Re: [R] publishing random effects from lme

From: Dimitris Rizopoulos <dimitris.rizopoulos_at_med.kuleuven.ac.be>
Date: Fri 04 Feb 2005 - 20:45:11 EST

If you have heteroscedasticity problems, the nlme package has many varFunctions (e.g., varPower, varIdent, etc.) that could assist you in fitting it. The usage of GLMMs is mainly for discrete and count data that you cannot fit with lme.

Testing between competing lme models should be done via LRTs and the anova.lme() function. However, take care of the fitting procedure (REML vs ML), especially in case you also change the fixed-effects. The latter has been recently discussed on the list.

I hope it helps.

Best,
Dimitris



Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium

Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat
     http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm


> Hi Dieter,
>
> Yes, Iīve tried both options. The anova(lme(...)) gives me good
> results for the fixed effects part, but what Iīm specifically
> interested in is what to do with the random effects.
>
> I have tried glmmPQL (generalized linear mixed-effects models),
> which did in fact greatly help account for heteroscedasticity, but I
> canīt do model simplification with these models (and theyīre still
> heavily debated, as I read from previous postings to "R Help".
>
> How would you deal with the random effects part of the models when
> publishing results from lme?
>
> Thanks for your help!
> Christoph
>
>
>
>
>
>
>
> ###
> Here are my original questions once again (with an example below):
>
> 1) What is the total variance of the random effects at each level?
> (2) How can I test the significance of the variance components?
> (3) Is there something like an "r squared" for the whole model which
> I can state? ##it seems, there isnīt (as I learned from a previous
> posting
>
> The data come from an experiment on plant performance with and
> without insecticide, with and without grasses present, and across
> different levels of plant diversity ("div").
>
> Thanks for your help!
> Christoph.
>
> lme(asin(sqrt(response)) ~ treatment + logb(div + 1, 2) + grass,
> random = ~ 1 | plotcode/treatment, na.action = na.exclude, method =
> "ML")
>
> Linear mixed-effects model fit by maximum likelihood
>
> Data: NULL
> AIC BIC logLik
> -290.4181 -268.719 152.209
>
> Random effects:
> Formula: ~ 1 | plotcode
> (Intercept)
> StdDev: 0.04176364
>
> Formula: ~ 1 | treatment %in% plotcode
> (Intercept) Residual
> StdDev: 0.08660458 0.00833387
>
> Fixed effects: asin(sqrt(response)) ~ treatment + logb(div + 1, 2) +
> grass
> Value Std.Error DF t-value p-value
> (Intercept) 0.1858065 0.01858581 81 9.997225 <.0001
> treatment 0.0201384 0.00687832 81 2.927803 0.0044
> logb(div + 1, 2) -0.0203301 0.00690074 79 -2.946073 0.0042
> grass 0.0428934 0.01802506 79 2.379656 0.0197
>
> Standardized Within-Group Residuals:
> Min Q1 Med Q3 Max
> -0.2033155 -0.05739679 -0.00943737 0.04045958 0.3637217
>
> Number of Observations: 164
> Number of Groups:
> plotcode ansatz %in% plotcode
> 82 164
>
>
>
>
>
>
> Dieter Menne wrote:
>
>>>Suppose I have a linear mixed-effects model (from the package nlme)
>>>with nested random effects (see below); how would I present the
>>>results from
>> the random effects part in a publication?
>>
>>
>>Have you tried anova(lme(....))?
>>
>>Your asin(sqrt()) looks a bit like these are percentages of counts.
>>The method is still quoted in old books, but has fallen a bit out of
>>favor. Have you thought of some glm model instead
>>(http://www.stats.ox.ac.uk/pub/MASS4/)?
>>Dieter Menne
>>
>>______________________________________________
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>>PLEASE do read the posting guide!
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>>
>>
>
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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 Fri Feb 04 19:56:19 2005

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