Dear all,
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?
Specifically, IŽd like to know:
(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?
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 -268.719 152.209Random effects:
Formula: ~ 1 | plotcode
(Intercept)
StdDev: 0.04176364 Formula: ~ 1 | treatment %in% plotcode
(Intercept) Residual
StdDev: 0.08660458 0.00833387Fixed 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 0.0197Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-0.2033155 -0.05739679 -0.00943737 0.04045958 0.3637217Number of Observations: 164
Number of Groups:
plotcode ansatz %in% plotcode
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