data='bang.txt';

model:'A. logistic regression'  regression binomial   1 /
  toler=1e-008  tolem=0.01  tolran=1e-005  bayes=1  bayess2=1  bayeslat=1
  bayespoi=1
  iterem=250  iternr=50  itersv=50
  iterboot=500
  nseed=0  nseedboot=0
  nrand=10
  usemiss=No
  sewald=yes
 outsect=0x1c17
;
dependent Uses_contraception;
exposure 1;
replicate District_ID;
predictor Living_children Age Urban;
attr Uses_contraception binomial ;
attr Living_children nominal ;
attr Age ordinal ;
attr Urban ordinal ;

end;
model:'B. logistic regression with random intercept'  regression binomial   1 /
  cfn=1  cfnde=10  cfcsp=0
  toler=1e-008  tolem=0.01  tolran=1e-005  bayes=1  bayess2=1  bayeslat=1
  bayespoi=1
  iterem=250  iternr=50  itersv=50
  iterboot=500
  nseed=441506  nseedboot=0
  nrand=0
  usemiss=No
  sewald=yes  dummy=first
 outsect=0x1c17
;
dependent Uses_contraception;
exposure 1;
replicate District_ID;
predictor Living_children Age Urban;
cfactor 1 <- Uses_contraception;
attr Uses_contraception binomial ;
attr Living_children nominal ;
attr Age ordinal ;
attr Urban ordinal ;

end;
model:'C. 2-class logistic regression with class-specific intercept'  regression binomial   2 /
  toler=1e-008  tolem=0.01  tolran=1e-005  bayes=1  bayess2=1  bayeslat=1
  bayespoi=1
  iterem=250  iternr=50  itersv=50
  iterboot=500
  nseed=91911  nseedboot=0
  nrand=0
  usemiss=No
  sewald=yes  dummy=first
 outsect=0x1c17
;
dependent Uses_contraception;
exposure 1;
replicate District_ID;
predictor Living_children Age Urban;
attr Uses_contraception binomial ;
attr Living_children nominal ;
attr Age ordinal ;
attr Urban ordinal ;
classind Living_children Age Urban;

end;