Variance component models using lmer

On Mon, Jan 3, 2011 at 1:40 PM, Luciano La Sala
Dear everyone,

I have a dataset consisting of 144 measurements of egg volume from 48 nests
(3 eggs/nest). I am interested in answering the question of how much of the
variation in the response variable (egg volume) can be attributed to
within-nest variation and how much to among-nests variation. My model was
specified as follows:

model <- lmer(EggLength ~ NestID + (1|NestID), data = Data)
summary(model)
Linear mixed model fit by REML
Formula: EggLength ~ NestID + (1 | NestID)
? Data: Data
? AIC ? BIC logLik deviance REMLdev
?712.4 724.3 -352.2 ? ?697.9 ? 704.4

Random effects:
?Groups ? Name ? ? ? ?Variance Std.Dev.
?NestID ? (Intercept) 5.5917 ? 2.3647
?Residual ? ? ? ? ? ? 4.5025 ? 2.1219

Number of obs: 144, groups: NestID, 48

Fixed effects:
? ? ? ? ? ?Estimate Std. Error t value
(Intercept) 68.02159 ? ?1.26104 ? 53.94
NestID ? ? ? 0.02753 ? ?0.01540 ? ?1.79

Correlation of Fixed Effects:
? ? ? (Intr)
NestID -0.952
The formula doesn't make sense.  You have the covariate NestID as both
a fixed-effect (treated, incorrectly, as a numeric value) and a random
effect grouping factor.  You should specify the model formula as

EggLength ~ 1 + (1|NestID)
From the above output I extracted the variance components by squaring the
standard deviations, then expressing them as percentages:

vars <- c(5.5917, 4.5025)
100*vars/sum(vars)
[1] 55.39518 44.60482

At this point, I would conclude that egg volume varied 55.4% among clutches
and 44.6% within clutches.

I'd appreciate suggestions/corrections to my model specification and results
interpretation.

Thank you in advance.

Luciano

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

Variance component models using lmer

Thread (3 messages)