One could also setup an SEM with autoregressive paths specified. The missing
data issue is something separate, however. There are ways in SEM of dealing
with it - Full Information Maximum Likelihood and the like. I'd recommend
taking a look at the lavaan package in R.
http://www.lavaan.org.
-Jarrett Byrnes
-----------------------------------------
Jarrett E. Byrnes
Postdoctoral Fellow
National Center for Ecological Analysis and Synthesis
http://nceas.ucsb.edu/~byrnes
ph: 805.892.2512
On 11/29/10 8:22 AM, Ben Bolker wrote:
On 11/29/2010 11:05 AM, Mudrak, Erika [EEOBS] wrote:
I am helping a colleague with stats analysis, and though it's a
seemingly simple setup, it's becoming quite complicated! The system
is a deciduous forest with treefall gaps of different carefully
chosen sizes. The response variable is amount of NH3 found in the
rainwater collected under each gap, sampled once a month during the
growing season. Explanatory variables includes gap size (main
variable of interest), soil temperature, soil moisture, microbial
biomass, etc.... They are all continuous variables, so we would
like to do a regression context. We expect the response variable to
be autocorrelated over time, so that leads us to want to do a
time-series regression. But the other explanatory variables may also
be correlated with each other and autocorrelated across time.
There are also lots of instances of missing data, for example when no
rainfall occurred, there was no opportunity to measure the chemical
composition of it. Is there a way to do structural equation modeling
(to account for correlation between explanatory variables) with a
time series component (to account for autocorrelation of explanatory
variables)? Or is there another more appropriate technique? Thank
you, Erika Mudrak
My guess (not having done much of this stuff myself) is that a full
Bayesian setup (WinBUGS etc.) would be the simplest (!!) way to handle
this kind of problem. Of course, there's a lot of conceptual and
programming overhead in learning to set it up ... if you want to go this
route and you are new to Bayesian stats and WinBUGS I would suggest
McCarthy's book for basics and one or more of (1) Clark [comprehensive
and oriented toward ecology but dense in places] (2) Gelman and Hill
[extremely clear treatment of multi-level modeling in general] or (3) my
book [not as specific to Bayes/WinBUGS, but long on general explanation]
for tackling your real problem.
good luck ...
Ben Bolker
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