-geo@r-project.org
Oggetto: Re: [R-sig-Geo] spatial regression model
You can evaluate the proportion of the variance explained by spatial
structure by log-likelihood test- between spatial model and a model
with intercept:
suppose:
library(lnlme)
M1-lme(response~ predictots+, method=REML
Paolo,
Using GLS seems a good approach for what you want to do.
However, as its names indicates GLS models does not use the OLS approach,
so you can't use the classic R square interpretation.
I would follow these steps (but if I am wrong please feel free to correct
me !!).
1) Run your model in
Dear community
I write to pose a question about the best way to incorporate spatial non
independence in a regression model that has multivariate responses and multiple
predictors. I would like to estimate the global R-sq under OLS and its
significance (no problem for that..) and compare it
