Given the information you shared, it *might* be appropriate to use the CLUSTER statement. Not knowing the survey design makes it difficult to know for sure. ... View more

What is the sampling design you are working with and what is your current SURVEYLOGISTIC code? ... View more

The use of the STRATA statement would depend upon what your survey design is.    The STRATA statement is meant to identify strata from a complex sampling design and not to run some sort of a conditional model/analysis that is usually associated with a case/control study. If you have a case control study based on a weighted random sample then you would want to include the indicator on the TABLES statement only and not the STRATA statement. ... View more

Yes, you could test for overdispersion by using the Pearson/DF. The second message is an indication that the model did not converge. A suggestion would be to simplify the model and add complexity back from there. ... View more

Sorry, it is a little hard to follow the thread. What is the exact error message you are receiving at this point? Does the version of R run outside of SAS without any issues? As I mentioned in the previous thread, this is likely related to an installation issue with R, but it is hard to say for sure I should also mention that there is a security vulnerability in all of the versions of R prior to 4.4.X so you might not want to run an earlier version anyway.  My suggestion would be to add the hotfix that I mentioned, remove all the old versions with the vulnerability and update to 4.4 and start from there.  This might resolve issue, but at the very least, it will eliminate several possible causes. ... View more

Try installing the hotfix here and see if that resolves the issue. 70253 - Using SAS/IML® Interface to R with version 4.3.0 of the R Language generates an execution error ... View more

If you can create a SAS data set from the output and post a portion of it here, then I can help you get it into the exact format that MIANALYZE can use.  It should be sufficient to simply post a Proc PRINT of the 1st 10 observations or so in the data set so that I can get the gist of the format. ... View more

Could you post the output you are unclear about? ... View more

There are two issues that need to be addressed in addition to the ERROR message.  Your MODELEFFECTS statement should list the parameters on it and there should not be a STDERR statement when you use the PARMS= data set.    Secondly, you should use a BY statement for the _IMPUTATION_ and only list RaceEthnicity on the DOMAIN statement.   Regarding the ERROR message, because of the way that SURVEYREG codes CLASS variables it is necessary to remove all blanks from the variable Parameter before feeding it into MIANALYZE.   You will then need to check the names of the effects in that data set (using a Proc PRINT) by placing them on the MODELEFFECTS statement .  You will also need to sort the output data set since you will need to run MIANALYZE by the domain variable.   If your goal is to also combine your ESTIMATE statement, then you will need another call to MIANALYZE (with another Proc SORT).   Below is an example that shows all of the steps for combining both the Parameter Estimates and Estimate statements when you use a DOMAIN statement in SURVEYREG. /*This assumes that the imputation has already been done*/ data farms; do _imputation_=1 to 3; do domainvar=1 to 3; do state=1 to 2; do region=1 to 3; do rep=1 to 5; farmarea=ceil(ranuni(323)*100); cornyield=.66+.95*state+.65*farmarea+rannor(3214); weight=ceil(ranuni(0)*10); output; end; end; end; end; end; run; proc surveyreg data=Farms; by _imputation_; domain domainvar; class region; strata State ; model CornYield = region FarmArea /solution; weight Weight; estimate 'Region 1 vs Region 2' region 1 -1; estimate 'Region 1 vs Region 3' region 1 0 -1; ods output parameterestimates=parms estimates=estimate_ds;; run; /*Because of the way that SURVEYREG codes CLASS variables it is necessary to remove all blanks from the variable Parameter*/ data parms; set parms; parameter=compress(parameter); run; /*This shows how the Parameters are now labeled for the MODELEFFECTS statement*/ proc print data=parms(obs=5); var parameter; run; proc sort data=parms; by domainvar _imputation_; run; proc mianalyze parms=parms; by domainvar; modeleffects intercept region1 region2 farmarea; run; proc sort data=estimate_ds; by label domain _imputation_; run; proc mianalyze data=estimate_ds; by label domain; modeleffects estimate; stderr stderr; run; ... View more

Try downloading the latest version of the MACRO at link below.  The WHERE= option was added after the release of the autocall version in SAS 9.4m8. https://support.sas.com/kb/62/addl/fusion_62362_20_nlmeans.sas.txt  ... View more

This is most likely an installation issue with R and/or SAS.  To confirm that is what is going on in your case, you could contact Technical Support and have them verify that it is not something else. ... View more

I believe that if you check the LOG you will see an explanation for the missing results in the form of a WARNING about the between imputation variance being zero. The message you are receiving can have a number of possible causes.  The first is that there is some problem with the imputation model itself (I have seen where people have left the response variable off the VAR statement in Proc MI as an example).  In general though it occurs when the missing data has no influence on the sampling error of a parameter estimate.  There is no fix or adjustment for that, but it does require some further investigation on your part.  Usually it is an indication of a problem with the imputation model with possible causes ranging from a poor imputation model to no real need to impute due to a very small fraction of missing information     Without fixing the problem, when the between imputation variance is zero then the number of Degrees of Freedom is undefined so you cannot get a confidence interval or p-values.  If you are unable to determine the cause then one suggestion would be that, if there are only minimum differences among these within-imputation variances, then look at the results for that variable for one of the imputations.   ... View more

You should remove the data step in between SURVEYLOGISTIC and MIANALYZE as it is causing the issue because you are using PARAM=GLM which has a missing standard error.  This step is unnecessary if you use the CLASS statement in MIANALYZE along with the CLASSVAR=CLASSVAL sub-option.       proc mianalyze parms(classvar=classval)=a_mvn; class SexualOrientationSex RaceEthnicity Education IncomePovertyRatio BMICategory HealthInsurance URBRRL REGION; MODELEFFECTS INTERCEPT AGEP_A SexualOrientation Sex RaceEthnicity Education IncomePovertyRatio BMICategory HealthInsurance URBRRL REGION; run;   ... View more

A point of clarification might help.  Typically, we refer to prior distributions of parameters and not of effects.  When an effect can be measured by a single parameter (like in a main effects only model) then these terms could be used somewhat interchangeably.  When you have an interaction, there is not a single parameter that captures the effect and so you cannot use the terms in that manner.   More to the point, the model you are fitting assumes that there is not a single treatment effect, but instead the effect depends upon the visit number.  Thus there is not a single effect. ... View more

It would be relatively straightforward to perform a test for the linear trend in the Log(odds) by using a CONTRAST (or ESTIMATE) statement and the approach detailed in this usage note for Proc GLM.  If you were to use PARAM=GLM on the CLASS statement then you should be able to use this methodology to generate the proper coefficients for the CONTRAST statement. http://support.sas.com/kb/22/912.html ... View more