[R] Help! Can a nested fixed effects model with autocorrelation be possible?

Bert Gunter bgunter@4567 @end|ng |rom gm@||@com
Wed Mar 4 20:27:52 CET 2020

Your English is excellent, but your statistics is bad.

This list is for questions about R programming, not statistics, although
sometimes the two topics do intersect. If you wish to seek online help of
this sort, the  special R Help list r-sig-mixed-models may be where you
should post, as it appears that mixed effects models might be useful for
you. Another specific statistical help site is stats.stackexchange.com .

However, I think you would do much better to consult with a local
statistical expert, as your statistical background seems minimal. But
that's just my opinion.

Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )

On Wed, Mar 4, 2020 at 10:30 AM Candela Madaschi <candelamadaschi using gmail.com>

> 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
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