Question

As many other people, I'm having troubles running a model which uses glmer function from package lme4.

Here is my model:

model = glmer(depvar ~ variety*cover+amplitude+time+ (1|pp) + (1|stim), 
  data = datafile, family=poisson)

And here is the warning I get:

Warning message:
In checkConv(attr(opt, &quot;derivs&quot;), opt$par, 
  ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.00606839 
   (tol = 0.001, component 1)

I read at this link that if I add

control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000))

at the end of my model, I solve the issue. I tried, so my model is now:

model = glmer(depvar ~ variety*cover+amplitude+time+ 
   (1|pp) + (1|stim), data = datafile, family=poisson,
    control=glmerControl(optimizer="bobyqa",
            optCtrl=list(maxfun=100000)))

and it works without giving any warning message.

I would like to ask whether someone could explain what I am adding to the model, because I am not sure if I understand it. Also, is this an acceptable solution to solve the warning issue? Or anyone solved it in a different way?

Many thanks.

The output without control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000))) is:

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: poisson  ( log )
Formula: depvar ~ variety * cover + amplitude +  time +      (1 | pp) + (1 | stim)
   Data: datafile

     AIC      BIC   logLik deviance df.resid 
  6916.6   6963.1  -3450.3   6900.6     2473 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-0.8955 -0.4712 -0.2797  0.3163  3.0090 

Random effects:
 Groups Name        Variance Std.Dev.
 stim   (Intercept) 0.031757 0.17821 
 pp     (Intercept) 0.008918 0.09443 
Number of obs: 2481, groups:  stim, 200; pp, 28

Fixed effects:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            0.77480    0.21459   3.611 0.000305 ***
variety2-1             0.04813    0.03096   1.555 0.119969    
cover2-1               0.06725    0.03096   2.172 0.029862 *  
amplitude             -0.04704    0.02685  -1.752 0.079837 .  
time                  -0.02545    0.03747  -0.679 0.496943    
variety2-1:cover2-1    0.01435    0.06170   0.233 0.816128    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
convergence code: 0
Model failed to converge with max|grad| = 0.00606839 (tol = 0.001,     component 1)

The output with control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000))) is:

Generalized linear mixed model fit by maximum likelihood (Laplace     Approximation) ['glmerMod']
Family: poisson  ( log )
Formula: depvar ~ variety * cover + amplitude + time +      (1 | pp) + (1 | stim)
 Data: datafile
Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))

 AIC      BIC   logLik deviance df.resid 
 6916.6   6963.1  -3450.3   6900.6     2473 

    Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-0.8956 -0.4712 -0.2797  0.3163  3.0090 

 Random effects:
 Groups Name        Variance Std.Dev.
 stim   (Intercept) 0.031759 0.17821 
 pp     (Intercept) 0.008917 0.09443 
 Number of obs: 2481, groups:  stim, 200; pp, 28

Fixed effects:
                       Estimate Std. Error z value Pr(>|z|)    
(Intercept)            0.77480    0.21457   3.611 0.000305 ***
variety2-1             0.04813    0.03096   1.555 0.119997    
cover2-1               0.06725    0.03096   2.172 0.029860 *  
amplitude             -0.04703    0.02685  -1.751 0.079861 .  
time                  -0.02545    0.03746  -0.679 0.496889    
variety2-1:cover2-1    0.01434    0.06170   0.232 0.816160    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Answer 1

由于模型拟合之间的可能性相差<0.1，并且参数的最大相对差异大约为10 ^（ - 4），我会说您已成功证明警告是误报，你可以继续你的初始模型。

将优化器切换到"bobyqa"并扩展最大迭代次数以抑制警告是无害的（除非浪费计算机时间），但不是必需的。

solution to the warning message using glmer

1 个答案: