title: How to cope with a singular fit in a linear mixed model (lme4)?
body: <p>I am running several linear mixed models for an study about birds with the variable nest as a random variable. The thing is that in some of these models I get what is called 'singular fit': my nest random variable has a variance and st error of 0.00.</p>
  
  <p>Some background: I am working with wild birds to see the effect of living in noisy environments on some oxidative stress parameters. For this, we took a blood sample for each of the nestlings of each nest to do the laboratory stuff. Because of the limited blood sample, some oxidative stress parameters couldn't be measured for every nestling. </p>
  
  <pre><code>model &lt;- lmer(antioxidant_capacity~age+sex+clutch+zone+(1|nestID),
   data=data, contrasts=list(sex=contr.sum, zon=contr.sum, clutch=contr.sum))
  </code></pre>
  
  <p>Then I get: </p>
  
  <pre><code>singular fit
  </code></pre>
  
  <p>This is the table: </p>
  
  <pre><code>REML criterion at convergence: 974.3
  
  Scaled residuals: 
   Min       1Q   Median       3Q      Max 
  -2.72237 -0.61737  0.06171  0.69429  2.88008 
  
  Random effects:
  Groups   Name        Variance     Std.Dev.
  nestID (Intercept)      0          0.00   
  Residual               363        19.05   
  Number of obs: 114, groups:  nido_mod, 46
  
  Fixed effects:
          Estimate      Std. Error  df        t value Pr(&gt;|t|)    
  (Intercept) 294.5970    36.8036  109.0000   8.005   1.41e-12 ***
  age          -0.2959     3.0418  109.0000  -0.097   0.922685    
  clutch1      -0.5242     2.0940  109.0000  -0.250   0.802804    
  sex1          2.3167    1.8286 109.0000     1.267   0.207885    
  zone1         6.2274     1.7958  109.0000   3.468   0.000752 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Correlation of Fixed Effects:
        (Intr) age  clutch1 sex1 
  age  -0.999                     
  clutch1   0.474 -0.465              
  sex1      0.060 -0.054 -0.106       
  zone1    -0.057  0.061 -0.022  0.058
  convergence code: 0
  singular fit
  </code></pre>
  
  <p>I have read about singularity problems and if I have understood well, the singularity is related to overfitting. Could this be due to that for some response variables I have nests with only one nestling while there are nest with more nestlings? How can I solve this? Any recommendation?</p>
  
  <p>Thank you, so much.</p>