title: POST HOC 3 levels for glmmTMB
body: <p>Good morning,
  I am running a glmmTMB model with beta distribution in which I have a response that is a proportion, two main testing predictors, their interaction and a control predictor:</p>
  <ul>
  <li>Condition : 3 levels predictor (&quot;Propcibo&quot;; &quot;pronocibo&quot;; &quot;solo cibo&quot;)</li>
  <li>Phase : 3 levels predictor (&quot;phase1&quot;; &quot;phase3&quot;; &quot;phase4&quot;)</li>
  <li>C.a: two levels (&quot;puppies&quot; and &quot;adults&quot;)</li>
  </ul>
  <p>model: full.looking.apparatus=glmmTMB(tr.proportion~fasi*condition+c.a+(1|subject),
  family=beta_family(link=&quot;logit&quot;), data=LOOKAPP_data)</p>
  <p>The reference values are the fasi: fasi3 and the condition:&quot;propcibo&quot;</p>
  <p>the output
  <a href="https://i.stack.imgur.com/w7Rw8.png" rel="nofollow noreferrer">the output of the model</a></p>
  <p>I have some problems interpreting the estimates about the interaction with both predictors with 3 levels:
  the line with &quot;fasi1:conditionpropnocibo&quot; give me information about the difference of the comparison between fase1 and fase3 in the condition &quot;propnocibo&quot; compared to the condition &quot;propcibo&quot;.</p>
  <p>Furthermore I was looking for a function to perform posthoc analysis to the glmmTMB model. In particular I would be interested in the comparison between the phase3 of the three different conditions.</p>
  <p>I didn't find any example with factors with 3 levels and since I am a beginner with R I find difficult to adapt different examples to my case.</p>
  <p>Thank you in advance to anyone who will consider my question.</p>
  <p>Cheers,</p>
  <p>Giulia</p>