Thanks for your reply, I have tried the code, below and I have very strange results like very high standard errors, what might be the case? because I sude glimmiw for equal slopes and the results look attractive
proc nlmixed data=longil_cat noad qpoints=30;
title 'BMICAT, PROC NLMIXED, ordinal, adaptive, q=30';
parms int1=2.5080 int2=4.5586 d= 0.07258;
eta1 =int1+ b+beta11*time + beta12***bleep**e + beta13*smoking + beta14*sex + beta15*time***bleep**e + beta16*time*sex;
eta2 = int2 + b + beta11*time + beta22***bleep**e + beta13*smoking + beta14*sex + beta25*time***bleep**e + beta26*time*sex;
if bmi_cat=1 then z = (1/(1+exp(-eta1)));
else if bmi_cat=2 then z = (1/(1+exp(-eta1))) - (1/(1+exp(-eta2)));
else z = 1 - (1/(1+exp(-eta2)));
if z > 1e-20 then ll = log(z);
else ll = -1e10;
model bmi_cat ~ general(ll);
random b ~normal(0,d**2) subject= id out=EB;
estimate 'var(d)' d*d;
run;
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