[R-sig-ME] problem with lme4 glmer

[Rhodes, Justin S]

Dear R project mixed models users:

We used "lmer4", "glmer" function see below.  I attached the data set.  The programs and results for SAS and R are shown below.  Results are incredibly different, and seem impossible to explain by differences in computational algorithms.  The estimates from SAS are reasonable, but the estimates from R are clearly wrong, based on looking at the simple data.  We realize that we are underpowered to estimate the random effect here, but it still should give reasonable estimates if it converges, right?  Can someone please help us figure this out?  Thanks very much for any information

R code:

#import data
Pole<-read.table("Pole.txt",header = TRUE)

#define factors
Pole$Color<-as.factor(Pole$Color)
Pole$Treatment<-as.factor(Pole$Treatment)
Pole$ID<-as.factor(Pole$ID)

#model statement
fm1<-glmer(Outcome ~ Eggs + Color + Treatment + (1|ID), family=binomial, data=Pole)

#results
summary(fm1)

                                               Estimate            Std. Error            z value                 Pr(>|z|)
(Intercept)                         -23.4673             12.4832                -1.880                                0.06012 .
Eggs                                    -0.0496                3.4036                 -0.015                  0.98837
Colorspotted                     36.0295               8.4055                  4.286                   1.82e-05 ***
Treatmentsharp                12.4964              4.1453                 3.015                    0.00257 **
---


SAS code:

proc genmod data=temp.Pole;
class ID Treatment Color;
model Outcome= Eggs Color Treatment/d=bin link=logit;
repeated subject=ID/type=cs;
run;

Analysis Of GEE Parameter Estimates
Empirical Standard Error Estimates
Parameter

Estimate
Standard
Error
95% Confidence Limits
Z
Pr > |Z|
Intercept

-1.0491
1.3990
-3.7911
1.6928
-0.75
0.4533
Eggs

-0.1071
0.4632
-1.0151
0.8008
-0.23
0.8171
Color
blue
1.5046
0.8476
-0.1567
3.1660
1.78
0.0759
Color
spot
0.0000
0.0000
0.0000
0.0000
.
.
Treatment
blunt
0.3908
0.2841
-0.1660
0.9476
1.38
0.1690
Treatment
sharp
0.0000
0.0000
0.0000
0.0000
.
.




Thanks very much for your help!!

Justin Rhodes
Professor
Department of Psychology
Beckman Institute

405 N Mathews Ave
Urbana, IL 61801

Affiliations:  Neuroscience Program, Program for Ecology, Evolution and Conservation Biology, Institute for Genomic Biology, Division of Nutritional Sciences

Email: jrhodes using illinois.edu<mailto:jrhodes using illinois.edu>
Phone: 217-265-0021

Website: http://rhodeslab.beckman.illinois.edu/


-------------- next part --------------
An embedded and charset-unspecified text was scrubbed...
Name: Pole2.txt
URL: <https://stat.ethz.ch/pipermail/r-sig-mixed-models/attachments/20201019/95448317/attachment.txt>