN                           0.03217  0.1794   -0.62 -0.42  0.32 -0.44  0.11 -0.52  
        participant_genderM                  0.41458  0.6439   -0.86 -0.18  0.77 -0.77 -0.24 -0.62  0.82 
        participant_age_groupY               0.52428  0.7241    0.46  0.80 -0.20  0.06 -0.44  0.08 -0.73 -0.63

Number of obs: 1532, groups:  Speaker, 40

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.7650     0.1862  -4.108 3.99e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
convergence code: 0
Model failed to converge with max|grad| = 0.0785481 (tol = 0.001, component 1)
failure to converge in 10000 evaluations

Is it because of the too complicated model or my laptop is not powerful enough? I don't know what should I do at this point. Is very anything I can do to fix this?

user20650
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phnlg
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  • did you mean for all variables to be random (and no fixed effects) , or did you mean `... + (1|Speaker)` – user20650 Jun 01 '18 at 15:18
  • ps more generally `?convergence` gives things to try – user20650 Jun 01 '18 at 15:21
  • voted to close as op non-responsive – user20650 Jun 05 '18 at 11:18
  • Sorry for not responding, what helped was using scale() function on syl_sec_EN variable – phnlg Jun 05 '18 at 19:22
  • ha, no problem ... just thought you had done a runner (now unvoted to close). But glad you worked it out. Maybe consider writing it as an answer?? (ps it does seem a bit non-standard to include terms as random effects but not as fixed effects?) – user20650 Jun 05 '18 at 19:26

1 Answers1

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Ok, so what helped me was grouping the speakers with group by, and then scale the syl_sec_EN variable

phnlg
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