title: mice: glm.fit: algorithm did not converge
body: <p>I have a dataset with about 12 categorical variables with levels ranging from 2 - 10, as well as other numerical variables. About 280 records. I'm using the <code>mice</code> package in <code>r</code> to perform imputation on the missing data with all default settings. 
  However, when I try to do the imputation like this:</p>
  
  <pre><code>imp &lt;- mice(df)
  </code></pre>
  
  <p>I continue to get this warning:</p>
  
  <pre><code>glm.fit: algorithm did not converge
  </code></pre>
  
  <p>The solutions I found online <a href="https://stackoverflow.com/questions/8596160/why-am-i-getting-algorithm-did-not-converge-and-fitted-prob-numerically-0-or">here</a> and <a href="https://stats.stackexchange.com/questions/5354/logistic-regression-model-does-not-converge">here</a> only focus on using the <code>glm</code> function directly, but in my case, it's a function that's called from within <code>mice</code>. I've tried setting <code>maxit = 50</code>, like this</p>
  
  <pre><code>imp &lt;- mice(df, maxit = 50)
  </code></pre>
  
  <p>but only ended up getting many more instances of the same warning. Any idea what could be causing this?</p>