Spatial correlogram - Problems with creating neigbourhood list and weights
On Tue, 5 Jan 2010, Strubbe Diederik wrote:
Dear all,
I have a dataset with several lat-long coordinates (48) and corresponding residuals values (not a grid). Using the spdep package, I try to create a correlogram of these residuals according to several lags (i.e. distance classes) but I can?t seem to get the neighborhood and weights right?
Below, I show the main parts of the code and the results:
rnp <- read.table ("D:/Diederik/RESID_WITH_DISTANCE.csv",sep=",",header=T)
resids <- rnp$RESID
##create neighbourhood list and make symmetric##
xy <- cbind(rnp$X, rnp$Y)
rnp_nb <-knn2nb(knearneigh(xy, longlat=TRUE))
rnp_nb <-make.sym.nb(rnp_nb)
rnp_nb
You have chosen a k=1 first nearest neighbour scheme, was this intended?
Neighbour list object:
Number of regions: 48
Number of nonzero links: 66
Percentage nonzero weights: 2.864583
Average number of links: 1.375
##create weights##
rnp_w <- nb2listw(rnp_nb,glist=NULL,style="W",zero.policy=FALSE)
rnp_w
Characteristics of weights list object:
Neighbour list object:
Number of regions: 48
Number of nonzero links: 66
Percentage nonzero weights: 2.864583
Average number of links: 1.375
Weights style: W
Weights constants summary:
n nn S0 S1 S2
W 48 2304 48 73.72222 209.3333
## Moran's I test for spatial autocorrelation##
moran.test(resids, rnp_w, randomisation=TRUE, ?)
Moran's I test under randomisation
data: resids
weights: rnp_w
Moran I statistic standard deviate = -0.3353, p-value = 0.6313
alternative hypothesis: greater
sample estimates:
Moran I statistic Expectation Variance
-0.08060979 -0.02127660 0.03130522
Note that Moran's I for this symmetrized first nearest neighbour scheme is not significant.
##make a correlogram## sp.correlogram(rnp_nb, resids, order=16, method="I", zero.policy=TRUE) Error in nb2listw(nblags[[i]], style = style, zero.policy = zero.policy) : No valid observations
This is most likely a result of your choice of symmtrized first nearest neighbours, leading to many graph components and/or the unrealistically high order=: library(spdep) data(columbus) nbk1 <- knn2nb(knearneigh(cbind(columbus$X, columbus$Y))) snbk1 <- make.sym.nb(nbk1) n.comp.nb(snbk1)$nc plot(snbk1, cbind(columbus$X, columbus$Y)) moran.test(columbus$CRIME, nb2listw(snbk1)) sp.correlogram(snbk1, columbus$CRIME, order=6, method="I", zero.policy=TRUE) sp.correlogram(snbk1, columbus$CRIME, order=7, method="I", zero.policy=TRUE) Try reducing order= to something realistic, but also consider a denser, more connected, representation of neighbours. Hope this helps, Roger
In addition: Warning message: In moran.test(var, listw, randomisation = randomisation, zero.policy = zero.policy) : Out-of-range p-value: reconsider test arguments Moran.test seems to work fine, but the sp.correlogram doesn?t? Do I ? in any way- have to attach the weigths (rnp_w) to the neighourhood list (rnp_nb)? Many thanks!! Diederik PS: R-version 2.9.2, Package spdep version 0.4-56, Windows Vista Home Premium Service Pack 2 on Acer Extensa 5630 Dr. Diederik Strubbe Evolutionary Ecology Group Department of Biology University of Antwerp Groenenborgerlaan 171 2020 Antwerpen, Belgium tel: +32 3 265 3464 [[alternative HTML version deleted]]
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.Bivand at nhh.no