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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