[R-sig-ME] gee, geese and glmer

[Ben Bolker]

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
>