Hello you all, I have been trying to impute missing data using the delta adjusted pattern imputation to compare it to the control-based pattern imputation. Unfortunately I am getting the above error message which I am unable to solve. Could somebody advice? My syntax is as follows: proc mi data=milk.missingwide out=milk.FNDSdimp seed=128 nimpute=1000; class sex single precovid randomization; FCS discrim(sex/details) discrim(single/details) reg(FNDSbase/details) reg(FNDS1/details) reg(FNDS2/details) reg(FNDS3/details) reg(FNDS4/details); mnar model(FNDSbase / modelobs= (randomization='0')) adjust (FNDS1/ shift=1 adjustobs=(randomization='1')) adjust (FNDS2/ shift=1 adjustobs=(randomization='1')) adjust (FNDS3/ shift=1 adjustobs= (randomization='1')) adjust (FNDS4/ shift=1 adjustobs= (randomization='1')); var sex single precovid FNDSbase FNDS1 FNDS2 FNDS3 FNDS4; run; *reverting back to long dataset type; data milk.FNDSdimplong; set milk.FNDSdimp; week=1; FNDS=FNDS1; output; week=2; FNDS=FNDS2; output; week=3; FNDS=FNDS3;output; week=4; FNDS=FNDS4; output; run; proc mixed data=milk.FNDSdimplong; class subj randomization sex single week precovid; model FNDS = randomization|week FNDSbase sex single precovid /ddfm=kr2 solution covb; repeated week / subject=subj(randomization) type=AR(1) ; rANDOM subj(randomization); by _imputation_; lsmeans randomization*week /pdiff cl; ods output SolutionF=milk.mxFNDSdparms covb=milk.mxFNDSdcovb; run; proc mianalyze parms(classvar=full)=milk.mxFNDSdparms covb(effectvar=rowcol)=milk.mxFNDSdcovb; class randomization week sex single precovid; modeleffects randomization week randomization*week FNDSbase sex single precovid; run;
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