Hi, I have conducted a mixed model for longitudinal data using PROC GLIMMIX. The code I used is below: proc glimmix data=diss method=laplace;
title Model 15: Total Support Final Model;
class carnegie (Ref='0') barrons (Ref='0') flagship(Ref='0');
EFFECT poly = polynomial(time/degree=2);
model totalsupport =
poly carnegie barrons flagship statelog
poly*carnegie poly*flagship poly*statelog
poly*carnegie*statelog poly*flagship*statelog
/dist=gamma link=log solution;
random intercept time / type=ARH(1) subject = id;
run; I had two questions about interpreting the output: As you can see, this is model uses a log link and the one continuous predictor is also transformed onto a log scale (statelog). Solutions for Fixed Effects Effect carnegie barrons flagship Estimate Standard Error DF t Value Pr > |t| Intercept 1.8144 0.6323 4661 2.87 0.0041 time 1.0161 0.1916 4661 5.30 <.0001 time^2 -0.04453 0.01360 4661 -3.27 0.0011 carnegie 1 -0.6513 0.09370 4661 -6.95 <.0001 carnegie 2 -0.5665 0.1611 4661 -3.52 0.0004 carnegie 0 0 . . . . barrons 1 0.3896 0.08327 4661 4.68 <.0001 barrons 2 -0.2169 0.09055 4661 -2.40 0.0166 barrons 0 0 . . . . flagship 1 -0.6884 0.1304 4661 -5.28 <.0001 flagship 0 0 . . . . statelog 1.5370 0.1524 4661 10.09 <.0001 time*carnegie 1 1.0983 0.1819 4661 6.04 <.0001 time*carnegie 2 -0.04387 0.2661 4661 -0.16 0.8691 time*carnegie 0 0 . . . . time^2*carnegie 1 -0.07228 0.01423 4661 -5.08 <.0001 time^2*carnegie 2 -0.01247 0.01982 4661 -0.63 0.5293 time^2*carnegie 0 0 . . . . time*flagship 1 -1.3081 0.2154 4661 -6.07 <.0001 time*flagship 0 0 . . . . time^2*flagship 1 0.08469 0.01663 4661 5.09 <.0001 time^2*flagship 0 0 . . . . statelog*time -0.2541 0.04758 4661 -5.34 <.0001 statelog*time^2 0.01149 0.003387 4661 3.39 0.0007 statelog*time*carnegie 1 -0.2803 0.04672 4661 -6.00 <.0001 statelog*time*carnegie 2 0.01977 0.06687 4661 0.30 0.7675 statelog*time*carnegie 0 0 . . . . statelog*time^2*carnegie 1 0.01843 0.003647 4661 5.05 <.0001 statelog*time^2*carnegie 2 0.002585 0.004956 4661 0.52 0.6019 statelog*time^2*carnegie 0 0 . . . . statelog*time*flagship 1 0.3230 0.05414 4661 5.97 <.0001 statelog*time*flagship 0 0 . . . . statelog*time^2*flagship 1 -0.02112 0.004179 4661 -5.05 <.0001 statelog*time^2*flagship 0 0 . . . . My understanding is that I would interpret this as you would a linear regression model with logged outcome and logged predictor (% change in predictor would lead to a % change in outcome) so for: statelog 1.5370 0.1524 4661 10.09 <.0001 As statelog increases 1.5370%, logged outcome would increase 1%. 1) Is there a way to convert this data in PROC GLIMMIX so that I can just interpret as increase in statelog = increase in 1 unit of logged outcome. I read about using lsmeans and ilink but that just broke down my categorical predictors. 2) I used an ARH(1) covariance structure for this but I'm unsure how to interpret it. I understand how to interpret the VC or UN but not ARH(1) as I've never had to use it before. Covariance Parameter Estimates Cov Parm Subject Estimate Standard Error Var(1) id 0.5255 0.04765 Var(2) id 0.001706 0.000193 ARH(1) id -0.4835 0.04940 Residual 0.1098 0.002474 Thank you!
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