title: Understand warning messages for mixed model in r lme4
body: <p>I have built and run a mixed effects logistic regression model in the lme4 package for r to estimate the probability of occupancy of fishes in different locations (cells/habitats). The data frame consists of 1,207,140 observations of 68 individual fish. For each individual (per day for ~ 1 year) it describes the the number of occurrences at each unique location relative to the total number of occurrences in all locations.</p>
  
  <p>Here is the base model:</p>
  
  <pre><code>    m.base = glmer(cbind(N,t.move-N) ~ jdate + snSurface.Area + Restoration..P.A. +    
      Release.Location+ Sex + (1|Station) + (0 + jdate|ID), data=allfishdat, family=binomial)
  where N=# unique positions, t.move=total positions, jdate=julian date, Station=locations, ID=fish ID
  </code></pre>
  
  <p>I get the following warning message:</p>
  
  <pre><code>Warning messages:
  1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
                  Model failed to converge with max|grad| = 3349.26 (tol = 0.001)
  2: In if (resHess$code != 0) { :
   the condition has length &gt; 1 and only the first element will be used
  3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
   Model is nearly unidentifiable: very large eigenvalue
   - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
   - Rescale variables?
  </code></pre>
  
  <p>I done some searching to try to understand what these messages mean and their implications on the model, but have not yet understood the warnings.</p>