[R] [OFF] Nested or not nested, this is the question.

Ronaldo Reis Jr. chrysopa at insecta.ufv.br
Thu Apr 10 15:40:30 CEST 2003

```Em Qua 09 Abr 2003 17:03, Peter Dalgaard BSA escreveu:

Peter,

thank for your exaplanation. But I still have doubt.

> Neither. First of all, you have numDF = 1 for things that have more
> than two levels, so you forgot to make them factors.
>
> reis\$plot<-factor(reis\$plot)
> reis\$size<-factor(reis\$size)
> reis\$specie<-factor(reis\$specie)

In this case size is quantitative, measurement in meter.
species is one DF because is a two level factor.

> Then you seem to be needing something that describes the replication,
> and you're not actually telling us, but if I guess that plots 1-4 is
> the 1st replication and 5-8 and 9-12 are the others, then this should
> work:
>
> reis\$repl <- factor((as.numeric(reis\$plot)-1)%/%4+1)
> table(reis\$plot,reis\$repl) # just to check

Ok, I understande this

> now you can do
>
> anova(lme(nsp/tot~size*specie,random=~1|repl/plot,data=reis))
>
> and have
>
>             numDF denDF   F-value p-value
> (Intercept)     1     8 207.18935  <.0001
> size            3     6  94.58027  <.0001
> specie          1     8  57.14293   1e-04
> size:specie     3     8  10.28573   4e-03

Here is my problem to understand.
Why is repl/plot, or repl/size? plot is the high level.
Why is not repl/specie or plot/specie?

I think this because in each repl I have two measurement of specie.
Is like the rats example of Crawley's book. repl = rat, specie = liver and
size = treatment, in this case the nested design is size/repl/specie.
The diference is that liver in rat is a random effect but specie is a fixed
effect. In rats example it make a simple nested anova, but dnt have any
example to make a nested ancova. It is a bit confused.

If I dont have real replication (one plot by size), only pseudoreplication?

> or, as I'd prefer in a balanced study:
>
> summary(aov(nsp/tot~specie*size+Error(repl+plot),data=reis))
>
> Error: repl
>           Df   Sum Sq  Mean Sq F value Pr(>F)
> Residuals  2 0.027708 0.013854
>
> Error: plot
>           Df   Sum Sq  Mean Sq F value    Pr(>F)
> size       3 0.305417 0.101806   94.58 1.927e-05 ***
> Residuals  6 0.006458 0.001076
> ---
> Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
>
> Error: Within
>             Df   Sum Sq  Mean Sq F value    Pr(>F)
> specie       1 0.041667 0.041667  57.143 6.551e-05 ***
> specie:size  3 0.022500 0.007500  10.286   0.00404 **
> Residuals    8 0.005833 0.000729
> ---
> Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
>
> (Error(repl/plot) actually works too because repl:plot is the same as plot)
>
> This gets a little confusin because "repl" is a coarsening of "plot".
> It may be easier with a within-repl numbering, which you can get by
> noting that plot is equivalent to repl:size
>
> anova(lme(nsp/tot~size*specie,random=~1|repl/size,data=reis))
> summary(aov(nsp/tot~specie*size+Error(repl/size),data=reis))

This example (create by me) is a manipulative example, but I try to understand
this for use with ecological "exploratory" data. In this case I only collect
the data, normally I can not manipulate (create) the "design". All book's
examples are perfectly data with tradicional design, its ease to understand.

Any help still welcome.
Please dont say "Read the book ..." I dont have money for books at the moment,
sorry.

Thanks for all
Ronaldo
--
APL is a write-only language.  I can write programs in APL, but I can't
-- Roy Keir
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