Chris - I don't think you are wrong at all -- you're right on the mark. I think this is the essential problem I am trying to solve, but the problem for me lies in your statement "a repeated measures model of some sort". But which sort? So far I have not been smart enough to figure out how to specify it properly. Maybe I am far off the mark with my current approach?
Matt >-----Original Message----- >From: Christian A. Parker [mailto:[EMAIL PROTECTED] >Sent: Thursday, May 22, 2008 10:39 AM >To: Landis, R Matthew >Cc: 'r-sig-ecology@r-project.org' >Subject: Re: [R-sig-eco] nlme model specification > >Matthew >Please correct me if I am wrong (anyone) but because your observations >are not independent across your desired groups (years) your error terms >will be biased which will then influence your significant tests. So >regardless of the factor that you are interested you would >still want to >account for the fact that all measurements were taken on the same trees >each year by doing a repeated measures model of some sort. >Hope this helps, >-Chris > >Landis, R Matthew wrote: >> Dear R-sig-eco: >> >> Many thanks to all of those who took the time to reply to my >question. The diversity of replies has made me go back and >try to clarify my question. Apologies for the length of the >e-mail. Thanks in advance to anyone willing to plow through >this and understand it. If you're ever in Middlebury I'll buy >you a beer. >> >> To repeat, I have 300 trees, ranging in size from 10 - 150 >cm diameter (big trees). To simplify my original question, >let's say I want to understand the relationship between growth >and two variables, diameter (continuous) and vine load >(ordinal index from 1-4). I'd also like to know the relative >importance of diameter vs. vine load, e.g. by partial R2. If >I had one year of data, this would be a simple regression. >> >> However, I have 9 years of annual measurements on the trees. > It's as if I have the above analysis repeated 9 times. There >was no initial treatment, so I view these 9 years as a random >sample of the years in the life of the tree, and unlike most >examples of repeated measures I have read, the time effect is >of no interest whatsoever. That is, I am not interested in >viewing xyplot(growth ~ time|id). I don't expect to see any >consistent directional response to time. In a way, it's as if >the 9 years represent blocks, (except that it's the same 300 >trees in each block) -- this is why I view the yr as a random >effect, and as the grouping variable. >> >> If I were to graph the data, I would use xyplot(growth ~ >diameter|yr) to see what I am most interested in. Grouping by >individual doesn't make sense to me here because each >individual only represents a very small slice of the full >range of measurements - e.g. over the ten years, each tree >only grows from 10 cm - 14 cm, so I can't really estimate the >growth vs. diameter relationship for each tree. xyplot(growth >~ diameter|id) would not be useful. This is why I don't >consider the individual to be the grouping variable, but >perhaps I am wrong on this. >> >> So, now, as before, I am back to >> >> fit <- lme(fixed = growth ~ diameter * vines, random = ~ 1|year) >> >> I'm expecting that this will estimate separate intercepts >for each year. Which is what I want (I would like to fit >separate slopes by year too, but that model didn't converge). >> >> I guess what I'm most concerned about is whether the >significance tests obtained for each term use the appropriate >error term and the appropriate degrees of freedom. I'm >currently using something like the following command to test >the effect of diameter >> >> anova(fit.full.model, update(fit.full.model, . ~ vines)) >> >> But maybe I'm way off base there. >> >> Thanks very much! >> >> Matt Landis >> >> >>> -----Original Message----- >>> From: [EMAIL PROTECTED] >>> [mailto:[EMAIL PROTECTED] On Behalf Of >>> Landis, R Matthew >>> Sent: Wednesday, May 21, 2008 1:55 PM >>> To: 'r-sig-ecology@r-project.org' >>> Subject: [R-sig-eco] nlme model specification >>> >>> Greetings R-eco folks, >>> >>> I'm trying to analyze a dataset on tree growth rates to see >>> which factors are important (and their relative importance >>> too, if I can get that), and I'm having some trouble figuring >>> out how to specify the model, despite having carefully read >>> Pinheiro and Bates, the help files for nlme, Crawley's book on >>> Statistics with S, MASS, and other books besides. >>> >>> The dataset consists of ~ 300 trees measured annually for 10 >>> years. So, I have 9 pseudo-replicated intervals over which to >>> assess growth (about 2700 rows in the dataset). There are 5 >>> different explanatory factors, which are a combination of >>> continuous variables and categorical factors. Some of these >>> vary with time. In the end, I would like to get both >>> coefficient estimates and partial R2 (or some other way of >>> ranking them) for each factor. Unlike most time-series >>> examples in the books, I am not interested in how growth >>> varies with time, nor am I particular interested in >>> interactions of explanatory factors with time. >>> >>> Based on this, I've convinced myself that I should specify the >>> model as: >>> >>> fit <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random >>> = ~1|year, method = 'ML') >>> >>> Year is clearly a random effect, and is the grouping variable >>> for the analysis. Each of the other coefficients is "inner" >>> to this variable. I'm ignoring individual tree as a grouping >>> factor, since I don't want to estimate separate coefficients >>> for each tree. Does this sound like the correct way to do this? >>> >>> Thanks for any help. Apologies if this is more of a >>> statistics question and less of an R question. >>> >>> Matt Landis >>> >>> **************************************************** >>> R. Matthew Landis, Ph.D. >>> Dept. Biology >>> Middlebury College >>> Middlebury, VT 05753 >>> >>> tel.: 802.443.3484 >>> ************************************************** >>> >>> >>> [[alternative HTML version deleted]] >>> >>> _______________________________________________ >>> R-sig-ecology mailing list >>> R-sig-ecology@r-project.org >>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >>> >>> >> >> _______________________________________________ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> >> > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
