You're welcome! Glad we could narrow it down! ... View more

OK, I apologize if I'm not understanding or explaining my meaning. It sounds like what you're asking is how to tell if the relationship of your predictor variable (YrsfromAV_AAO) to your outcome variable (ventricualr volume difference score) changes depending on which genetic status category the subject falls under. That's what the coefficient on the interaction term will tell you. Depending on the GS category, the coefficient on the YrsfromAV_AAO variable (which represents the slope of the linear relationship with your outcome variable) would go up or down equivalent to the coefficient on the interaction term for that GS group.   I haven't used PROC PLM, so I can't help you there, sorry. Just quickly reading through the documentation, it seems to be a procedure to do post-processing of the output from procedures like PROC MIXED in order to get average marginal effects. But I don't know much about it. ... View more

OK, maybe I misinterpreted your question. Are you trying to understand why the coefficient on the interaction term is statistically significant for one group and not for another? It's hard for me to say, as it would depend on your model and what the time periods mean in the context of your analysis. Perhaps there was some change from one year to the next that would explain why that interaction is significant. ... View more

If I understand your question correctly, you're asking how to interpret the coefficient on YrsfromAV_AAO_Time1 given the interaction with your time class variable. You can think of the interaction term as a series of dummy variables (0 or 1) that change the marginal effect of YrsfromAV_AAO_Time1 depending on which time period you're in. So you have a model that looks like the following:   y_it = B_1*x_1t + B_2t*x_2t*x_1t where x_1t is the YrsfromAV_AAO_Time1 variable and x_2t is the time period dummy variable.   When you take the marginal of this, you get: dy/dx_1t = B_1 + B_2t*x_2t   So your marginal effect is going to be dependent on which time period you're in. The coefficient then becomes the beta on YrsfromAV_AAO_Time1 + the beta on whichever time variable is positive (1) in that observation. To get an overall marginal effect of YrsfromAV_AAO_Time1, you would need to calculate the average of the marginal effects for each observation. ... View more

I agree. I think sorting would be more efficient. I'm not an expert on the performance of the procedures, but my understanding is that data step merges rely more on I-O of your hard drive and SQL and Hashes are more dependent on memory, as they are done by pulling the data into RAM. One way to significantly reduce hard drive I-O is by using data views. You can create a data view for each of your datasets and then sort the view without outputting any data until the final merge step. Here's some code from this SAS paper: data ALL / view=ALL; set A B C...; run; proc sort data=ALL; by <keys>; run;   ... View more

Have you tried using PROC PANEL? You should be able to specify one-way and two-way fixed and random effects using the options in the model statement:   proc panel data=data; id stock_name date; model stock_price = volume liquidity volatility / fixone (or fixtwo); run;     ... View more

If they're all based on the same key, you can use a data step merge with an in statement (sort the data first):   data merged; merge data1(in=in1) data2 data3 ... etc.; by key; if in1 then output; run; If you have multiple keys that are different from one another, you can use proc sql with something like this: proc sql: create table merged as select t1.*, t2.* ... etc. from data1 as t1 left join data2 as t2 on (key1=key1 & key2=key2); quit; ... View more

I managed to get GAREABAR to give me all of the bars by changing the numeric variable to a character variable, but the chart still looks pretty bad. Because there are so many bars, they visually seem to overlap each other, and so there's no apparent color differentiation between the different technologies. I think I will probably try to figure something else out, most likely with a scatter chart. I'll leave this as unanswered in case anyone else has any ideas for how to make the chart work. ... View more

Thanks for the response. My goal is not to have readers be able to differentiate between the different bars, but to look at the overall curve produced by the bars. I want the color of the bars to indicate ranges of technologies used to generate electricity. I will check into the format to be sure I haven't inadvertently grouped the data in some way. My guess is that SAS automatically groups them when you have too many bars to display, but the problem is I want that many bars displayed. ... View more