[R] mixed model nested ANOVA (part two)

[Mark Difford]

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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