[R-sig-ME] Using corSpher to correct for spatial autocorrelation

Thierry Onkelinx thierry@onkelinx @ending from inbo@be
Fri Jun 22 09:12:14 CEST 2018

Dear Julie,

corSpher() is a spherical variogram / correlogram model. It defines a
specific shape of the variogram, not the kind of data. All variogram
models in nlme assume Euclidean distances, so you will need projected
data. But that opens another can of worm when your data spans a
considerable part of the globe.

This paper might be relevant for you:

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

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able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
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not ensure that a reasonable answer can be extracted from a given body
of data. ~ John Tukey

2018-06-22 2:12 GMT+02:00 Julie Lee-Yaw via R-sig-mixed-models
<r-sig-mixed-models using r-project.org>:
> Hi
> I want to use the the correlation setting with corSpher in nlme to account for potential spatial autocorrelation in my data. My data include observations from across the globe with locations in latitude and longitude (decimal degrees). From the R help for corSpher, the example syntax would be something like:
> fm1Wheat2 <- gls(yield ~ variety - 1, corr =corSpher(c(28, 0.2), form = ~ latitude + longitude, nugget = TRUE))
> My question is whether the latitude and longitude provided should be projected into a spatial projection that preserves distances or areas or whether providing decimal degrees is appropriate?
> Many thanks!
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