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
_________________________________
Erika Mudrak
Postdoctoral Researcher
Ecology, Evolution, and Organismal Biology
Bessey Hall 253
Iowa State University
mudrak.public.iastate.edu
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