title: What values to look at in cross validated linear regression in DAAG package
body: <p>I performed the following on a data set that contains 151 variables with 161 observations:-</p>
  
  <pre><code>&gt; library(DAAG)
  &gt; fit &lt;- lm(RT..seconds.~., data=cadets)
  &gt; cv.lm(df = cadets, fit, m = 10)
  </code></pre>
  
  <p>And got the following results:- </p>
  
  <pre><code>fold 1 
  Observations in test set: 16 
                    7     11     12      24     33    38      52     67     72
  Predicted      49.6   44.1   26.4    39.8   53.3 40.33    47.8   56.7   58.5
  cvpred        575.0 -113.2  640.7 -1045.8  876.7 -5.93  2183.0 -129.7  212.6
  RT..seconds.   42.0   44.0   44.0    45.0   45.0 46.00    49.0   56.0   58.0
  CV residual  -533.0  157.2 -596.7  1090.8 -831.7 51.93 -2134.0  185.7 -154.6
  </code></pre>
  
  <p>What I want to do is compare the predicted results to the actual experimental results, so I can plot a graph of the two against each other to show how similar they are. I'm I right in assuming I would do this by using the values in the Predicted row as my predicted results and not the cvpred? </p>
  
  <p>I only ask this as when I performed the very same thing in the caret package, the predicted and the observed values came out to be far more different from one another:-</p>
  
  <blockquote>
    <p>library(caret)
    ctrl &lt;- trainControl(method = "cv", savePred=T, classProb=T)
    mod &lt;- train(RT..seconds.~., data=cadets, method = "lm", trControl = ctrl)
    mod$pred</p>
  </blockquote>
  
  <pre><code>        pred obs rowIndex .parameter Resample
  1      141.2  42        6       none   Fold01
  2     -504.0  42        7       none   Fold01
  3     1196.1  44       16       none   Fold01
  4       45.0  45       27       none   Fold01
  5      262.2  45       35       none   Fold01
  6      570.9  52       58       none   Fold01
  7     -166.3  53       61       none   Fold01
  8    -1579.1  59       77       none   Fold01
  9     2699.0  60       79       none   Fold01
  </code></pre>
  
  <p>The model shouldn't be this inaccurate as I originally started from 1664 variables, reduced it through the use of random forest so only variables that had a variable importance of greater than 1 was used, which massively reduced my dataset from 162 * 1664 to 162 * 151. </p>
  
  <p>If someone could explain this to me I'd be grateful, thanks</p>