title: How do you correctly perform a glmmPQL on non-normal data?
body: <p>I ran a model using <code>glmer</code> looking at the effect that Year and Treatment had on the number of points covered with wood, then plotted the residuals to check for normality and the resulting graph is slightly skewed to the right. Is this normally distributed?</p>
  
  <pre><code>model &lt;- glmer(Number~Year*Treatment(1|Year/Treatment), data=data,family=poisson)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/MKimm.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/MKimm.png" alt="enter image description here"></a></p>
  
  <p>This site recommends using <code>glmmPQL</code> if your data is not normal: <a href="http://ase.tufts.edu/gsc/gradresources/guidetomixedmodelsinr/mixed%20model%20guide.html" rel="nofollow noreferrer">http://ase.tufts.edu/gsc/gradresources/guidetomixedmodelsinr/mixed%20model%20guide.html</a></p>
  
  <pre><code>library(MASS)
  library(nlme)
  
  model1&lt;-glmmPQL(Number~Year*Treatment,~1|Year/Treatment,
                  family=gaussian(link = "log"),   
                  data=data,start=coef(lm(Log~Year*Treatment)),
                  na.action = na.pass,verbose=FALSE)
  summary(model1)
  plot(model1)
  </code></pre>
  
  <p>Now do you transform the data in the Excel document or in the R code (<code>Number1 &lt;- log(Number)</code>) before running this model? Does the <code>link="log"</code> imply that the data is already log transformed or does it imply that it will transform it?</p>
  
  <p>If you have data with zeros, is it acceptable to add 1 to all observations to make it more than zero in order to log transform it: <code>Number1&lt;-log(Number+1)</code>?</p>
  
  <p>Is <code>fit&lt;-anova(model,model1,test="Chisq")</code> sufficient to compare both models?</p>
  
  <p>Many thanks for any advice!</p>