Hi everyone!
I'm a little bit lost as to which statistical model I should use.
I'm evaluating the decomposition of two plant species (factor: "species")
under two treatments: "temperature" (with 2 levels) and "water level" (3
levels). These two treatments are completely crossed. I have 5 replicates
per plant species for each time period (6 dates).
So I have 6 dates x 2 species x 2 temperatures x 3 water levels x 5
replicates: 360 obs. (180 obs. per species).
The comparison between plant species isn't relevant in my study, rather I
want to test the significance of the treatments within the plant species.
So I think that a nested model could fit my data. Could this be possible?
On the other hand, I've been reading that there could be temporal
correlation problems in my data, and that this can be addressed with nlme
package. But as I understand you can't fit a nested fixed effects model
using lme. My design doesn't have random effects, they are all fixed
factors, and, from what I understand, time can't be used as a random effect
because it is not a categorical variable. Furthermore, "species" can't be a
random factor because it only has two levels. In summary, I'm completely
lost.
My questions are:
Can I use a nested model for the structure of my data?
Is there such a thing as a nested fixed effects model with autocorrelation
approach?
Can I use lme or lme4 if that model exists?
Could I transform time variable to a categorical factor and use it as a
random factor nested in species?
Is there a simple way to analyse this data set?
Thank you!!
(I'm very sorry for my english, it is not my native language)
--
Candela Madaschi
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
