Hi Thierry,
in geeglm() and in connection with the GEE theory, argument "id" should
identify the independent sample units in your data set, which seem to be
the plots. GEE will produce consistent estimates even if you misspecify
the correlation structure in your data (i.e., ignore nested design). If
you specify it correctly, you gain in efficiency. So, in your case you
may gain a little (or a lot) if you specify your own nested working
correlation matrix. In geeglm(), argument 'corstr' gives the option for
a user-defined correlation matrix, but I don't know how exactly this
needs to be defined.
Regarding the 'waves' argument and AFAIK, this used in connection with
the AR1 working correlation structure in order to identify the order and
spacing of the observations (i.e., if subject 1 has visits at time 1 and
time 2, whereas subject 2 has visits at time 1 and time 3).
A final thing: you said that you have some missing values. In this case
note that the GEE approach will be valid (i.e., produce unbiased
estimates) if the missing data mechanism is Missing Completely At
Random, that is when the reasons for missingness do not depend on Y.
I hope it helps.
Best,
Dimitris
ONKELINX, Thierry wrote:
Dear all,
Is it possible to incorporate a nested design in GEE? I have
measurements on trees that where measured in two years. The trees are
nested in plots. Each plot contains 24 trees. The number of plots is 72.
Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing,
so we end up wih 3431 data points.
This is what I have tried until now.
#assuming independence between trees and thus ignoring the plot level.
library(geepack)
geeglm(formula = Y ~ Year, id = TreeID, family = binomial, corstr =
"exchangeable")
#using waves. But I'm wondering if this is correct.
library(geepack)
geeglm(formula = Y ~ Year, id = PlotID, waves = TreeID, family =
binomial, corstr = "exchangeable")
#using a unstructured correlation on the plot level. geeglm with
unstructured correlation resulted in an out of memory error.
library(Zelig)
zelig(formula = Y ~ Year, model = "logit.gee", id = "PlotID", corstr =
"unstructured")
#Ideally I think I need a correlation matrix structured like below
(given for a plot with 3 trees). Here a1 is the correlation within a
tree, a2 the correlation between trees from the same plot and the same
year and a3 the correlation between trees from the same plot but a
different year. Does it make sense to run the model with a unstructured
correlation, calculate the average a1, a2 and a3 and use that as a fixed
working correlation?
matrix(c(
1, "a1", "a2", "a3", "a2", "a3",
"a1", 1, "a3", "a2", "a3", "a2",
"a2", "a3", 1, "a1", "a2", "a3",
"a3", "a2", "a1", 1, "a3", "a2",
"a2", "a3", "a2", "a3", 1, "a1",
"a3", "a2", "a3", "a2", "a1", 1
), nrow = 6)
Best regards,
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.onkel...@inbo.be
www.inbo.be
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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
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~ Roger Brinner
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ensure that a reasonable answer can be extracted from a given body of
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Dimitris Rizopoulos
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Erasmus University Medical Center
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