title: Fixed effects regression in R (with a very large number of dummy variables)
body: <p>Is there an easy way to do a fixed-effects regression in R when the number of dummy variables leads to a model matrix that exceeds the R maximum vector length? E.g., </p>
  
  <pre><code>&gt; m &lt;- lm(log(bid) ~ after + I(after*score) + id, data = data)
  Error in model.matrix.default(mt, mf, contrasts) : 
  cannot allocate vector of length 905986769
  </code></pre>
  
  <p>where id is a factor (and is the variable causing the problem above). </p>
  
  <p>I know that I could go through and de-mean all the data, but this throws the standard errors off (yes, you could compute the SE's "by hand" w/ a df adjustment but I'd like to minimize the probability that I'm introducing new errors). I've looked at the plm package but it seems only designed for classical panel data w/ a time component, which is not the structure of my data.  </p>