gee, geese and glmer
On 14-03-07 11:25 PM, Ming-Huei Chen wrote:
Hi Ben, In an analysis we found that glmer in new lme4 gave result different from old lme4, gee and geese, where old lme4 seems to be closer to gee and geese.. Please see highlighted sex effect below. Case by sex (2x2) table is also given. Can you please let us know how would you look into the results? Thanks!
[cc'ing to r-sig-mixed-models: **please** try r-sig-mixed-models first, not personal e-mail to me ...] I can't say exactly what's going here; without having a reproducible example <http://tinyurl.com/reproducible-000> it's hard to say precisely. Thoughts: * gee and geese are giving _exactly_ the same parameter estimates, to 8 significant digits, so I would guess they are wrapping identical underlying methods. * As far as diagnosing the issue with lme4 1.0-6: * does changing the optimization method, i.e. glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")) [must do library("optimx") first] or glmerControl(optimizer="bobyqa") change the result? * I would be curious whether the soon-to-be-released version 1.1-4 (which can be installed from github or lme4.r-forge.r-project.org/repos) gives either (1) convergence warnings or (2) different/better answers * You can try specifying the starting values for lme4 to diagnose misconvergence; for example, start lme4 from the estimates given by old lme4/lme4.0 and see if it gives a similar answer. * You can use the 'slice' and 'splom.slice' functions from bbmle to visualize the likelihood surfaces good luck, Ben Bolker
Ming-Huei
###GEE
summary(gee(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomial,data=data))$coef
Estimate Naive S.E. Naive z Robust S.E. Robust z (Intercept) -1.88047373 0.13532162 -13.8963286 0.15960440 -11.782092 sex -0.23436854 0.08611269 -2.7216494 0.09050577 -2.589543 PC1 -0.05478639 0.06195318 -0.8843192 0.06822178 -0.803063 PC2 -0.09934572 0.06494563 -1.5296753 0.06520811 -1.523518 PC3 -0.07020391 0.06626875 -1.0593818 0.06962147 -1.008366 PC4 -0.13413097 0.06746716 -1.9880927 0.06979901 -1.921674
###GEESE
summary(geese(case~sex+PC1+PC2+PC3+PC4,id=famid,family=binomial,data=data))$mean
estimate san.se wald p
(Intercept) -1.88047373 0.15960440 138.8176912 0.000000000
sex -0.23436854 0.09050577 6.7057312 0.009610351
PC1 -0.05478639 0.06822178 0.6449102 0.421938319
PC2 -0.09934572 0.06520811 2.3211071 0.127629159
PC3 -0.07020391 0.06962147 1.0168016 0.313278888
PC4 -0.13413097 0.06979901 3.6928324 0.054646745
### lme4_0.999999-2
summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=data))
Estimate Std. Error z value Pr(>|z|) (Intercept) -3.01599 0.28305 -10.655 <2e-16 *** sex -0.41056 0.16285 -2.521 0.0117 * PC1 -0.17116 0.12903 -1.326 0.1847 PC2 -0.15510 0.13382 -1.159 0.2465 PC3 -0.19044 0.13580 -1.402 0.1608 PC4 0.02532 0.13732 0.184 0.8537 ###lme4_1.0-6
summary(glmer(case~sex+PC1+PC2+PC3+PC4+(1|famid),family=binomial,data=data))
Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.2784 0.8631 -11.909 <2e-16 ***
sex 0.3497 0.1975 1.770 0.0767 .
PC1 -0.3555 0.1623 -2.190 0.0285 *
PC2 -0.1087 0.1653 -0.657 0.5109
PC3 -0.2242 0.1652 -1.357 0.1748
PC4 0.1103 0.1671 0.660 0.5091
Case by sex
1 2
0 2554 3021
1 310 290