[R-sig-ME] glmer with/without intercept gave different results
Chang, Yu-Mei
ychang at rvc.ac.uk
Thu Nov 18 17:18:31 CET 2010
Dear Jarrod,
Yes, that's the culprit. The 10 repeated cells are either all 0's or
1's!
> table(table(Cyptoplasmic.vacuolation, Cell)[1,])
0 10
37 213
Many thanks!
Ruby
-----Original Message-----
From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk]
Sent: 18 November 2010 16:09
To: Chang, Yu-Mei
Cc: ONKELINX, Thierry; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] glmer with/without intercept gave different
results
Dear Ruby,
I notice that the fixed effect estimates are very small and the Cell
variance very large which may indicate numerical issues.
What does:
table(table(Cyptoplasmic.vacuolation, Cell)[1,])
look like?
Cheers,
Jarrod
On 18 Nov 2010, at 15:51, Chang, Yu-Mei wrote:
> Dear Thierry,
>
> I understood the hypotheses were different between the two models.
> What
> surprise me were the different estimated variances for the random
> effects and also the estimated differences between fixed effects
> levels.
>
>
> Ruby
>
> -----Original Message-----
> From: ONKELINX, Thierry [mailto:Thierry.ONKELINX at inbo.be]
> Sent: 18 November 2010 15:43
> To: Chang, Yu-Mei; r-sig-mixed-models at r-project.org
> Subject: RE: [R-sig-ME] glmer with/without intercept gave different
> results
>
> Dear Ruby,
>
> The hypotheses of those models are different. Hence the diference in
> p-values.
>
> Fit1:
> H0: Capsule 1 = 0
> H0: Capsule 2 - Capsule 1 = 0
> H0: Control - Capsule 1 = 0
>
> Fit2:
> H0: Capsule 1 = 0
> H0: Capsule 2 = 0
> H0: Control = 0
>
> However, the predictions of both model should be the same.
>
> Best regards,
>
> Thierry
>
>
>
------------------------------------------------------------------------
> ----
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek
> team Biometrie & Kwaliteitszorg
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> Research Institute for Nature and Forest
> team Biometrics & Quality Assurance
> Gaverstraat 4
> 9500 Geraardsbergen
> Belgium
>
> tel. + 32 54/436 185
> Thierry.Onkelinx at inbo.be
> 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
>
>
>> -----Oorspronkelijk bericht-----
>> Van: r-sig-mixed-models-bounces at r-project.org
>> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Chang, Yu-
>> Mei
>> Verzonden: donderdag 18 november 2010 15:22
>> Aan: r-sig-mixed-models at r-project.org
>> Onderwerp: [R-sig-ME] glmer with/without intercept gave
>> different results
>>
>> Dear all,
>>
>>
>>
>> I have fitted two glmer models (with or without intercept
>> term). I thought the results should be similar if not
>> identical, but they are quite different. I suspect it's
>> related to the random effects. Any suggestions on how to
>> proceed is greatly appreciated.
>>
>>
>>
>> Kind regards,
>>
>> Ruby Chang
>>
>>
>>
>> fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus)
>> + (1|Cell),family=binomial(link = "logit"))
>>
>> fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 +
>> (1|Villus) + (1|Cell),family=binomial(link = "logit"))
>>
>>
>>
>>> table(Cyptoplasmic.vacuolation, Group)
>>
>> Group
>>
>> Cyptoplasmic.vacuolation Capsule 1 Capsule 2 Control
>>
>> 0 560 340 1230
>>
>> 1 190 160 20
>>
>>
>>
>>> fit1 <- glmer(Cyptoplasmic.vacuolation ~ Group + (1|Villus) +
>> (1|Cell),family=binomial(link = "logit"))
>>
>>> summary(fit1)
>>
>> Generalized linear mixed model fit by the Laplace approximation
>>
>> Formula: Cyptoplasmic.vacuolation ~ Group + (1 | Villus) + (1 | Cell)
>>
>> AIC BIC logLik deviance
>>
>> 138.9 168.1 -64.47 128.9
>>
>> Random effects:
>>
>> Groups Name Variance Std.Dev.
>>
>> Cell (Intercept) 1983.5708 44.5373
>>
>> Villus (Intercept) 5.9475 2.4387
>>
>> Number of obs: 2500, groups: Cell, 250; Villus, 50
>>
>>
>>
>> Fixed effects:
>>
>> Estimate Std. Error z value Pr(>|z|)
>>
>> (Intercept) -11.945 9.123 -1.309 0.190
>>
>> GroupCapsule 2 -1.409 14.343 -0.098 0.922
>>
>> GroupControl -17.792 33.251 -0.535 0.593
>>
>>
>>
>> Correlation of Fixed Effects:
>>
>> (Intr) GrpCp2
>>
>> GroupCapsl2 -0.636
>>
>> GroupContrl -0.274 0.175
>>
>>> fit2 <- glmer(Cyptoplasmic.vacuolation ~ Group -1 + (1|Villus) +
>> (1|Cell),family=binomial(link = "logit"))
>>
>>> summary(fit2)
>>
>> Generalized linear mixed model fit by the Laplace approximation
>>
>> Formula: Cyptoplasmic.vacuolation ~ Group - 1 + (1 | Villus) + (1 |
>> Cell)
>>
>> AIC BIC logLik deviance
>>
>> 132.9 162.0 -61.43 122.9
>>
>> Random effects:
>>
>> Groups Name Variance Std.Dev.
>>
>> Cell (Intercept) 5.9933e+03 7.7417e+01
>>
>> Villus (Intercept) 1.2025e-07 3.4677e-04
>>
>> Number of obs: 2500, groups: Cell, 250; Villus, 50
>>
>>
>>
>> Fixed effects:
>>
>> Estimate Std. Error z value Pr(>|z|)
>>
>> GroupCapsule 1 -15.08 17.70 -0.852 0.394
>>
>> GroupCapsule 2 -14.72 19.29 -0.763 0.445
>>
>> GroupControl -18.23 54.54 -0.334 0.738
>>
>>
>>
>> Correlation of Fixed Effects:
>>
>> GrpCp1 GrpCp2
>>
>> GroupCapsl2 0.000
>>
>> GroupContrl 0.000 0.000
>>
>>
>> [[alternative HTML version deleted]]
>>
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>>
>
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