On Tue, 29 Jun 2010, Julien Beguin wrote:
Thank you Roger for your fast response and Jenn for cc:ing your message
about autocov_dist in spdep. I am far to be a specialist in spatial
statistic and I know that autologistic models have been criticised in
inferential context (e.g. Dorman et al. ). Several authors (in ecology,
at least) also found it useful in prediction context (Betts et al.). I
guess that the debate might not be over...
My objective is not to trust results of autologistic model without
criticism but rather to compare them with other approaches (e.g. Moran
eigenvector, spatial regression, etc...).
Sorry for my ignorance but what means:
"general weights sum to zero". Is autocovariate term (not) calculated as
follows ? A = sum(wij*yj)/ sum(wij) where wij=1/euclidian distance
between i and j (for inverse distance). It is maybe a stupid question
but how could weights sum to zero?
No, it is A_i = sum_j(wij*yj)/ sum_j(wij)
So for some i, sum_j(wij) is zero, and A_1 = 0/0. If you are going to use
general weigts, they should not sum to zero, but for some i, it is
permitted. However, if you look at the code:
if (any(sapply(glist, function(x)
isTRUE(all.equal(sum(x), 0)))))
warning("zero sum general weights")
you will see that this is a warning, given properly when the condition is
met. The code is the documentation here.
Also, it is not false to say that I am asking to include a constant
vector of zeros, but only for points that have no neighbour in a single
region. Imagine a study area composed of 5 regions containing each N
presence/absence points with associated covariates. If I calculate the
autocovariate term for each region separately (with constant nbs) and
that for one region (not the others) the minimum distance between pair
of points < nbs value, it will crash for that region. It might be that
spatial autocorrelation vary from region to region, and that in a
specific region, A value based on a constant nbs among regions would be
zero (because of no neighbour) for that region.
What you said earlier was that you would encounter distance thresholds in
data subsets for which no observations would have any neighbours. I have
already explained that this is your problem.
Roger
A GLMM with fixed factors: Y~ covariates + Autocovariate; and random
factors = 1|Region/Autocovariate could account for that, no?
Thank you again for your time,
Julien
_______________________________________
De : Roger Bivand [roger.biv...@nhh.no]
Date d'envoi : 25 juin 2010 15:55
À : Julien Beguin
Cc : r-sig-geo@stat.math.ethz.ch
Objet : Re: [R-sig-Geo] question about autocov_dist function in "spdep" package
On Fri, 25 Jun 2010, Julien Beguin wrote:
Dear list member,
I am using the autocov_dist function in spdep on binary point data set
to estimate the autocovariate to be used in autologistic regression
(session info at the end):
coords<-as.matrix(cbind(datafile$X_COORD, datafile$Y_COORD))
ac1 <- autocov_dist(datafile$binary_variable, coords, nbs=400, type =
"inverse", zero.policy=TRUE)
It works fine with large value of nbs. When nbs value is below the neibhorhood
distance of some pairs of point I get warnings because of empty neighbors:
Warning messages: 1: In autocov_dist(datafile$binary_variable, coords,
nbs = 400, type = "inverse", :
With value 400 some points have no neighbours
2: In nb2listw(nb, glist = gl, style = style, zero.policy = zero.policy) :
zero sum general weights
It seems, but I might be wrong, that points with no neighbour have an
autocov_dist value of zero. Since it is what I want, it is ok for me.
No, the general weights sum to zero, as it says, you are using inverse.
But if I decrease nbs value below the smallest neighbourhood distance
among pairs of points in my data set, I get an error message:
Error in nb2listw(nb, glist = gl, style = style, zero.policy = zero.policy) :
No valid observations
In addition: Warning messages:
1: In autocov_dist(datafile$binary_variable, coords, nbs = 100, type =
"inverse", :
With value 100 some points have no neighbours
2: In nb2listw(nb, glist = gl, style = style, zero.policy = zero.policy) :
zero sum general weights
My question is as follows: why do autocov_dist function require that at
least one neighbor pair exists? and why not returning zero value for all
points when no any point has a neighbor?
This might appear to be a strange question but in my case I estimating
autocov_dist for different spatial block in which the minimum distance
between points vary from block to block, but still I would like a commun
nbs value for all block.
It does indeed seem very strange, as you are asking to include a constant
vector of zeros, which will alias the constant. The function itself is a
really bad idea, and is included only to show (see Dormann et al 2007)
that using it is inferior to all other methods examined there. Trying to
include a second zero constant doesn't seem well-founded, to put it
mildly.
Hope this helps,
Roger
Thank you for your help,
Julien Beguin
--------------------
Ph.D. student
Laval University
Québec, Canada
SESSION INFO:
R version 2.10.1 (2009-12-14)
i386-pc-mingw32
locale:
[1] LC_COLLATE=French_Canada.1252 LC_CTYPE=French_Canada.1252
[3] LC_MONETARY=French_Canada.1252 LC_NUMERIC=C
[5] LC_TIME=French_Canada.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] spdep_0.5-4 spam_0.20-3 coda_0.13-4
[4] deldir_0.0-12 maptools_0.7-29 foreign_0.8-40
[7] nlme_3.1-96 MASS_7.3-4 Matrix_0.999375-33
[10] lattice_0.17-26 boot_1.2-41 sp_0.9-60
[13] compositions_1.01-1 robustbase_0.5-0-1 tensorA_0.31
[16] rgl_0.91
loaded via a namespace (and not attached):
[1] grid_2.10.1 tools_2.10.1
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--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
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