[R] lme, lmer, gls, and spatial autocorrelation
Timothy_Handley at nps.gov
Timothy_Handley at nps.gov
Mon Aug 24 21:10:59 CEST 2009
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.berton at ge
ne.com> To
<Timothy_Handley at nps.gov>,
08/24/2009 11:43 <r-help at 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-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Timothy_Handley at nps.gov
Sent: Monday, August 24, 2009 11:12 AM
To: r-help at 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
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