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.