About SAS_Rob

SAS_Rob · ‎05-26-2021

Unfortunately there is no direct way to combine non-parametric statistics that depend on the normal distribution for their p-values like the JT test using Proc MIANALYZE. The theory behind combined estimates requires both a point estimate and a standard error.

SAS_Rob · ‎05-25-2021

You would have to manually drop the estimates from the models that didn't converge. Once you do that you should be able to use Proc MIANALYZE to combine the estimates from the rest of the models that did. The only caveat is that MIANALYZE requires that there be consecutive numbering of the values in the _IMPUTATION_ variable so you will also need to re-number them once you omit the models that did not converge.

SAS_Rob · ‎05-19-2021

To include interactions terms in the model, you would need to explicitly define the model. It only includes the main effects by default. Below is a modified version of an example from the documentation that includes the interaction of a categorical and continuous variable in the FCS regression model. data Fish3; title 'Fish Measurement Data'; input Species $ Length Width @@; datalines; Roach 16.2 2.2680 Roach 20.3 2.8217 Roach 21.2 . Roach . 3.1746 Roach 22.2 3.5742 Roach 22.8 3.3516 Roach 23.1 3.3957 . 23.7 . Roach 24.7 3.7544 Roach 24.3 3.5478 Roach 25.3 . Roach 25.0 3.3250 Roach 25.0 3.8000 Roach 27.2 3.8352 Roach 26.7 3.6312 Roach 26.8 4.1272 Roach 27.9 3.9060 Roach 29.2 4.4968 Roach 30.6 4.7736 Roach 35.0 5.3550 Parkki 16.5 2.3265 Parkki 17.4 . Parkki 19.8 2.6730 Parkki 21.3 2.9181 Parkki 22.4 3.2928 Parkki 23.2 3.2944 Parkki 23.2 3.4104 Parkki 24.1 3.1571 . . 3.6636 Parkki 28.0 4.1440 Parkki 29.0 4.2340 Perch 8.8 1.4080 . 14.7 1.9992 Perch 16.0 2.4320 Perch 17.2 2.6316 Perch 18.5 2.9415 Perch 19.2 3.3216 . 19.4 3.1234 Perch 20.2 . Perch 20.8 3.0368 Perch 21.0 2.7720 Perch 22.5 3.5550 Perch 22.5 3.3075 Perch 22.5 3.6675 Perch . 3.5340 Perch 23.5 3.4075 Perch 23.5 3.5250 Perch 23.5 3.5250 . 23.5 3.5250 Perch 23.5 3.9950 Perch 24.0 3.6240 Perch 24.0 3.6240 Perch 24.2 3.6300 Perch 24.5 3.6260 Perch 25.0 3.7250 Perch . 3.7230 Perch 25.5 3.8250 Perch . 4.1658 Perch 26.5 3.6835 . 27.0 4.2390 Perch . 4.1440 Perch 28.7 5.1373 . 28.9 4.3350 Perch 28.9 4.3350 Perch 28.9 4.5662 Perch 29.4 4.2042 Perch 30.1 4.6354 Perch 31.6 4.7716 Perch 34.0 6.0180 Perch 36.5 6.3875 Perch 37.3 7.7957 Perch 39.0 . Perch 38.3 6.7408 Perch . 6.2646 . 39.3 . Perch 41.4 7.4934 Perch 41.4 6.0030 Perch 41.3 7.3514 Perch 42.3 7.1064 Perch 42.5 7.2250 Perch 42.4 7.4624 Perch 42.5 6.6300 Perch 44.6 6.8684 Perch 45.2 7.2772 Perch 45.5 7.4165 Perch 46.0 8.1420 . 46.6 7.5958 ; proc mi data=Fish3 seed=1305417 nimpute=15 out=outex7; class Species; fcs nbiter=10 discrim(Species/details) reg(Width=length species length*species/details); var Species Length Width; run;

SAS_Rob · ‎05-18-2021

Notice that in the example I posted that I do not have the CLASS statement in the MIANALYZE step. Instead, because the variable and the level are now concatenated with the COMPRESS function, the variables names on the MODELEFFECTS statement are also different. It might help to simply Proc PRINT the PARMS= data set after you use the COMPRESS function and then the variables names contained in the variable PARAMETER will be the same ones you use on the MODELEFFECTS statement.

SAS_Rob · ‎05-17-2021

It would be helpful to see the steps in between the MI and MIANALYZE as well, but it might well be that the problem is because of the way that SURVEYREG codes CLASS variables, it is necessary to remove all blanks from the variable Parameter before using MIANALYZE. Below is a simple example. /*This assumes that the imputation has already been done*/ data farms; do _imputation_=1 to 2; 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; proc surveyreg data=Farms; by _imputation_; class region; strata State ; model CornYield = region FarmArea /solution; weight Weight; ods output parameterestimates=parms; run; data parms; set parms; parameter=compress(parameter); run; proc mianalyze parms=parms; modeleffects intercept region1 region2 farmarea; run; The WARNING message from MI regarding the non-existence of the ML estimates for the LOGISTIC method also may cause a problem that merits your attention. You can try following the suggestion regarding the LIKELIHOOD=AUGMENTED option or simplify the logistic model itself or use the DISCRIM method for all the CLASS variables.

SAS_Rob · ‎05-11-2021

Try running it without the CLASS statement like I did and see if that helps.

SAS_Rob · ‎05-11-2021

The key is not to use spaces in the labeling of the ESTIMATE statements since those ultimately will be the variables you place on the MODELEFFECTS statement. Below is a simple example you can follow and apply in your case. data test; do a=1 to 3; do rep=1 to 10; x=ranuni(123)*10; if ranuni(234)>.55 then y=.; else y=.46+.88*a+x+rannor(123); output; end; end; proc mi data=test out=outmi; var a x y; run; ods trace on; proc mixed data=outmi; by _imputation_; class a; model y=a x; estimate 'A1_v_A2' a 1 -1 0;*do not use spaces in the labels; estimate 'A1_v_A3' a 1 0 -1; ods output estimates=estimate_ds(rename=(label=effect)); run; proc mianalyze parms=estimate_ds; modeleffects A1_v_A2 A1_v_A3;*these are the labels you used; run;

SAS_Rob · ‎05-03-2021

I suspect that you have accidentally changed the data set before you use it again as it should have no bearing on whether you use it right away or not. I would run a Proc CONTENTS right before the distant MI call to find out what is going on with the data set type. You could set it to TYPE=EST as someone else suggested previously.

SAS_Rob · ‎04-21-2021

Just to clarify, typically the Generalized Logit link (LINK=GLOGIT) is used for a nominal response variable and the cumulative link function with the proportional odds assumption is used for an ordinal response. That being said, I have included an example using both link functions below. /*Sample data set that assumes the imputation has already been done*/ data test; seed=2534565; do _imputation_=1 to 25; do a=1 to 3; do i=1 to 250; ind1=ranuni(seed); logit=-1 + .05*ind1-.67*a; p=exp(-logit)/(1+exp(-logit)); if ranuni(seed)>p then y=1; else if ranuni(314)>.5 then y=2; else y=3; output; end; end; end; run; /*Generalized Logit Model*/ proc logistic data=test; by _imputation_; class a/param=ref; model y=ind1 a/link=glogit; ods output parameterestimates=parmsds; run; proc mianalyze parms(classvar=classval link=glogit)=parmsds; class a; modeleffects Intercept ind1 a; run; /*Default Proportional Odds Model*/ proc logistic data=test; by _imputation_; class a/param=ref; model y=ind1 a; ods output parameterestimates=parmsds; run; proc mianalyze parms(classvar=classval link=logit)=parmsds; class a; modeleffects Intercept ind1 a; run;

SAS_Rob · ‎04-21-2021

Hi You can essentially ignore the first step in the sample program as I used it to generate a data set similar to what you would have gotten from Proc MI on your own data. You would just pick up at the Proc SURVEYMEANS step that you would run on your own data.

SAS_Rob · ‎04-15-2021

Check this example which shows how you can use the DESCENDING option on the CLASS statement. https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_causalmed_examples03.htm Likewise you could also use the REF= for the individual variables. https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_causalmed_syntax04.htm

SAS_Rob · ‎04-14-2021

You will need to use a Procedure that gives a standard error for the median so that you will be able to calculate the between and within imputation variance. I think both Proc LIFETEST and SURVEYMEANS are the only procedures that do this. I include an example below which uses the latter. /*Sample Data Set that assumes Proc MI has already been run*/ data test; do _imputation_=1 to 5; do rep=1 to 100; y=100+10*rannor(34211); output; end; end; run; proc surveymeans data=test median; by _imputation_; var y; ods output quantiles=stats; run; proc mianalyze data=stats; modeleffects estimate; stderr stderr; run;

SAS_Rob · ‎04-13-2021

It sounds like all you really want is the Type3 test for the interaction which should be part of the default output when you run: proc surveylogistic; class intervention month/param=glm; model reponse=intervention|month; run;

SAS_Rob · ‎04-09-2021

The imputation method will depend on the missing data pattern and the variable type. This table from the documentation will be helpful. https://go.documentation.sas.com/?cdcId=pgmsascdc&cdcVersion=9.4_3.4&docsetId=statug&docsetTarget=statug_mi_details05.htm&locale=en There is not a way to get a combined ROC curve directly using multiple imputation. You can however get a combined estimate of the C-statistic as the area under the ROC curve. Below is an example that shows how you might code this. data roc; input alb tp totscore popind @@; totscore = 10 - totscore; datalines; 3.0 5.8 10 0 3.2 6.3 5 1 3.9 6.8 3 1 2.8 4.8 6 0 3.2 5.8 3 . 0.9 4.0 5 0 2.5 5.7 8 0 1.6 5.6 5 1 3.8 5.7 5 1 3.7 6.7 6 1 3.2 5.4 4 1 3.8 6.6 6 1 4.1 6.6 5 1 3.6 5.7 5 1 4.3 7.0 4 1 3.6 6.7 4 . 2.3 4.4 6 1 4.2 7.6 4 0 4.0 6.6 6 0 3.5 5.8 6 1 3.8 6.8 7 1 3.0 4.7 8 0 4.5 7.4 5 1 3.7 7.4 5 1 3.1 6.6 6 1 4.1 8.2 6 1 4.3 7.0 5 1 4.3 6.5 4 1 3.2 5.1 5 1 2.6 4.7 6 1 3.3 6.8 6 0 1.7 4.0 7 0 3.7 6.1 5 1 3.3 6.3 7 1 4.2 7.7 6 1 3.5 6.2 5 1 2.9 5.7 9 0 2.1 4.8 7 1 2.8 6.2 8 0 4.0 7.0 7 1 3.3 5.7 6 1 3.7 6.9 5 . 3.6 6.6 5 1 ; proc mi data=roc out=mi_roc; class popind; var alb tp totscore popind; monotone logistic; run; proc logistic data=mi_roc; by _imputation_; ods output ROCAssociation=roc_ds(where=(ROCMODEL='Model')); model popind(event='0') = alb tp totscore; roc; run; proc mianalyze data=roc_ds; modeleffects area; stderr stderr; run;

SAS_Rob · ‎04-07-2021

Unfortunately MI only reports EM estimates for the mean and covariance matrices.

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