Re: [R] publishing random effects from lme

[Christoph Scherber]

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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