Hello,

I think this is mostly a statistics question with possibly some R details.

Any feedback is appreciated.

I have several years of spatial biological sampling data in the same region

but the number and locations of sites vary across year. Very strong

spatial autocorrelation is present in the data.

I want to construct a regression model using Moran' eigenvectors as

explanatory variables to account for SAC. For example,

y_ijk=intercept+x1_ijk+x2_ijk+ EV_k

where x1,x2 are environmental covariates and EV are Moran eigenvectors; i,j

are location and k is year.

Environmental covariate relationships with response variable are assumed

constant across years.

My plan was to first estimate using all years of data:

y=intercept+x1+x2

then use function ME in spdep to find identify Moran eigenvectors to reduce

residual SAC using a year specific (index k) spatial weights and

year-specific residuals using function ME from spdep package:

EV_k= ME(residuals_k~1, listw=weights_k),

then linearly combine resulting eigenvectors for a given year into a single

vector and then concatenate each year's vector such that the final Moran

eignevector used in the regression is

EV= c(EV_2014,EV_2015,EV_2016)

and add EV as an offset or covariate as in the first equation shown.

This approach seems to work quite well (eliminates residual SAC, doesn't

shift regression coefficients substantially, improves model fit), I just

don't know if it is statistically sound?

thanks!

[[alternative HTML version deleted]]

_______________________________________________

R-sig-Geo mailing list

[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo