title: Efficient method to subset drop rows with NA values in R
body: <p><strong>Background</strong>
  Before running a stepwise model selection, I need to remove missing values for any of my model terms. With quite a few terms in my model, there are therefore quite a few vectors that I need to look in for NA values (and drop any rows that have NA values in any of those vectors). However, there are also vectors that contain NA values that I do not want to use as terms / criteria for dropping rows.</p>
  
  <p><strong>Question</strong>
  How do I drop rows from a dataframe which contain NA values for any of a list of vectors? I'm currently using the clunky method of a long series of !is.na's</p>
  
  <pre><code>&gt; my.df[!is.na(my.df$termA)&amp;!is.na(my.df$termB)&amp;!is.na(my.df$termD),]
  </code></pre>
  
  <p>but I'm sure that there is a more elegant method.</p>