https://communities.sas.com/t5/Statistical-Procedures/Help-external-cross-validation-with-proc-glmselect/m-p/228751#M12075 <P>Hi everyone. I am trying to predict an outcome with several predictor variables (4-5), and some of them show a certain level of collinearity (up to an r correlation coefficient of 0.7) so I must apply a restricted regression technique. I am using elastic net because I understang it should have an advantage over lasso or ridge in this situation.</P><P>Well, with proc glmselect I am able to run an elastic net regression with external k fold cross validation as the model selection method</P><P><A href="http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_glmselect_details30.htm" target="_blank">http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_glmselect_details30.htm</A></P><P>&nbsp;</P><P>Now, I would like to graph predicted vs observed values (and then calculate slope and intercept for the predicted vs observed regression) for all the data used in the k fold steps, but I cannot obtain the parameters estimates for each k fold run.</P><P>The cvdetails option in the model statement indeed works for obtaining parameters estimates when running internal cross validation, but it does not for external cross validation. Any clue?</P><P>&nbsp;</P><P>Thanks in advance,</P><P>Pedro</P> Tue, 06 Oct 2015 22:59:00 GMT pedroe 2015-10-06T22:59:00Z https://communities.sas.com/t5/Statistical-Procedures/Help-external-cross-validation-with-proc-glmselect/m-p/228751#M12075 <P>Hi everyone. I am trying to predict an outcome with several predictor variables (4-5), and some of them show a certain level of collinearity (up to an r correlation coefficient of 0.7) so I must apply a restricted regression technique. I am using elastic net because I understang it should have an advantage over lasso or ridge in this situation.</P><P>Well, with proc glmselect I am able to run an elastic net regression with external k fold cross validation as the model selection method</P><P><A href="http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_glmselect_details30.htm" target="_blank">http://support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_glmselect_details30.htm</A></P><P>&nbsp;</P><P>Now, I would like to graph predicted vs observed values (and then calculate slope and intercept for the predicted vs observed regression) for all the data used in the k fold steps, but I cannot obtain the parameters estimates for each k fold run.</P><P>The cvdetails option in the model statement indeed works for obtaining parameters estimates when running internal cross validation, but it does not for external cross validation. Any clue?</P><P>&nbsp;</P><P>Thanks in advance,</P><P>Pedro</P> Tue, 06 Oct 2015 22:59:00 GMT https://communities.sas.com/t5/Statistical-Procedures/Help-external-cross-validation-with-proc-glmselect/m-p/228751#M12075 pedroe 2015-10-06T22:59:00Z https://communities.sas.com/t5/Statistical-Procedures/Help-external-cross-validation-with-proc-glmselect/m-p/461179#M24078 <P>Hi.</P><P>&nbsp;</P><P>I've right now exact the same question as Pedro in 2015. At that time no answer was posted.&nbsp; Does anyone have an idea by now? I'm interested in the predictors selected by 5 fold corss validation in each of the 5 steps by proc glmselect...cvdeatils doesn't work with selection=elasticnet(choose=cvex) .</P><BLOCKQUOTE><HR /><HR /></BLOCKQUOTE><P>Hoping for any advice.</P> Wed, 09 May 2018 20:19:30 GMT https://communities.sas.com/t5/Statistical-Procedures/Help-external-cross-validation-with-proc-glmselect/m-p/461179#M24078 elge 2018-05-09T20:19:30Z