[R] Help! Can a nested fixed effects model with autocorrelation be possible?
drj|m|emon @end|ng |rom gm@||@com
Wed Mar 4 22:00:47 CET 2020
Try posting your question to the R-SIG-mixed-models list. You will
probably get an expert answer there.
On Thu, Mar 5, 2020 at 5:30 AM Candela Madaschi
<candelamadaschi using gmail.com> wrote:
> 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
> 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
> 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]]
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