I have repeated measures data that contains subjects' performance on a task over many trials. I also have a group variable as well (three groups, and this variable is categorical- there is no order to the groups).
In NLMIXED, I get the parameters for an exponential function that describes the learning curves. Specifically, I have a parameter that reflects the asymptote and another that reflects the learning rate (ie, a time constant). I also find that there is a significant effect of my group variable on the asymptote parameter, but not the learning rate. This all looks good and the fit is nice.
**How can I test for differences between pairs of groups?** Ie, the hypothesis that Group A does not differ from Group B, but does differ from Group C. Here are some avenues I've tried, with little luck:
1. Use the CONTRAST statement. This really seems like the what I'm supposed to do, but I just can't figure out the syntax, and how to code the data to use it! I read that it's *not* executed the same was as the contrast statement in PROC MIXED, but I can't seem to find the right way.
2. Get each individual subjects' parameter estimate, then determine (in a separate analysis) whether there is a signif effect of group on the estimate. The output gives me the mean and variance of the estimate, but can I get the estimate for each individual? Or, should I run NLMIXED on each subject (without the group variable, of course), and get the parameters from each output?
Would either of these work? Thanks for any insight!
Code so far:
PROC NLMIXED DATA = data;
PARMS m_g0 = 1 m_g2 = -0.5 b01 = 0 b21 = 0
s2e = 0.1 v_Ey0 = 0.1 v_Ey1 = 0.1 c_01 = 0.1;
g0 = m_g0 + b01*group + Ey0;
g2 = m_g2 + b21*group + Ey1;
eqn = g0 + (-exp(trial * g2)) ;
MODEL adapt ~ normal(eqn,s2e) ;
RANDOM Ey0 Ey1 ~ normal([0, 0], [v_Ey0, c_01, v_Ey1])
SUBJECT = subject;
~AK
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