Hi Tim, I don't believe there is a satisfactory solution in R - at least yet - for non-normal models. Ultimately, this should be possible using lmer() but not in the near-term. One possibility is to use glmPQL as described in:
Dormann, F. C., McPherson, J. M., Araújo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., Kissling, W. D., Kühn, I., Ohlemüller, R., Peres-Neto, P. R., Reineking, B., Schröder, B., Schurr, F. M. and Wilson, R. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. – Ecography 30: 609–628. However, note the caution: "This is an inofficial abuse of a Generalized Linear Mixed Model function (glmmPQL {MASS}), which is a wrapper function for lme {nlme}, which in turn internally calls gls {nlme}." If all you need are parameter estimates, fine. If you want to do model comparison, though, no luck. Manuel On Mon, 2009-08-24 at 12:10 -0700, timothy_hand...@nps.gov wrote: > Bert - > > I took a look at that page just now, and I'd classify my problem as > spatial regression. Unfortunately, I don't think the spdep library fits my > needs. Or at least, I can't figure out how to use it for this problem. The > examples I have seen all use spdep with networks. They build a graph, > connecting each location to something like the nearest N neighbors, attach > some set of weights, and then do an analysis. The plots in my data have a > very irregular, semi-random, yet somewhat clumped (several isolated > islands), spatial distribution. Honestly, it's quite weird looking. I don't > know how to cleanly turn this into a network, and even if I did, I don't > know that I ought to. To me (and please feel free to disagree) it seems > more natural to use a matrix of distances and associated correlations, > which is what the gls function appears to do. > > In the ecological analysis section, it looks like both 'ade4' and 'vegan' > may have helpful tools. I'll explore that some more. However, I still think > that one of lme or gls already has the functionality I need, and I just > need to learn how to use them properly. > > Tim Handley > Fire Effects Monitor > Santa Monica Mountains National Recreation Area > 401 W. Hillcrest Dr. > Thousand Oaks, CA 91360 > 805-370-2347 > > > > Bert Gunter > <gunter.ber...@ge > ne.com> To > <timothy_hand...@nps.gov>, > 08/24/2009 11:43 <r-help@r-project.org> > AM cc > > Subject > RE: [R] lme, lmer, gls, and spatial > autocorrelation > > > > > > > > > > > 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. -- http://mutualism.williams.edu ______________________________________________ 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.