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 at illinois.edu<mailto:jrhodes at illinois.edu>
Phone: 217-265-0021
Website: http://rhodeslab.beckman.illinois.edu/
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problem with lme4 glmer
2 messages · Rhodes, Justin S, Thierry Onkelinx
Dear Justin, First of all you are comparing two different algorithms: GEE vs mixed models. GEE estimates 'population average' estimates for the fixed effect. The mixed models fixed effect refers to an average individual. Those will be by definition different. Very large estimates and standard errors indicate (quasi) complete separation, leading to numerical instability. Rather a problem with the data / model formulation than with the algorithm. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> Op ma 19 okt. 2020 om 19:46 schreef Rhodes, Justin S <jrhodes at illinois.edu>:
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 at illinois.edu<mailto:jrhodes at illinois.edu>
Phone: 217-265-0021
Website: http://rhodeslab.beckman.illinois.edu/
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