Re: [R] lme, lmer, gls, and spatial autocorrelation
Manuel,
Thanks for the reference. I printed it out and read through it this
morning.
I think I'm going to take a gls approach. I've spent the last couple weeks
reading about spatial autocorrelation, and found that the world of SAC is
large, complex, and requires more time than I currently have. Using gls
seems a reasonable compromise between statistical rigour, and the
unfortunate but real constraint of my limited time to work on this project.
According to Dorman et al, in their (admittedly limited) tests, GLS worked
reasonably well with Poisson distributed synthetic data.
Also, I've come to think that the ability to do model comparison would be
useful. While I would like to be able to confidently choose a model for
spatial autocorrelation a priori, based on biological knowledge, I don't
have enough information to do this. Even after some data exploration, using
variograms and plots of Moran's I, it still seems like there's insufficient
information. Using a fitness score such as AIC, I could compare a small
number of reasonable models to find the most appropriate error structure.
Additionally, I could compare the SAC-informed and SAC-ignorant models to
get a holistic assessment of the importance of SAC in my data.
Tim Handley
Fire Effects Monitor
Santa Monica Mountains National Recreation Area
401 W. Hillcrest Dr.
Thousand Oaks, CA 91360
805-370-2347
Manuel Morales
Manuel.A.Morales
@williams.edu To
timothy_hand...@nps.gov
08/24/2009 05:31 cc
PMBert Gunter
gunter.ber...@gene.com,
r-help@r-project.org
Subject
Re: [R] lme, lmer, gls, and spatial
autocorrelation
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
Re: [R] lme, lmer, gls, and spatial autocorrelation
Ben Bolker wrote: My two cents: this is a hard problem to do, period (not just in R). I would second the recommendation of the Dormann et al paper listed below; also see Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Saveliev, and Graham M. Smith. Mixed Effects Models and Extensions in Ecology with R. 1st ed. Springer, 2009. Thanks for the ref..:-) The last chapter in the book discusses Poisson + auto-correlation with MCMC. It shouldn't be too difficult to replace the AR-1 by some of these spatial correlation structures...I guess...I hope. - Dr. Alain F. Zuur First author of: 1. Analysing Ecological Data (2007). Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p. 2. Mixed effects models and extensions in ecology with R. (2009). Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. 3. A Beginner's Guide to R (2009). Zuur, AF, Ieno, EN, Meesters, EHWG. Springer Statistical consultancy, courses, data analysis and software Highland Statistics Ltd. 6 Laverock road UK - AB41 6FN Newburgh Email: highs...@highstat.com URL: www.highstat.com -- View this message in context: http://www.nabble.com/lme%2C-lmer%2C-gls%2C-and-spatial-autocorrelation-tp25120963p25142366.html Sent from the R help mailing list archive at Nabble.com. __ 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] 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.
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.
Re: [R] lme, lmer, gls, and spatial autocorrelation
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.comTo 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
Re: [R] lme, lmer, gls, and spatial autocorrelation
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.comTo
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
Re: [R] lme, lmer, gls, and spatial autocorrelation
My two cents: this is a hard problem to do, period (not just in R).
I would second the recommendation of the Dormann et al paper listed
below; also see Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A.
Saveliev, and Graham M. Smith. Mixed Effects Models and Extensions in
Ecology with R. 1st ed. Springer, 2009.
Dormann et al mention WinBUGS for Bayesian approaches; this can also be
done in principle with the geoRglm package, although I have to admit that I
haven't actually tried it it looks a bit challenging. Maybe also doable
with
AD Model Builder.
How comfortable are you with the Poisson assumption for this data set anyway
... ?
Maybe just use gls with appropriate scaling of the variance?
Manuel Morales wrote:
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
