[R-sig-eco] nlme model specification

Thu May 22 17:14:35 CEST 2008

Matthew,

I support Chris' point of view. The random effects should model the
dependence structure between the measurements. The parameters of your
fixed effect will be better when you have specified the random effect
correctly. Define the most complex model that are willing to accept, run
it with different random effects and compare those models (by LRT or
AIC, both under REML). Take the best one and refine your fixed effects
(under ML).

A few suggestions:

M0 <- gls(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, method = 'REML')
Ma <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random = ~1|year,
method = 'REML')
M1 <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random = ~1|id,
method = 'REML')
M2 <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random = ~year|id,
method = 'REML')
M3 <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random =
~factor(year)|id, method = 'REML')
M4 <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random = list(id =
pdCompSymm(~factor(year))), method = 'REML')

anova(M0, Ma)
anova(M0, M1, M2)
anova(M0, M1, M3)
anova(M0, M1, M4)

Warning: M3 may take some time to calculate as it will have estimate 45
parameters for the random effect.

A random slope by year when you group by year makes no sense since you
have in each group only data from one year. Hence it is impossible to
have a slope by year in each group.

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-ecology-bounces at r-project.org
[mailto:r-sig-ecology-bounces at r-project.org] Namens Christian A. Parker
Verzonden: donderdag 22 mei 2008 16:39
Aan: Landis, R Matthew
CC: 'r-sig-ecology at r-project.org'
Onderwerp: 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
>> **************************************************
>>
>>
>>        [[alternative HTML version deleted]]
>>
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