Hello,
I would appreciate if somebody could help me clear my mind about the below issues.
I have a factorial experiment to study the effects of Grazing and Fire on Forest biomass production. The experimental unit (to which the treatment combinations are applied) are PLOTs. The measures were made repeatedly for 13 years.
I am planning to use the linear mixed effect model function lme in R for this. I know that in software like SPSS, using Repeated Measure analysis of variance for studies like mine, sometimes (case of non-sphericity), one needs to adjust for the degree of freedom (DF) used to test the significance of the "within subject factor" (Time i.e., Year in my case).
My question is:
How does this work with lme in R? Isn't it enough for me to specify in my model, Year × plotID as a random factor to account for the temporal autocorrelation? Or what else should I do to ensure that I have correct results from the summary function applied to my model (correct t- and p-values)? Thanks in advance.

With regards,

Sidzabda Djibril Dayamba,
Swedish University of Agricultural Sciences
Faculty of Forest SCience
Southern Swedish Forest Research Centre
Tropical Silviculture and Seed Laboratory
PO Box 101
SE - 230 53 Alnarp,
Sweden
Tel: +46 76 83 515 70 (Mobile)
        +46 40 41 53 95 (Office)


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