[R] Mixed model Nested ANOVA

[Mark Difford]

Hi Stephen,

Hopefully you will get an answer from one of the experts on mixed models who
subscribe to this list.  However, you should know that both lme() and lmer()
currently have anova() methods.  The first will give you p-values (but no
SS), and the second will give you SS (but no p-values).  You can, however,
get the latter using functions in the languageR package.  This also has an
aovlmer.fnc() that uses MCMC to get p-values.

>From what you have said about your data, and about what you want from them,
you clearly should be using either lme() or lmer().  Further, objects from
both functions work with glht() from the multcomp package, so you also have
access to a full range of post-hoc tests.

HTH, Mark.


Stephen Cole-2 wrote:
> 
> hello R help
> 
> 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.
> 
> I would like to run this as an ANOVA and then compute variance components.
> 
> My question is when i use the aov command;   mod1 <- aov(density ~
> region/location/site)
> is there any way to tell R which factor is random and fixed.
> 
> I know i can specify fixed and random factors using lme or lmer but these
> methods do not allow me to extract an anova table (or do they?)
> I know that the data can be analyzed using a nested ANOVA because i have
> based my design on several papers in the marine biological literature
> (MEPS).  Thank-you for any advice in advance.
> 
> Stephen Cole
> 
> 	[[alternative HTML version deleted]]
> 
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