Doing repeated measures on a randomized block design
Dear Richard, your question could be handled using a linear model incorporating a temporal autcorrelation structure within trees. However, I don't think using "tree" as random factor (e.g. in lme()) would be very helpful here because random factors assume a compound symmetry autocorrelation structure (same correlation for any temporal distance), which is probably overly simplistic for long time series. Instead, you could use Generalized Least Squares, gls() in R, which is a standard choice in such cases. For instance: gls(FvFm ~ Exposure, correlation = corAR1(form = ~time|Tree), data = perm.fvfm) This will fit a model assuming a first-order autoregressive correlation structure, i.e. residual autocorrelation should decrease as the temporal distance between them increases. Notice that "time" should be the temporal order of observations within trees, so you will have to convert your "Date" to this format first. For other correlation structures, relevant R functions and examples similar to yours, see Zuur et al. (2009), "Mixed effects models and extensions in ecology with R". Best wishes, Pedro Em sex, 14 de jun de 2019 ?s 14:42, Richard Boyce <boycer at nku.edu> escreveu:
I?m measuring chlorophyll fluorescence (FvFm), my measured variable, on N and S exposures (treatment variable) of 4 red cedar trees. Here?s what the beginning of the data file looks like: head(perm.fvfm). Tree Exposure Date FvFm 1 1 S 13.Feb 0.775 2 1 N 13.Feb 0.795 3 2 S 13.Feb 0.737 4 2 N 13.Feb 0.759 5 3 S 13.Feb 0.615 6 3 N 13.Feb 0.712 If I were just doing this one time, this would be a randomized block design, where trees were the blocks (random variable) and exposure was the treatment variable (fixed variable). Actually, since there are only two treatment levels, it would be a paired t-test. However, I?ve repeated this on many dates (18 so far this year). So this also requires a repeated-measures design, with trees as subjects. Repeated-measures, however, usually have time (date) as a within-subject variable and then some other treatment that is a between-subjects variable. I don?t have have a between-subjects variable, however, as all subjects (trees) get both levels of exposure and all levels of time (date). I?ve searched the web, but there is not a lot out there for this kind of design. It looks like lm, lme, lmer, and permuco in R might all work, but advice for how to set up the Error() or random variable designations are confusing and sometimes contradictory. Any advice would be much appreciated! Thanks, Rick Boyce
_______________________________________________ R-sig-ecology mailing list R-sig-ecology at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology