[R-sig-Geo] specifying correlation structure in (n)lme

Gregoire, Timothy timothy.gregoire at yale.edu
Fri Jun 14 03:20:43 CEST 2013


Look at the Pinheiro & Bates book on Mixed Models, it is part of the Springer series and is prob'ly an online resource thru the Harvard library system.

An alternative, outside the mixed models construct, is to use the likfit() function of the geoR package by Diggle & Ribeiro.


Timothy G. Gregoire
J. P. Weyerhaeuser Professor of Forest Management
School of Forestry & Environmental Studies, Yale University
360 Prospect Street, New Haven, CT  06511-2104  U.S.A.

office: 1.203.432.9398  mobile: 1.203.508.4014, fax: 1.203.432.3809
timothy.gregoire at yale.edu
G&V sampling text: http://crcpress.com/product/isbn/9781584883708

-----Original Message-----
From: r-sig-geo-bounces at r-project.org [mailto:r-sig-geo-bounces at r-project.org] On Behalf Of Gabriel Gartner
Sent: Thursday, June 13, 2013 8:43 PM
To: r-sig-geo at r-project.org
Subject: [R-sig-Geo] specifying correlation structure in (n)lme

Hi All,

I have what I imagine is a fairly basic question, but I am a relative beginner at using spatial data in my analyses.  I am trying to address spatial autocorrelation in my data which examines lizard morphology across a broad geographic range.  My spatial data is in the form of decimal long/lat and I am trying to select which variogram model to use.

My question is, what is the form of the correlation structure within each of the corClasses for decimal lat/long data.  In other words, what goes after "form" in the following example script:

exp.cybotes<-update(null.model, correlation =corExp(1, form = ???), method= "ML")

I apologize for the basic question but I am stuck!


Gabriel E.A. Gartner
Postdoctoral Fellow
Museum of Comparative Zoology 
Harvard University
26 Oxford St.
Cambridge, MA 02138
Office: 617-384-8437

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