title: lme4 and languageR compatibility error: "input model is not a mer object”
body: <p>I have a dataset dat2 on which I want to fit a linear mixed-effects model. I used lmer() (package lme4) in the past in complement of pvals.fnc to compute the associated p-values. </p>
  
  <p>However, since I reinstalled R 3.0.2 version with the new lme4 (1.0.4) and languageR (1.4) packages I obtain an error about the output of lmer function. It says that the output is not a mer object. Indeed its class is lmeRmod. </p>
  
  <p>Here is the code I use:</p>
  
  <pre><code>names(dat2)&lt;-c("auc","subj","decod","soa","vis")
  attach(dat2)
  mod1 &lt;- lmer(auc ~ decod + (1 | subj))
  mod2 &lt;- lmer(auc ~ vis+ (1 | subj))
  mod3 &lt;- lmer(auc ~ decod + vis + (1 | subj))
  mod4 &lt;- lmer(auc ~ decod + vis + decod*vis + (1 | subj))
  pvals.fnc(mod1)
  </code></pre>
  
  <p>And I get this error:</p>
  
  <pre><code>&gt; pvals.fnc(mod1)
  the input model is not a mer object
  NULL
  </code></pre>
  
  <p>Indeed, when I look at mod1, I find it is a lmeRmod object and not a mer object.</p>
  
  <pre><code>&gt; mod1
  Linear mixed model fit by REML ['lmerMod']
  Formula: auc ~ decod + (1 | subj) 
  REML criterion at convergence: -213.3884 
  Random effects:
   Groups   Name        Std.Dev.
   subj     (Intercept) 0.04187 
   Residual             0.11087 
  Number of obs: 155, groups: subj, 6
  Fixed Effects:
  (Intercept)       decod2       decod3       decod4  
       0.9798      -0.1141      -0.3599      -0.3090 
  </code></pre>
  
  <p>This problem is very similar to the one discribed <a href="https://stackoverflow.com/questions/19046076/languager-error-input-model-is-not-a-mer-object">here</a>. Any ideas of 1/ what the problem might be (why do I do not output a mer object) and 2/ how to work around it (I tried reinstalling older versions but I have compatibility problems between packages) ?</p>
  
  <p>Any help would be great !
  thanks !</p>