# Re: [R] publishing random effects from lme

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

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").

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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