Dear all
I'm not sure if I did the right analysis for my specific split splot
design. We are
studying biomass increase with different CO2 concentrations with four
different
functional plant groups (e.g. grasses, herbs, broad-leafed trees and
conifers). Of each
functional plant group we have four species. The design is orthogonal.
The design is:
Blocks: 2 (climate chambers, called Gruppe)
Plot: 3 (3 CO2 concentrations)
whole plot treatment is CO2
in each chamber we have the same 4 functional plant groups (funktGr),
each represented by
four species (the species are nested in the functional plant groups,
because if I take
species as a single factor I get 16 species, which is not true.
in each chamber I have four replicates (pseudoreplicates)
half of the plants got fertilisation (Fert), that means each plant got
it's own
fertilisation
the response variable is biomass (g)
Randomisation was done to plot and subplot level.
I tried this:
aov(log(g)~CO2*funktGr/Species*Fert+Error(Gruppe/CO2),data=biomass)
the output is:
Error: Gruppe
Df Sum Sq Mean Sq F value Pr(>F)
Residuals 1 0.11923 0.11923
Error: Gruppe:CO2
Df Sum Sq Mean Sq F value Pr(>F)
CO2 2 14.0297 7.0149 872.65 0.001145 **
Residuals 2 0.0161 0.0080
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Error: Within
Df Sum Sq Mean Sq F value Pr(>F)
Fert 1 0.042 0.042 1.3379 0.2503649
funktGr 3 249.764 83.255 2639.6236 < 2.2e-16 ***
CO2:Fert 2 0.030 0.015 0.4830 0.6184632
CO2:funktGr 6 1.415 0.236 7.4747 1.501e-06 ***
Fert:funktGr 3 0.696 0.232 7.3551 0.0001777 ***
funktGr:Species 12 16.813 1.401 44.4214 < 2.2e-16 ***
CO2:Fert:funktGr 6 0.480 0.080 2.5386 0.0254449 *
CO2:Fert:funktGr:Species 60 2.045 0.034 1.0809 0.3636501
Residuals 93 2.933 0.032
Questions:
- Can I nest the factor Species in funktGr the way I did? It doesn't
make sense to
analyse Species as a main factor in the whole analysis, I would need
to do four separate
analysis within a functional plant group, is that correct?
- Do I need to average over the four pseudoreplicates for each chamber
or is the
pseudoreplication taken out by the Error term?
- I often found in the R-helps that people use the lme functiona
instead of aov. Which
one is more appropriate?
- with the aov function can I also eliminate the non significant
factors to simplifiy the
model?
Thanks a lot for your answers
Christina
--
Christina Sch?del
Institute of Botany, University Basel
Sch?nbeinstrasse 6
CH-4056 Basel
ph. +41 61 267 35 06
fax +41 61 267 29 80
E-Mail C.Schaedel at unibas.ch
