Re: [R] mixed model nested ANOVA (part two)

From: Mark Difford <mark_difford_at_yahoo.co.uk>
Date: Sun, 24 Feb 2008 13:25:28 -0800 (PST)

Hi Stephen,

Slip of the dactylus: lm() does not, of course, take a fixed=arg. So you need

To recap:
mod.rand <- lme(fixed=y ~ x, random=~x|Site, data=...) mod,fix <- lm(y ~ x, data=...) ## or
##mod,fix <- lm(formula=y ~ x, data=...)

Bye.

Mark Difford wrote:

> 
> Hi Stephen,
> 
>>> 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, modWithoutRandom).
> 
> 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 component.
> 
> To recap:
> mod.rand <- lme(fixed=y ~ x, random=~x|Site, data=...)
> mod,fix <- lm(fixed=y ~ x, data=...)
> 
> anova(mod.rand, mod.fix)
> 
> HTH, Mark.
> 
> 
> 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(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
>> factors.
>>
>> Thank-you again for any additional advice i receive
>>
>> Stephen Cole
>>
>> ______________________________________________
>> R-help_at_r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
>
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