nb2listw
Spatial weights for neighbours lists
The nb2listw
function supplements a neighbours list with spatial weights for the chosen coding scheme. The can.be.simmed
helper function checks whether a spatial weights object is similar to symmetric and can be so transformed to yield real eigenvalues or for Cholesky decomposition.
 Keywords
 spatial
Usage
nb2listw(neighbours, glist=NULL, style="W", zero.policy=NULL)
can.be.simmed(listw)
Arguments
 neighbours
 an object of class
nb
 glist
 list of general weights corresponding to neighbours
 style
style
can take values “W”, “B”, “C”, “U”, “minmax” and “S” zero.policy
 default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zerolength weights vectors
 listw
 a spatial weights object
Details
Starting from a binary neighbours list, in which regions are either listed as neighbours or are absent (thus not in the set of neighbours for some definition), the function adds a weights list with values given by the coding scheme style chosen. B is the basic binary coding, W is row standardised (sums over all links to n), C is globally standardised (sums over all links to n), U is equal to C divided by the number of neighbours (sums over all links to unity), while S is the variancestabilizing coding scheme proposed by Tiefelsdorf et al. 1999, p. 167168 (sums over all links to n).
If zero policy is set to TRUE, weights vectors of zero length are inserted for regions without neighbour in the neighbours list. These will in turn generate lag values of zero, equivalent to the sum of products of the zero row t(rep(0, length=length(neighbours))) %*% x
, for arbitraty numerical vector x
of length length(neighbours)
. The spatially lagged value of x for the zeroneighbour region will then be zero, which may (or may not) be a sensible choice.
If the sum of the glist vector for one or more observations is zero, a warning message is issued. The consequence for later operations will be the same as if noneighbour observations were present and the zero.policy argument set to true.
The “minmax” style is based on Kelejian and Prucha (2010), and divides the weights by the minimum of the maximum row sums and maximum column sums of the input weights. It is similar to the C and U styles; it is also available in Stata.
Value

A
listw
object with the following members:References
Tiefelsdorf, M., Griffith, D. A., Boots, B. 1999 A variancestabilizing coding scheme for spatial link matrices, Environment and Planning A, 31, pp. 165180; Kelejian, H. H., and I. R. Prucha. 2010. Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Econometrics, 157: pp. 5367.
See Also
Examples
example(columbus)
coords < coordinates(columbus)
cards < card(col.gal.nb)
col.w < nb2listw(col.gal.nb)
plot(cards, unlist(lapply(col.w$weights, sum)),xlim=c(0,10),
ylim=c(0,10), xlab="number of links", ylab="row sums of weights")
col.b < nb2listw(col.gal.nb, style="B")
points(cards, unlist(lapply(col.b$weights, sum)), col="red")
col.c < nb2listw(col.gal.nb, style="C")
points(cards, unlist(lapply(col.c$weights, sum)), col="green")
col.u < nb2listw(col.gal.nb, style="U")
points(cards, unlist(lapply(col.u$weights, sum)), col="orange")
col.s < nb2listw(col.gal.nb, style="S")
points(cards, unlist(lapply(col.s$weights, sum)), col="blue")
legend(x=c(0, 1), y=c(7, 9), legend=c("W", "B", "C", "U", "S"),
col=c("black", "red", "green", "orange", "blue"), pch=rep(1,5))
summary(nb2listw(col.gal.nb, style="minmax"))
dlist < nbdists(col.gal.nb, coords)
dlist < lapply(dlist, function(x) 1/x)
col.w.d < nb2listw(col.gal.nb, glist=dlist)
summary(unlist(col.w$weights))
summary(unlist(col.w.d$weights))
# introducing other conditions into weights  only earlier sales count
# see http://sal.uiuc.edu/pipermail/openspace/2005October/000610.html
data(baltimore)
set.seed(211)
dates < sample(1:500, nrow(baltimore), replace=TRUE)
nb_15nn < knn2nb(knearneigh(cbind(baltimore$X, baltimore$Y), k=15))
glist < vector(mode="list", length=length(nb_15nn))
for (i in seq(along=nb_15nn))
glist[[i]] < ifelse(dates[i] > dates[nb_15nn[[i]]], 1, 0)
listw_15nn_dates < nb2listw(nb_15nn, glist=glist, style="B")
which(lag(listw_15nn_dates, baltimore$PRICE) == 0.0)
which(sapply(glist, sum) == 0)
ex < which(sapply(glist, sum) == 0)[1]
dates[ex]
dates[nb_15nn[[ex]]]