[R-sig-eco] SEM with time series?

Jarrett Byrnes byrnes at nceas.ucsb.edu
Mon Nov 29 20:10:02 CET 2010

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.
  -Jarrett Byrnes

Jarrett E. Byrnes
Postdoctoral Fellow
National Center for Ecological Analysis and Synthesis
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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