[R-sig-ME] glmer with/without intercept gave different results

[Jarrod Hadfield]

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
>>
>>
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>>
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