The log is really long. This is the code I ran originally. ODS graphics on; proc glmselect data=dvddu plots=all; partition fraction(validate=.3); class pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 ; model dvd = pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 / selection=lasso(stop=none choose=validate); run; proc glmselect data=dvddu plots=all; partition fraction(validate=.3); class pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 ; model dvd = pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 / selection=lasso(adaptive stop=none choose=validate); run; ods graphics off; /* I got this warning in the log WARNING: The adaptive weights for the LASSO method are not uniquely determined because the full least squares model is singular. */ And this in the results [cid:image001.png@01D7AFC6.279FABC0] So I tried something not using k fold validation in case that was the issue as some suggest with smaller data bases (440 useful cases of which about 40 were at one level of the DV). ODS graphics on; proc glmselect data=dvddu plots=all; /*partition fraction(validate=.3);*/ class pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 ; model dvd = pd1 pd2 pd3 pd4 pd5 pd6 pd7 pd8 pd9 pd10 pd11 pd12 pd13 pd14 pd15 pd16 pd17 pd18 pd19 pd20 pd21 pd22 pd23 pd24 pd25 pd26 pd27 pd28 pd29 pd30 pd31 / selection=lasso(stop=none choose=sbc); run; ods graphics off; I got no warnings in the log. I got the same message in the results. I looked at documentation on this message but can't figure out if it means the lasso selection is invalid due to data limitations or not. Thanks for your help. I would really like to use this method and am spending a long time trying to learn it and the code. But I can't find anything to explain this message or to suggest what the issue is. I ran logistic regression on the same data base without issue so it does not seem like a data issue. ... View more

Following a suggestion made by several I ran the lasso on the entire data set rather than a training data set (I had held half the data out to use in the logistic regression). Even with that I got the same errors. I find this very confusing because I thought LASSO was intended to deal with limited data relative to predictors. And it won't run correctly even on a data set that the logistic regression will run 🙂 I think the issue may be my choice of cross validation to choose alpha although I know of no other way to choose it. ... View more

The adaptive lasso ran and had there been true overlap it could not have so I am confused what these warnings mean, if they mean anything. ... View more

I think the statement is just a reminder of what is occurring that always is given when using cross validation although I am not certain. People who mentioned it did not seem concerns. I ran the adaptive lasso command from the paper you mentioned and got a warning that I also do not understand, but may be related to the first one. "The adaptive weights for the LASSO method are not uniquely determined because the full least squares model is singular." *sigh*. I am checking the variables again, but I use them in a logistic regression without problem so they should not overlap. ... View more

No the dummies don't overlap (or it would not run in the logistic regression I already ran which it did). Reading various comments associated with this "error" it appears that it will always be generated when you chose cross validation in the code. I found this out after I posted this. I don't really understand what the message is telling you, it explains why it is stopping at a specific step for sure.   If anyone knows the code for adaptive lasso please let me know. ... View more

Thanks for the advice of reconstituting. That is what I guessed. I am going to do this with a case statement in Proc SQL rather than a data statement because I work with sql a lot and rarely data statements. ... View more

The variables were created by the federal agency we report to. They have split age into a series of categories all of which are dummy variables and I have to run analysis using their variable schema. That said I don't understand what this entails. "If you had an age variable that was in categories it's usually normal to have an AGE variable with the different categories in a single variable and then use the CLASS statement." The organization in question says they are doing lots of things that puzzle me such as doing fixed effect regression (as per the Allison book) with no panel data. 🙂 ... View more

Thank you for your response. I would infinitely prefer to run a logistic regression model in this case. I was taught to do that because of the well known errors when you have a binary DV -which you note. However, the federal government has decided for our agency that LPM models have to be run so there is no option to use the logit link (that is run logistic regression).  Although I do not know the details many economists believe that LPM models are fine (meaning linear models with a binary dependent variable) and I think this process was determined by economists. I have the whole population so the SE really don't matter to me at all only the accuracy of the slopes, I ignore the statistical slope test since I have the population. Although the error I mentioned caused the SE not to be reported it does generate slopes. Some have suggested the slopes will still be true despite the error, but I have to get more details.    As a really basic point, is specifying /LINK=identity DIST=BINOMIAL  the correct way to generate the Linear Probability model in Proc Genmod? This is actually the first time I have not used Proc Logistics for a binary DV so I am getting the basics right. And how in he code do you request the odds ratios and predicted probabilities -I was hoping to plot them so as to argue that LPM might be unreasonable with the feds. 🙂 ; ... View more

Thank you. When I ran it without specifying a distribution it apparently does not do either. I think it treats the DV as an interval level variable. When I specified dist=binomial then it does tell you this. But that generated new issues. First, do I need to declare the dependent variable (DV) in the class statement? And much more important I get an error, and lack of convergence although results are generated.   ERROR: The mean parameter is either invalid or at a limit of its range for some observations.   This error I suspect means that there are probabilities beyond 0 and 1 which are impossible (and why I don't like LPM models). But I don't know how I can fix this problem, if I can, and if I can interpret the slopes that result as legitimate given this error and the lack of convergence.  ... View more