Hi all,
I am working with butterfly community data (rows=60 study sites, columns=120
species, cells=0 to 1500 individuals counted). I am using the metaMDS function
from the vegan package to do NMDS, and then using the envfit function from
vegan to link community structure to landscape covaria
You can also try using ifesle()
A$X2 <- ifelse(A$X2>1, 1, A$X2)
On Thu, Oct 25, 2012 at 2:08 PM, Peter Solymos wrote:
> Maybe:
> A$X2[A$X2>1] <- 1
>
> Peter
>
> --
> Péter Sólymos, Dept Biol Sci, Univ Alberta, T6G 2E9, Canada AB
> soly...@ualberta.ca, Ph 780.492.8534, http://psolymos.github.com
Hi Ellen,
Luc Anselin has written several nice guides for spatial statistics in GeoDa
and R. For starters, try searching for "luc anselin r spatial workbook
2007" and check out the 2007 version of the spatial regression analysis
workbook.
Best,
Tim
On Tue, Nov 6, 2012 at 4:24 AM, ellenpape w
Hi Jade,
You should look into the hier.part package. I know it works for glm but
you might try it for glm.nb.
Best,
Tim
On Fri, Nov 30, 2012 at 6:40 AM, Martin Weiser wrote:
> Jade Maggs pí¹e v Èt 01. 11. 2012 v 16:02 +0200:
> > Dear list,
> >
> > I have run a generalized linear model with ne
Hi Li,
I am sure there are many ways to do this in R. When I have done this type
of thing in the past, I have applied a predictive equation to each raster
cell using map algebra in ArcGIS (raster calculator tool). In R, there are
similar tools in the 'raster' package, apparently. A quick google
Hi Lizzie,
MuMIn is good for this. You can compare AICs of mixed-effects models,
where the random component related to repeated measures is held constant,
and fixed effects are evaluated using an all possible subsets approach.
Specifically, look at the dredge function for automated creation and
a
Here are some very helpful documents from Luc Anselin:
http://openloc.eu/cms/storage/openloc/workshops/UNITN/20110324-26/Basile/Anselin2007.pdf
http://labs.bio.unc.edu/Buckley/documents/AnselinIntroSpatRegres.pdf
https://geodacenter.asu.edu/system/files/rex1.pdf
On Tue, Jul 9, 2013 at 10:46 AM,
Hi Luis,
You can try this:
# enter data
spider <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5)
prey <-
c("cockroach","ant","termite","fly","isopod","cockroach","ant","termite","fly","isopod","cockroach","ant","termite","fly","isopod","cockroach","ant","termite","fly","isopod","cockroach",
If you want to do this in a glm framework, you might look into the mvabund
package:
http://cran.r-project.org/web/packages/mvabund/mvabund.pdf
I've never used it with anything approaching 1000 species, though.
On Tue, Feb 10, 2015 at 2:41 AM, Rajendra Mohan panda wrote:
> Dear All
>
> I have >
Hi Alexandre,
Not sure what the best solution is, but a few hacker ideas come to
mind. First,
you could create a spatially lagged variable from scratch. This would be
created by deciding on a neighborhood size, say first order neighbors, and
then creating a variable that was the average respons
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