Switching of signs of the regression coefficient is a known drawback to ordinary least squares regression when you have correlated predictor variables. What should you do about this? There are several choices ... remove the correlated predictor variables; or use the final model for prediction only, not for interpretation; or change your estimation to Partial Least Squares (PROC PLS in SAS) which is less susceptible to the switching signs problem and produces regression coefficients that have smaller mean squared error than ordinary least squares does with correlated predictor variables. ... View more

Do you need variables you understand? What about principal component or factor analysis? Neither Principal Components nor Factor Analysis would be appropriate in a regression situation (even though you can certainly read about using those techniques for regression in the literature). It is more logical to use (here we go again) PLS, which creates vectors of independent variables that are designed to be highly predictive of the dependent variable. PCA and FA create vectors with no regard to the relationship to the dependent variable. ... View more

... it would not make sense to keep them all since we would run into major multicollinearity. This is NOT true if you use PROC PLS. PLS was specifically designed to work in the presence of multicollinearity; and specifically designed to keep all variables in the model (although many will be of no practical importance). Least squares regression has known problems in this case. My final model should have between 4 and 10 independent variables. My problem here is that specifying in advance how many independent variables might lead to trouble, if for example, there's only 1 significant predictor, or if there are 16 significant predictors. What would be helpful is a function that would try all possible set of 4 independent variables (I have a total of 180 independent variable) and tell me which set is the most explanatory with the least amount of multicollinearity, heteroskedasticity and serial correlation. In addition to my previously stated misgivings, I don't think there is a way to do this in SAS, other than by writing your own MACRO or PROC IML code. You could use one of the STEPWISE methods in PROC REG, and force the options START=4 and STOP=4 (I have never actually tried this, so I can't guarantee it will do what I think it will do), which will give you the best fitting 4 parameter model it can find in a sequential fashion (but does not iterate through all possible 4 parameter models), but as I said, the drawback is that ordinary least squares has known problems in the case of multicollinearity (and it doesn't try to minimize the multicollinearity or adjust for it in any way). It also assumes 4 is the right number, which it may not be. Which again leads me back to PROC PLS, which has none of these drawbacks ... View more

Macro variables created by call symput will not be available until after the RUN; statement at the end of the data step. You don't need macros here anyway. data employee1 employee2 employee3; set department; if employeeID=1 then output employee1; else if employeeID=2 then output employee2; else if employeeID=3 then output employee3; run; ... View more

To tell you the truth, I don't have a problem with getting a power of > 0.999 in this case. I do have a problem with your statement "no matter what ntotal value i enter". When I enter an ntotal value of 5 or less, the power decreases. The power of a test is: the probability that the test will reject the null hypothesis when the null hypothesis is false. So, with only 2 observation, the probability of rejecting H0: mean=0 when in fact H0 is false is 0.219. That seems like the right answer to me. With only 2 observations, and mean ne 0, you could easily wind up accepting the null hypothesis. When ntotal increases to 10, it seems very likely that you will reject the null hypothesis when it is false. ... View more

Show us your SASLOG for what you tried. You might also want to show us a portion of your data. ... View more

To be honest, I would try a different approach. I've gone through this, my view has evolved from picking a small number of variables, to saying that doesn't make sense and using a different approach in which all variables are used in the model. The problem is that there are many different definitions of "best possible independent variables". Also, forcing yourself to only 4 may or may not be harmful, and furthermore if your independent variables are correlated with one another, then least squares fitting and picking a few variables may not be the best approach at all. I would use PROC PLS and fit a model that includes all independent variables,and which accounts for correlation between the independent variables; and from there you could judge a small number of important variables (although that number may not be 4). ... View more

Yes, that is a very good introductory example, for a simple ordinal logistic regression. ... View more

Based upon my understanding of your description, and my understanding of ordinal logistic regression, I would the answer is YES! PROC LOGISTIC still applies here. ... View more

There are probably several techniques that might work here, depending on what assumptions you want to make about errors and objective function, etc. You might want to try ordinal logistic regression, since your dependent variables are really 6 different categories. See PROC LOGISTIC I might also go with PROC PLS, as your independent variables are most likely correlated with one another. There's also a Logistic version of PLS in the literature, although I think you'd have to code that yourself, as far as I know it doesn't exist in SAS. ... View more

I too would be interested in hearing if there is software designed to do this. At the time, I wound up writing SAS code to do search and replace inside of *.sas files in a directory(s), and I must say it usually worked, but there were a few problems that I never anticipated and never figured out why they were happening. ... View more

Yes, lines get truncated if they are too long for the program editor. That sounds to me like a reason to force everyone to use the Enhanced Editor. But I agree with Fareeza, in my opinion, productivity gains come with good coding, not with Enterprise Guide ... View more

However, if the macro variables are cast in concrete including all the parentheses, you could always try: Or use text manipulation functions to remove the parentheses (and any other unwanted characters) ... View more