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How is the R-squared calculated for an elastic net? How about LASSO? Should be different from OLS, or not?

Edit: The main problem is as follows:

We have all kinds of fruits like $f_1, f_2, ..., fn$ for which we have $5$ different properties $c_1,...,c_5$.

We know well 10 of the fruits which are $f_1,...,f_{10}$. For each of these known fruits, we want to find which of the remaining fruits in the set $f_i, 10<i<=n$ can better explain its features.

$f_j = \beta_if_i + \epsilon; 1<=j<=10; 10<i<=n$

To this end, I used elastic net due to the property of my data.

I used cross validation to fit my models and at the end I have 10 models each for one fruit $f_j; 1<=j<=10$.

For me, this is very important to see which of these models are very well fitted to the data. Then I can rank the models from $1$ to $10$ and use this ranking in further analysis.

In my question, fruit $f_k; 1<=j<=10$ might not be well explained by any other fruits! that's why I'm looking for a statistics that I can compare the goodness of fit for each of the obtained model.

I want now to know, whether or not the PRESS or R-squared is a good measure?

Thank you very much. N.

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Generally speaking, if you are using a regularized method you typically have more variables than you have samples ($n\ll p$). As such, using R-squared is not recommended. This is because a model with all the variables will always have the largest R-squared. It is recommended to do some form of cross-validation.

If your dependent variable is continuous, you likely would use the Predicted Residual Sum of Squares (PRESS) statistic:

$$PRESS=\sum_{i=1}^{N} (y_{i}-\hat{y}_{i,-i})^2$$

If you are really attached the idea of an 'R-squared' you can convert the PRESS into R2 by dividing it by the sum of squares (SS) and subtracting everything from 1:

$$SS=\sum_{i=1}^{N} (y_{i}-\bar{y})^2$$ $$R2=1-PRESS/SS$$

If your dependent variable is a classifier (e.g. Control vs. Disease), then you would use a different metric like Accuracy, Area Under the Receiver Operator Curve (AUROC), or Kappa.

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  • $\begingroup$ Thanks a lot for your clear response. I have a stupid question, what do you mean by \^{y}_{i,−i}. I don't get (i,-i), would you mind please shortly comment on it. $\endgroup$ – sbmm Nov 12 '14 at 8:32
  • $\begingroup$ @sbmm, it is simply the notation for the formal definition of PRESS. It signifies that $\hat{y}_{i}$ (i.e. the current sample) is predicted from from the remaining samples minus the current sample, hence the ${-i}$ $\endgroup$ – cdeterman Nov 12 '14 at 13:07
  • $\begingroup$ Imagine, I have an apple in one side and other fruits (except an apple) in the other side. I used elastic net to see which fruits, can better explain the features of the apple. I have 5 features for my fruits. Imagine my y(apple)=(1,1,2,1,3) and matrix X with size 20*5 which means 20 fruits with 5 features. I found a model per fruit via elastic net, and I found out that for e.g., the apple can be explained by pear and quince. Next, I take another fruit and continue the same. I want to see which of the fruits are better modeled by other. Would PRESS be a good statistics to compare my models? $\endgroup$ – sbmm Nov 12 '14 at 14:37
  • $\begingroup$ If I want to calculate the goodness of the fit for each fruit, which statistics should I apply? I I know it, then I can compare my models obtained for each fruit. $\endgroup$ – sbmm Nov 12 '14 at 14:41
  • $\begingroup$ Have you done some analysis? Do you have a dataset? I would likely be better able to help you if I can 'see' the dataset and how you have done your analysis. From what I understand, you are wanting to compare between classes (i.e. fruits) instead of classifying them from their respective features (which is what elastic-net and LASSO are for). In which case, you should be using a different method. You may wish to start another question to more directly address this. $\endgroup$ – cdeterman Nov 12 '14 at 15:20

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