title: Handling missing data using aregImpute() in R
body: <p>I am new to R and I am using <code>rms</code> package for handling imputations. I noticed that the <code>aregImpute</code> function in <code>rms</code> only returns column values which has NA values.</p>
  
  <pre><code>impute &lt;- aregImpute(Y~X1+X2+X3+X4+X5+X6,data= train_data, n.impute=5, nk=0)
  impute$imputed$Y
  </code></pre>
  
  <p>When I tried to find the target value in the imputed data set using <code>impute$imputed$Y</code>, it returned <code>NULL</code>. According to my understanding, because target variable had no <code>NA</code> values I got <code>NULL</code>. My query is that how can I combine imputed dataset with the original dataset so that I will have the complete data set with no <code>NA</code> values. I actually want to try out various algorithm like <code>Decision Tree</code>, <code>Random Forest</code> with the imputed data. Does anyone have any suggestion. It would be a great help.</p>