[R-sig-ME] lmer vs glmmPQL
Ken Beath
ken at kjbeath.com.au
Tue Jun 23 23:46:49 CEST 2009
On 24/06/2009, at 2:18 AM, Federico Calboli wrote:
> Hi All,
>
> I'm doing a simple logistic regression with one fixed and one random
> effects, and I'm comparing the results I got from lmer() and
> glmmPQL(). I'm finding that lmer gives my a "better" p-value for my
> fixed effects. Because I'm a paranoid old man I'd go for the glmmPQL
> results then, but my collaborators are less paranoid and I'm sure
> they'd prefer the results from lmer. Am I too conservative? (I
> ralise it looks like I'm asking for counselling more than advice,
> but there you go...).
>
This seems to results from the use of a t-test with few df in glmmPQL
and z in lmer. z seems fine to me. What is more of a problem is that
your random effects variance is effectively 0. There are only 3 blocks
so fitting a random effects model will be difficult and appears
unnecessary.
Ken
> Best,
>
> Federico
>
> My models:
>
> mod1 = glmmPQL(y ~ genotype, random = ~1|block, family = binomial,
> data)
> mod2 = lmer(y ~ genotype + (1|block), family = binomial, data)
>
> my data:
>
> > data
> genotype block y.1 y.2
> 1 A a 16 29
> 2 B a 19 26
> 3 C a 23 23
> 4 A c 6 24
> 5 B c 11 11
> 6 C c 13 14
> 7 A b 4 17
> 8 B b 10 8
> 9 C b 12 6
>
>
> > data[[1]]
> [1] A B C A B C A B C
> attr(,"contrasts")
> [,1] [,2]
> B 1 -1
> A -2 0
> C 1 1
> Levels: B A C
>
> my results:
>
> > summary(mod1)
> Linear mixed-effects model fit by maximum likelihood
> Data: dat
> AIC BIC logLik
> NA NA NA
>
> Random effects:
> Formula: ~1 | block
> (Intercept) Residual
> StdDev: 1.285532e-06 0.8077838
>
> Variance function:
> Structure: fixed weights
> Formula: ~invwt
> Fixed effects: y ~ genotype
> Value Std.Error DF t-value p-value
> (Intercept) -0.3327269 0.12516679 4 -2.658269 0.0565
> genotype1 0.3288359 0.09065856 4 3.627190 0.0222
> genotype2 0.1138920 0.14947659 4 0.761938 0.4885
> Correlation:
> (Intr) gntyp1
> genotype1 -0.068
> genotype2 -0.027 -0.019
>
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -1.0807836 -0.8047002 -0.4620287 0.8940787 1.5832300
>
> Number of Observations: 9
> Number of Groups: 3
>
>
> > summary(mod2)
> Generalized linear mixed model fit by the Laplace approximation
> Formula: y ~ genotype + (1 | block)
> Data: dat
> AIC BIC logLik deviance
> 13.92 14.71 -2.960 5.919
> Random effects:
> Groups Name Variance Std.Dev.
> block (Intercept) 0 0
> Number of obs: 9, groups: block, 3
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -0.33273 0.12652 -2.630 0.008541 **
> genotype1 0.32884 0.09164 3.588 0.000333 ***
> genotype2 0.11389 0.15109 0.754 0.450965
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) gntyp1
> genotype1 -0.068
> genotype2 -0.027 -0.019
>
>
>
>
> --
> Federico C. F. Calboli
> Department of Epidemiology and Public Health
> Imperial College, St. Mary's Campus
> Norfolk Place, London W2 1PG
>
> Tel +44 (0)20 75941602 Fax +44 (0)20 75943193
>
> f.calboli [.a.t] imperial.ac.uk
> f.calboli [.a.t] gmail.com
>
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