title: What is the max number of variables once can use in an exhaustive all-subsets regression using glmulti()
body: <p>I am using the <code>glmulti()</code> package in R to try and run an all-subset regression on some data. I have 51 predictors, all with a maximum of 276 observations. I realize that the exhaustive and genetic algorithm approaches cannot compute with this many variables as I receive the following:</p>
  
  <pre><code>Warning message:
  In glmulti(y = "Tons_N", data = MDatEB1_TonsN, level = 1, method = "h",  :
    !Too many predictors.
  </code></pre>
  
  <p>With these types of requirements (i.e. many variables with lots of observations), how many will I be able to use in a single run of the all-subsets regression? I am looking into variable elimination techniques but I would like to use as many variables as possible in this stage of the analysis. That is, I want to use the results of this analysis to make variable elimination decisions. Is there another package that can process more variables at a time?</p>
  
  <p>Here is the code I am using. Unfortunately, because of the confidentiality associated with the project, I cannot attach datasets.</p>
  
  <p><code>TonsN_AllSubset &lt;- glmulti(Tons_N ~ ., data = MDatEB1_TonsN, level = 1, method = "h",crit = "aic", confsetsize = 20, plotty = T, report = T,fitfunction = "glm")</code></p>
  
  <p>I am relatively new to this package and modeling in general. Any direction or advice will be greatly appreciated. Thank you!</p>