About cd2011

cd2011 · ‎05-22-2014

Thanks for your input. But I dont think it will help since maxiter will increase the number of iterations if the current number of iterations are at the default cap. But the current iterations is only 4, so increasing maxiter did not help. I tried by setting it to 20.

cd2011 · ‎05-22-2014

VIF for all the variables included in the model are between 1 and 3, which I dont think is high. Also note, I dont get the error if I omit the repeated option.

cd2011 · ‎05-22-2014

Hi, I am having a problem with proc genmod I am hoping someone here can help me with. I am trying to model a variable which is very skewed. I am estimating GLM via GENMOD with repeated option to account for the repeated observations (note: I have an unbalanced panel). Now when I used dist=normal and link=log, I get the warning "The relative Hessian convergence criterion of xxxx is greater than the limit of 0.0001. The convergence is questionable." I get no such warning when I used dist=gamma and link=log. The mean prediction error is significantly smaller when I use the normal/log specifications, but I am not sure if I should be using that model with the warning message. The prediction mean error with gamma/log is closer to what I get if I estimate a log linear model in OLS with the same variables. Any idea why I am getting the error and any risks associated with using that model? The number of iterations is about 4, so increasing number of iterations will not help. Thanks in advance.

cd2011 · ‎03-04-2014

I am not exactly sure what you are trying to do here, but I got those error codes when I did not specify a goptions with sas graph command, for eg: goptions device=png;

cd2011 · ‎12-27-2013

Hi Steve, As per your suggestion, I have been experimenting with Proc FMM. I looked through the 130-page SAS document on FMM procedure and few other document, but I am still confused about few things. Most of the examples out there are on count data. As I have mentioned earlier, the response variable in my data is continuous but has several zeros. I think what I am trying to do is, mixing distribution logit (for zero and not zero part) and lognormal (for the positive part). This is what I am doing: (For the second model statement I tried both dist=constant and dist=binary. With binary I don't get any zero predictions which I would normally expect. Not sure if I am doing this part wrong or the prediction part wrong. ) proc fmm data= datafile ; model x =y1 y2 y3/noint dist=lognormal; model x= /dist=constant; probmodel y1 y2 y3 ; output out=fmm predicted residual; Thank you very much.

cd2011 · ‎12-19-2013

Thanks. I will look into that.

cd2011 · ‎12-18-2013

Hi, I am dealing with this problem where my dependent variable is continuous but consisted of several zeros (about 25%). The purpose of my study is out of sample prediction so I would expect several predicted values to be zeros as well. I understand that I cannot use count model since my dependent variable is continuous. OLS is a possibility ,but in this case OLS is giving low predictions but hardly any which can be considered zero. I tried GLM too with tweedie distribution nad link=log, this also gives no predictions close to zeros as I would expect. However, I ran a tobit model with lower bound censored at zero, and it gave me a mean value which is very close to the observed mean value. Tobit also generated zero predictions, but it predicted zeros for about 68% cases, which is very high. Next, I am going to estimate a hurdle regression but I would appreciate any suggestions for an alternative model that might be better suited. Thanks in advance. -CD

cd2011 · ‎07-23-2013

Hi Rick, Thanks for your reply. There are many useful information on the links you provided. I was able to get the qqplot as per your instruction. However, I was not able to draw the diagonal line because I am using SAS9.2 so lineparm will not work. Thanks much, -CD

cd2011 · ‎07-10-2013

I understand it might be hard to say whats going on without looking at the data. I can see the independent variable with the largest range is creating the problem. With inverse gaussian, the estimated coefficient for this variable is quite large causing over predictions.

cd2011 · ‎07-10-2013

Steve, Your code does work. However, I need to draw a reference line at 45 degree for comparison. It might be a silly question, but I could not find any resources for help with that. Thanks!

cd2011 · ‎07-09-2013

I am estimating a GLM where the response variable is very skewed. So, I used gamma distribution and log link options. Now, for a comparison purpose, I was trying Normal distribution and Inverse Gaussian distribution. The model give similar coefficients with Normal distribution, but with Inverse Gaussian, the estimations are different, and the predicted values almost blows up (veryyy large). Any idea why this is happening? Thanks for any help you could provide.

cd2011 · ‎07-08-2013

Hmmm! it is pretty strange. If I try the exact code with gamma option, it works, but iguass option does not work. SAS does not even recognize it as a valid option. Thanks for the help.

cd2011 · ‎07-08-2013

here is for ppplot: Base SAS(R) 9.3 Procedures Guide: Statistical Procedures got the igauss syntex from here: Base SAS(R) 9.3 Procedures Guide: Statistical Procedures Is there any way to get qqplot for inverse gaussian if proc univariate does not work. Thanks

cd2011 · ‎07-08-2013

Hi, I am trying qqplot/ppplot for inverse gaussian. SAS support website says to use : proc univariate data=name; qqplot var/ igauss(mu= lambda= ); run; But I am getting an error. Any idea why is it not working? I can make it work for gamma but not inv gaussian. I can't find any alternatives either. Thanks for any help.

cd2011 · ‎09-23-2011

As it turns out, I was infact mis-interpreting the results. The data is still heteroscedastic, GMM is only giving heteroscedastic-consistent variance covarince matrix.

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