[R] publishing random effects from lme
dimitris.rizopoulos at med.kuleuven.ac.be
Fri Feb 4 10:45:11 CET 2005
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.
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
----- Original Message -----
From: "Christoph Scherber" <Christoph.Scherber at uni-jena.de>
To: "Dieter Menne" <dieter.menne at menne-biomed.de>
Cc: <r-help at stat.math.ethz.ch>
Sent: Friday, February 04, 2005 10:09 AM
Subject: Re: [R] publishing random effects from lme
> Hi Dieter,
> Yes, I´ve tried both options. The anova(lme(...)) gives me good
> results for the fixed effects part, but what I´m 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
> can´t do model simplification with these models (and they´re 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!
> 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 isn´t (as I learned from a previous
> 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!
> lme(asin(sqrt(response)) ~ treatment + logb(div + 1, 2) + grass,
> random = ~ 1 | plotcode/treatment, na.action = na.exclude, method =
> Linear mixed-effects model fit by maximum likelihood
> Data: NULL
> AIC BIC logLik
> -290.4181 -268.719 152.209
> Random effects:
> Formula: ~ 1 | plotcode
> 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) +
> 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
>> 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
>>R-help at stat.math.ethz.ch mailing list
>>PLEASE do read the posting guide!
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide!
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