Re: [R] lmer and glmmPQL

From: Cox, Stephen <stephen.cox_at_ttu.edu>
Date: Thu 08 Dec 2005 - 02:16:37 EST


Thanks for the reply Doug!

A follow up question and comment ...

  1. If I understand correctly, looking at a simple situation in which SITES are nested in ZONES, the following should be similar. However, despite the same F values, the p-value from lmer is 1/2 the other methods. Why is this true?

> anova(lmer(RICH ~ ZONE + (1|SITE:ZONE), data))
Analysis of Variance Table

     Df Sum Sq Mean Sq Denom F value Pr(>F) ZONE 4 97.8 24.5 6610.0 2.1046 0.07753 .
> # make the nesting explicit
> data$SinZ = with(data, ZONE:SITE)[drop=TRUE]
> anova(lme(RICH ~ ZONE, data, random = ~1 | SinZ))

            numDF denDF   F-value p-value
(Intercept)     1  6600 100.38331  <.0001
ZONE            4    10   2.10459   0.155

> summary(aov(RICH ~ ZONE + Error(SITE:ZONE), data))

Error: SITE:ZONE

          Df Sum Sq Mean Sq F value Pr(>F)
ZONE       4  29669    7417  2.1046  0.155
Residuals 10 35243 3524

2) I think the anova problems with lmer may also apply to poisson. Compare the following (which includes a covariate). Based on the parameter estimates, the covariate should be significant. However, its anova p-value is .998:

> lmer(RICH ~ ZONE + lANPP + (1|SITE:ZONE), family = poisson, data)
Generalized linear mixed model fit using PQL

Formula: RICH ~ ZONE + lANPP + (1 | SITE:ZONE) 
   Data: data 
 Family: poisson(log link)
      AIC      BIC    logLik deviance

 9700.252 9754.628 -4842.126 9684.252
Random effects:
     Groups        Name    Variance    Std.Dev. 
  SITE:ZONE (Intercept)    0.069493     0.26361 
# of obs: 6615, groups: SITE:ZONE, 15

Estimated scale (compare to 1) 1.183970

Fixed effects:

              Estimate Std. Error z value Pr(>|z|)

(Intercept)  1.5169605  0.1533564  9.8917  < 2e-16 ***
ZONE2        0.4034169  0.2156956  1.8703  0.06144 .  
ZONE3       -0.1772011  0.2158736 -0.8209  0.41173    
ZONE4       -0.2368290  0.2158431 -1.0972  0.27254    
ZONE5       -0.1011186  0.2158114 -0.4686  0.63939    
lANPP        0.2201926  0.0081857 26.8995  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 


> anova(lmer(RICH ~ ZONE + lANPP + (1|SITE:ZONE), family = poisson,
data)) Analysis of Variance Table Df Sum Sq Mean Sq Denom F value Pr(>F) ZONE 4 2.809e-05 7.022e-06 6609 4.298e-06 1.0000 lANPP 1 5.229e-06 5.229e-06 6609 3.200e-06 0.9986 Thanks again for any insight you may be able to provide!!
> -----Original Message-----
> From: Douglas Bates [mailto:dmbates@gmail.com]
> Sent: Wednesday, December 07, 2005 8:28 AM
> To: Cox, Stephen
> Cc: r-help@stat.math.ethz.ch
> Subject: Re: [R] lmer and glmmPQL
>
> On 12/5/05, Cox, Stephen <stephen.cox@ttu.edu> wrote:
> > I have been looking into both of these approaches to conducting a
> > GLMM, and want to make sure I understand model
> specification in each.
> > In particular - after looking at Bates' Rnews article and searching
> > through the help archives, I am unclear on the
> specification of nested
> > factors in lmer. Do the following statements specify the same mode
> > within each approach?
> >
> > m1 = glmmPQL(RICH ~ ZONE, family = poisson, data, random = ~ YEAR |
> > SITE / QUADRAT)
> > m2 = lmer(RICH ~ ZONE +(YEAR|SITE)+ (YEAR|QUADRAT), family
> = poisson,
> > data)
>
> If you want to ensure that QUADRAT is nested within SITE then
> use the interaction operator explicitly
>
> m2 <- lmer(RICH ~ ZONE +(YEAR|SITE)+ (YEAR|SITE:QUADRAT),
> family = poisson, data)
>
> For the grouping factors nested versus non-nested depends on
> the coding. If QUADRAT has a distinct level for each
> SITE:QUADRAT combination then the nesting will automatically
> be detected. However, if the nesting is implicit (that is,
> if levels of QUADRAT are repeated at different SITES) then it
> is necessary to use the interaction operator. There is no
> harm in using the interaction operator when the nesting is explicit.
> >
> > As a follow up - what would be the most appropriate model formula
> > (using glmmPQL syntax) to specify both a nested facor and repeated
> > observations? Specifically, I am dealing with experimental
> data with
> > three factors. ZONE is a fixed effect. Three sites (SITE)
> are nested
> > within each ZONE. Multiple quadrats within each SITE are measured
> > across multiple years. I want to represent the nesting of
> SITE within
> > ZONE and allow for repeated observations within each
> QUADRAT over time
> > (the YEAR | QUADRAT random effect). -- I am assuming that
> glmmPQL is
> > the best option at this point because of recent discussion on Rhelp
> > about issues associated with the Matrix package used in lmer (i.e.,
> > the anova results do not seem to match parameter tests).
> >
>
> I believe the anova problems only occur with a binomial response.
> They are caused by my failure to use the prior.weights appropriately.
> For a Poisson model this should not be a problem.
>
> > Any information would be very much appreciated!
> >
> > Regards
> >
> > Stephen
> >
> > ______________________________________________
> > R-help@stat.math.ethz.ch mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide!
> > http://www.R-project.org/posting-guide.html
> >
>
> ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Received on Thu Dec 08 02:23:17 2005

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