title: warning message glmer Model failed to converge
body: <p>I have seen questions about this on this forum (and have asked it myself before) but I haven't been able to solve my problem still, so I wil try again with more detailed information:</p>
  
  <p>I have a data set with a binomial dependent variable, 3 categorical fixed effects and 2 categorical random effects (item and subject). I want to to a mixed effects model using glmer. Here is what I entered in R:</p>
  
  <pre><code>modelall&lt;- glmer(moodR ~ group*context*condition + (1|subject) + ``(1|item), data=RprodHSNS, family="binomial")`
  </code></pre>
  
  <p>I then get the following warnings:</p>
  
  <pre><code>Warning messages:
  1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.02081 (tol = 0.001, component 11)
  2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model is nearly unidentifiable: large eigenvalue ratio
  - Rescale variables?`
  </code></pre>
  
  <p>This is what my summary looks like:</p>
  
  <pre><code>Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
  Family: binomial  ( logit )
  Formula: moodR ~ group * context * condition + (1 | subject) + (1 | item)
  Data: RprodHSNS`
  
  
  AIC      BIC   logLik deviance df.resid
  1400.0   1479.8   -686.0   1372.0     2195 `
  
  Scaled residuals: 
  Min      1Q  Median      3Q     Max 
  -8.0346 -0.2827 -0.0152  0.2038 20.6578 `
  
  Random effects:
  Groups  Name        Variance Std.Dev.
  item    (Intercept) 1.475    1.215   
  subject (Intercept) 1.900    1.378   
  Number of obs: 2209, groups:  item, 54; subject, 45
  Fixed effects:`
  Estimate Std. Error z value Pr(&gt;|z|)`                             
  (Intercept)                -0.61448   42.93639  -0.014 0.988582  
  group1                     -1.29254   42.93612  -0.030 0.975984    
  context1                    0.09359   42.93587   0.002 0.998261   
  context2                   -0.77262    0.22894  -3.375 0.000739***
  condition1                  4.99219   46.32672   0.108 0.914186
  group1:context1            -0.17781   42.93585  -0.004 0.996696
  group1:context2            -0.10551    0.09925  -1.063 0.287741
  group1:condition1          -3.07516   46.32653  -0.066 0.947075
  context1:condition1        -3.47541   46.32648  -0.075 0.940199
  context2:condition1        -0.07293    0.22802  -0.320 0.749087
  group1:context1:condition1  2.47882   46.32656   0.054 0.957328
  group1:context2:condition1  0.30360    0.09900   3.067 0.002165 **
  
  ---
  
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  Correlation of Fixed Effects:
              (Intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1
  group1      -1.000                                                                            
  context1    -1.000  1.000                                                                
  context2     0.001  0.000 -0.001                                                              
  condition1  -0.297  0.297  0.297  0.000                                                       
  grp1:cntxt1  1.000 -1.000 -1.000  0.001 -0.297                                                
  grp1:cntxt2  0.001  0.000  0.000 -0.123  0.000  0.000                                       
  grp1:cndtn1  0.297 -0.297 -0.297 -0.001 -1.000  0.297     0.000                               
  cntxt1:cnd1  0.297 -0.297 -0.297 -0.001 -1.000  0.297     0.001  1.000                        
  cntxt2:cnd1  0.000  0.000 -0.001  0.011  0.001  0.000    -0.197 -0.001    -0.001              
  grp1:cnt1:1 -0.297  0.297  0.297  0.001  1.000 -0.297    -0.001 -1.000    -1.000  0.001       
  grp1:cnt2:1  0.000  0.000  0.001 -0.198  0.000 -0.001     0.252  0.000     0.001 -0.136  0.000
  </code></pre>
  
  <p>Extremely high p-values, which does not seem to be possible. </p>
  
  <p>In a previous post I read that one of the problems could be fixed by increasing the amount of iterations by inserting this bit in the command: glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000))</p>
  
  <p>So here's the new command:</p>
  
  <pre><code>modelall&lt;- glmer(moodR ~ group*context*condition + (1|subject) + (1|item), data=RprodHSNS, family="binomial", glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000)))
  </code></pre>
  
  <p>I get one less warning, but the other one is still there:</p>
  
  <pre><code>&gt; Warning message:
  In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
    Model failed to converge with max|grad| = 0.005384 (tol = 0.001, component 7)
  </code></pre>
  
  <p>The summary still looks weird:</p>
  
  <pre><code>Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
   Family: binomial  ( logit )
  Formula: moodR ~ group * context * condition + (1 | subject) + (1 | item)
     Data: RprodHSNS
  Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))`
  
  AIC      BIC   logLik deviance df.resid 
  1400.0   1479.8   -686.0   1372.0     2195
  
  Scaled residuals: 
  Min      1Q  Median      3Q     Max 
  -8.0334 -0.2827 -0.0152  0.2038 20.6610 
  
  Random effects:
  Groups  Name        Variance Std.Dev.
  item    (Intercept) 1.474    1.214   
  subject (Intercept) 1.901    1.379   
  Number of obs: 2209, groups:  item, 54; subject, 45
  
  Fixed effects:
                          Estimate Std. Error z value Pr(&gt;|z|)    
  (Intercept)                -0.64869   26.29368  -0.025 0.980317    
  group1                     -1.25835   26.29352  -0.048 0.961830    
  context1                    0.12772   26.29316   0.005 0.996124    
  context2                   -0.77265    0.22886  -3.376 0.000735 ***
  condition1                  4.97325   22.80050   0.218 0.827335    
  group1:context1            -0.21198   26.29303  -0.008 0.993567    
  group1:context2            -0.10552    0.09924  -1.063 0.287681    
  group1:condition1          -3.05629   22.80004  -0.134 0.893365    
  context1:condition1        -3.45656   22.80017  -0.152 0.879500    
  context2:condition1        -0.07305    0.22794  -0.320 0.748612    
  group1:context1:condition1  2.45996   22.80001   0.108 0.914081    
  group1:context2:condition1  0.30347    0.09899   3.066 0.002172 ** 
  
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Correlation of Fixed Effects:
          (Intr) group1 cntxt1 cntxt2 cndtn1 grp1:cnt1 grp1:2 grp1:cnd1 cnt1:1 cnt2:1 g1:1:1
  group1      -1.000                                                                            
  context1    -1.000  1.000                                                                     
  context2     0.000  0.000  0.000                                                              
  condition1   0.123 -0.123 -0.123 -0.001                                                       
  grp1:cntxt1  1.000 -1.000 -1.000  0.001  0.123                                                
  grp1:cntxt2  0.001  0.000  0.000 -0.123  0.001  0.000                                         
  grp1:cndtn1 -0.123  0.123  0.123  0.000 -1.000 -0.123    -0.001                               
  cntxt1:cnd1 -0.123  0.123  0.123  0.000 -1.000 -0.123     0.000  1.000                        
  cntxt2:cnd1  0.000  0.000  0.000  0.011 -0.001  0.000    -0.197  0.001     0.001              
  grp1:cnt1:1  0.123 -0.123 -0.123  0.000  1.000  0.123     0.000 -1.000    -1.000 -0.001      
  grp1:cnt2:1  0.000 -0.001  0.001 -0.198  0.001 -0.001     0.252 -0.001     0.000 -0.136  0.000
  </code></pre>
  
  <blockquote>
    <p></p>
  </blockquote>
  
  <p>Does anyone have an idea what I can do to solve this? Or tell me what this warning even means? Please explain in a way that an R-newbie like myself can understand!</p>
  
  <p>Any help is much appreciated!</p>