About evardoodle

evardoodle · ‎09-07-2017

A very helpful person helped me solve this problem. When you only input prior variances, SAS assumes all covariances are 0, it does not compute the actual covairances. I confirmed this by setting covariances to 0 in the covariance matrix and obtained the same results as inputing variances only.

evardoodle · ‎09-05-2017

I don't believe that your intitial values should be equal to your prior values, it should be linked to the seed that you set. If you want to set initial values then you can specify INITIALMLE to use the maximimum likelihood estimates, or you can input a datset with initial values using INITIAL=. I noticed when I tried INITIALMLE, I would get the exact same posterior estimates, even when I changed my priors. I'm not sure why this happened. It could be my data. I also got very different posterior estimates when I used the variances vs. covariances in a prior dataset.

evardoodle · ‎09-05-2017

Sorry I can't help more regarding correct SE methods. As I'm sure you know, most resources I found in internet searches are directed specifically toward complex sampling schemes, not unequal weighting problems exclusively. How different are the errors between MEANS and SURVEYMEANS? If you don't have complex sampling design, they should be pretty similar. Also check your weights for extreme values as this can be problematic. An alternative is to calculate stabilized weights, which usually reduces standard errors. Good luck.

evardoodle · ‎09-02-2017

You used the noprint option. It looks like the SAS log is tellingyou not to use the noprint option.

evardoodle · ‎09-02-2017

The weight statement should be sufficient for both situations. SURVEYMEANS was developed for complex survey designs. See here: SURVEYMEANS Overview Your study doesn't have stratified/clustered sampling. Furthermore, subgroup analysis is much more straightforward to use in the MEANS procedure.

evardoodle · ‎09-02-2017

Hello SAS Experts, When using PROC GENMOD for a Bayes analysis, I noticed there is a substantial difference in my posterior estimates when entering in the exact same prior regression information using the coefficient means and full covariance matrix vs. the coefficient means and just the coeffient variances. I set a seed for reproducilibity and both models appear to achieve convergence. Please review my code, input priors, and results below. Here is my code: proc genmod data=data; model DGSYTOT1 = totpcbla RIDAGEYR totpcbla_RIDAGEYR RIAGENDR smoker HSplus / dist=normal link=identity; bayes coeffprior=normal(input=prior) seed=12345 plots=all stats(percent=2.5 50 97.5) diagnostics=all outpost=post; run; Here are the prior datasets with theoretically equivalent information: Here are the disparate results: I would report very different interpretations of these results and I am not sure which results to trust. Can anyone explain why the input dataset structure matters so much? Is this an estimation error? Am I inputting something incorrectly? Any help would be greatly appreciated. Thanks in advance! Eva

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Re: PROC GENMOD BAYES - Variance vs. Covariance for prior input datase...

Re: Informative Priors in Proc Genmod not working

Re: PROC SURVEYMEANS versus WEIGHT statement

Re: Output not created warning with Imputation with Proc MI

Re: PROC SURVEYMEANS versus WEIGHT statement

PROC GENMOD BAYES - Variance vs. Covariance for prior input dataset pr...

Re: PROC SURVEYMEANS versus WEIGHT statement

Re: PROC GENMOD BAYES - Variance vs. Covariance for prior input datase...

Re: Informative Priors in Proc Genmod not working

Re: PROC SURVEYMEANS versus WEIGHT statement

Re: Output not created warning with Imputation with Proc MI

Re: PROC SURVEYMEANS versus WEIGHT statement

PROC GENMOD BAYES - Variance vs. Covariance for prior input dataset pr...