[R-sig-ME] glmer with/without intercept gave different results
Jarrod Hadfield
j.hadfield at ed.ac.uk
Thu Nov 18 17:33:48 CET 2010
Dear Ruby,
I do not think a REML solution exists for the Cell variance in this
instance (it's infinity). I presume the data for each Cell all have
the same Villus and Group? If so you would be better off reducing
your data to 250 binary data (i.e. One datum for each Cell) and
removing the (1|Cell) term from the model.
Cheers,
Jarrod
On 18 Nov 2010, at 16:18, Chang, Yu-Mei wrote:
> 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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>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
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>
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