Have you looked at the "Spatial" task view on CRAN? That would seem to me the logical first place to go.
Bert Gunter Genentech Nonclinical Biostatisics -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of timothy_hand...@nps.gov Sent: Monday, August 24, 2009 11:12 AM To: r-help@r-project.org Subject: [R] lme, lmer, gls, and spatial autocorrelation Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is that I'd like to draw a straight line through my data, accounting for spatial autocorrelation and using Poisson errors (I have count data). There's a longer explanation at the end of this e-mail, I just didn't want to overdo it at the start. There are three R functions that do at least some of what I would like, but I'm unclear on some of their specifics. 1. lme - Maybe models spatial autocorrelation, but doesn't allow for Poisson errors. I get mixed messages from The R Book. On p. 647, there's an example that uses lme with temporal autocorrelation, so it seems that you can specify a correlation structure. On the other hand, on p.778, The R Book says, "the great advantage of the gls function is that the errors are allowed to be correlated". This suggests that only gls (not lme or lmer) allows specification of a corStruct class. Though it may also suggest that I have an incomplete understanding of these functions. 2. lmer - Allows specification of a Poisson error structure. However, it seems that lmer does not yet handle correlated errors. 3. gls - Surely works with spatial autocorrelation, but doesn't allow for Poisson errors. Does allow the spatial autocorrelation to be assessed independently for different groups (I have two groups, one at each of two different spatial scales). Since gls is what The R Book uses in the example of spatial autocorrelation, this seems like the best option. I'd rather have Poisson errors, but Gaussian would be OK. However, I'm still somewhat confused by these three functions. In particular, I'm unclear on the difference between lme and gls. I'd feel more confident in my results if I had a better understanding of these choices. I'd greatly appreciate advice on the matter More detailed explanation of the data/problem is below: The data: [1] A count of the number of plant species present on each of 96 plots that are 1m^2 in area. [2] A count of the number of plant species present on each of 24 plots that are 100m^2 in area. [3] X,Y coordinates for the centroid of all plots (both sizes). Goal: 1. A best fit straight-line relating log10(area) to #species. 2. The slope of that line, and the standard error of that slope. (I want to compare the slope of this line with the slope of another line) The problem: Spatial autocorrelation. Across our range of plot-separation-distances, Moran's I ranges from -.5 to +.25. Depending on the size of the distance-bins, about 1 out of 10 of these I values are statistically significant. Thus, there seems to be a significant degree of spatial autocorrelation. if I want 'good' values for my line parameters, I need to account for this somehow. Tim Handley Fire Effects Monitor Santa Monica Mountains National Recreation Area 401 W. Hillcrest Dr. Thousand Oaks, CA 91360 805-370-2347 ______________________________________________ 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. ______________________________________________ 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.
