[R] mixed model nested ANOVA (part two)
mark_difford at yahoo.co.uk
Sun Feb 24 22:07:29 CET 2008
>> Also i have read in Quinn and Keough 2002, design and analysis of
>> experiments for
>> biologists, that a variance component analysis should only be conducted
>> after a rejection
>> of the null hypothesis of no variance at that level.
Once again the caveat: there are experts on this list who really know about
this stuff, and I am not one of them. Your general strategy would be to set
up two models with the same fixed effects, one of which doesn't have random
effects. You then test the two models using anova(mod.withRandom,
I haven't tried this using lmer/2(), but with lme() you do this by fitting
your fixed+random effects model using lme() and your fixed-only effects
model using lm(). If you are using weights to model heteroskedasticity,
then it's better to use gls(), as this will accept the same weights argument
as the call to lme().
Then you simply do anova(lme.model, lm/gls.model). This tells you about the
significance of your random effects, i.e. whether you need a random-effects
mod.rand <- lme(fixed=y ~ x, random=~x|Site, data=...)
mod,fix <- lm(fixed=y ~ x, data=...)
Stephen Cole-2 wrote:
> First of all thank you for the responses. I appreciate the
> suggestions i have received thus far.
> Just to reiterate
> I am trying to analyze a data set that has been collected from a
> hierarchical sampling design. The model should be a mixed model
> nested ANOVA. The purpose of my study is to analyze the variability
> at each spatial scale in my design (random factors, variance
> components), and say something about the variability between regions
> (fixed factor, contrast of means). The data is as follows;
> region (fixed)
> Location (random)
> site nested in location nested in region.
> Also i have read in Quinn and Keough 2002, design and analysis of
> experiments for biologists, that a variance component analysis should
> only be conducted after a rejection of the null hypothesis of no
> variance at that level.
> I have tried to implement
> mod1<-lmer(density ~ 1 + (1|site) + (1|location) + (1|region))
> However, as i understand it, this treats all my factors as random.
> Plus I do not know how to extract SS or MS from this model.
> anova(mod1) gives me
> Analysis of Variance Table
> Df Sum Sq Mean Sq
> and summary(mod1) gives me
> Linear mixed-effects model fit by REML
> Formula: density ~ 1 + (1 | site) + (1 | location) + (1 | region)
> AIC BIC logLik MLdeviance REMLdeviance
> 15658 15678 -7825 15662 15650
> Random effects:
> Groups Name Variance Std.Dev.
> site (Intercept) 22191 148.97
> location (Intercept) 33544 183.15
> region (Intercept) 41412 203.50
> Residual 696189 834.38
> number of obs: 960, groups: site, 4; location, 4; region, 3
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 261.3 168.7 1.549
> from what i understand the variance in the penultimate column are my
> variance components. But how do i conduct my significance test?
> I have also tried
> mod1<-lmer(density ~ region + (1|site) + (1|location))
> Which i think is the correct mixed model for my design. However once
> again i do not know how to evaluate significance for the random
> Thank-you again for any additional advice i receive
> Stephen Cole
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained, reproducible code.
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