From: Christoph Scherber <Christoph.Scherber_at_uni-jena.de>

Date: Fri 04 Feb 2005 - 20:09:28 EST

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 19:23:59 2005

Date: Fri 04 Feb 2005 - 20:09:28 EST

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):

- 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 > >______________________________________________ >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 Fri Feb 04 19:23:59 2005

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