[R-sig-eco] nlme model specification

Thu May 22 16:44:51 CEST 2008

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:cparker at pdx.edu]
>Sent: Thursday, May 22, 2008 10:39 AM
>To: Landis, R Matthew
>Cc: 'r-sig-ecology at 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: r-sig-ecology-bounces at r-project.org
>>> [mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of
>>> Landis, R Matthew
>>> Sent: Wednesday, May 21, 2008 1:55 PM
>>> To: 'r-sig-ecology at 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
>>> **************************************************
>>>
>>>
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>>>
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>>
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