Hi, my dependent variable is a proportion ("prob.bind"), and the independent variables are factors for group membership ("group") and a covariate ("capacity"). I am interested in the effects of group, capacity, and their interaction. Each subject is observed on all (4) levels of capacity (I use capacity as a covariate because the effect of this variable is normatively linear). I fit three models, but I am observing differences between the three.
The first model is a quasibinomial without any subject effects using glm. The second is a random-effects model using lmer. The third model is a generalized estimating equation using gee from the gee package in which I cluster for the subject using an unstructured correlation matrix. The results of the first and the third model almost coincide, but the second, using lmer, shows an insginficant coefficient where I would expect a significant one. The other 2 models show the coefficient significant. I do not really have experience with gee. Therefore I apologize in advance for my ignorant question whether one of lmer and gee is preferable over the other in this setting? Thanks for any advice, Daniel Below is the output of the three models. ----- GLM ----- Call: glm(formula = prob.bind ~ capacity * group, family = quasibinomial, subset = c(combination == "gnl")) Deviance Residuals: Min 1Q Median 3Q Max -18.9843 -4.1129 0.3816 6.0047 18.1858 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -3.4274 0.4641 -7.386 1.10e-12 *** capacity 0.9931 0.1281 7.754 9.55e-14 *** group2 0.7242 0.6337 1.143 0.25392 group3 2.0264 0.6168 3.286 0.00112 ** capacity:group2 -0.1523 0.1764 -0.863 0.38864 capacity:group3 -0.3885 0.1742 -2.231 0.02633 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for quasibinomial family taken to be 39.01488) Null deviance: 22672 on 359 degrees of freedom Residual deviance: 15813 on 354 degrees of freedom AIC: NA Number of Fisher Scoring iterations: 5 ----- LMER ----- Generalized linear mixed model fit using Laplace Formula: prob.bind ~ capacity * group + (1 | subject) Subset: c(combination == "gnl") Family: quasibinomial(logit link) AIC BIC logLik deviance 11082 11109 -5534 11068 Random effects: Groups Name Variance Std.Dev. subject (Intercept) 42.977 6.5557 Residual 26.845 5.1813 number of obs: 360, groups: subject, 90 Fixed effects: Estimate Std. Error t value (Intercept) -3.8628 1.2701 -3.041 capacity 1.1219 0.1176 9.542 group2 0.9086 1.7905 0.507 group3 2.3700 1.7936 1.321 capacity:group2 -0.1745 0.1610 -1.083 capacity:group3 -0.3807 0.1622 -2.348 Correlation of Fixed Effects: (Intr) capcty group2 group3 cpct:2 capacity -0.322 group2 -0.709 0.228 group3 -0.708 0.228 0.502 capcty:grp2 0.235 -0.730 -0.310 -0.167 capcty:grp3 0.233 -0.725 -0.166 -0.305 0.529 ----- GEE ----- GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA gee S-function, version 4.13 modified 98/01/27 (1998) Model: Link: Logit Variance to Mean Relation: Binomial Correlation Structure: Unstructured Call: gee(formula = prob.bind ~ capacity * group, id = subject, subset = c(combination == "gnl"), family = binomial, corstr = "unstructured") Summary of Residuals: Min 1Q Median 3Q Max -0.8397112 29.7353417 59.2605133 89.2223581 99.8099842 Coefficients: Estimate Naive S.E. Naive z Robust S.E. Robust z (Intercept) -3.4798395 0.4910274 -7.0868545 0.4739913 -7.3415687 capacity 1.0149659 0.1366365 7.4282170 0.1284162 7.9037210 group2 0.7781014 0.6691731 1.1627806 0.7424769 1.0479807 group3 2.0720270 0.6527565 3.1742727 0.6234005 3.3237495 capacity:group2 -0.1750448 0.1877361 -0.9323982 0.2060484 -0.8495325 capacity:group3 -0.4021872 0.1865916 -2.1554413 0.1724780 -2.3318168 Estimated Scale Parameter: 39.28916 Number of Iterations: 3 Working Correlation [,1] [,2] [,3] [,4] [1,] 1.00000000 0.1632065 0.04525213 -0.08946253 [2,] 0.16320653 1.0000000 0.17635584 0.16703313 [3,] 0.04525213 0.1763558 1.00000000 0.22291895 [4,] -0.08946253 0.1670331 0.22291895 1.00000000 ------------------------- cuncta stricte discussurus ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.