On Oct 1, 2012, at 12:59 AM, Dan Bebber wrote: > Thanks, but the problem is quite specific and not addressed on the Spatial > Data taskview page. > Quite specifically, I would like to know how to edit corSpatial functions to > calculate great circle distances. > The Bayesian equivalent, georamps in the ramps package, is able to do this, > therefore I imagine it must be possible for nlme. >
Can't you use the corStruct functions in pkg::ramps? They allow specification of the 'haversine' metric. The corR* functions inherit from class corStruct. -- David. > Dan > ________________________________________ > From: David Winsemius [dwinsem...@comcast.net] > Sent: 01 October 2012 08:38 > To: Dan Bebber > Cc: r-help@r-project.org > Subject: Re: [R] nlme: spatial autocorrelation on a sphere > > On Sep 30, 2012, at 6:48 PM, Dan Bebber wrote: > >> I have spatial data on a sphere (the Earth) for which I would like to run an >> gls model assuming that the errors are autcorrelated, i.e. including a >> corSpatial correlation in the model specification. >> >> In this case the distance metric should be calculated on the sphere, >> therefore metric = "euclidean" in (for example) corSpher would be incorrect. >> >> I would be grateful for help on how to write a new distance metric for the >> corSpatial function. >> I believe there are several ways that distances on a sphere can be >> calculated in R, for example the "distMeeus" function in the geosphere >> library. However, I have no idea how to write this into a corSpatial >> function. >> >> The aim is to end up with a metric = "sphere" option that calculates great >> circle distances between points using latitude and longitude. > > LMCTVTFY: http://cran.r-project.org/web/views/Spatial.html > > -- > > David Winsemius, MD > Alameda, CA, USA > > David Winsemius, MD Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
