spdep (version 0.1-10)

globalG.test: test for spatial autocorrelation

Description

.

Usage

globalG.test(x, listw, zero.policy=FALSE, alternative="greater",
 spChk=NULL)

Arguments

x

a numeric vector the same length as the neighbours list in listw

listw

a listw object created for example by nb2listw

zero.policy

if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA

alternative

a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided.

spChk

should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

Value

• A list with class htest containing the following components:
• statistic
  the value of the standard deviate of Moran's I.
• p.value
  the p-value of the test.
• estimate
  the value of the observed statistic, its expectation and variance.
• alternative
  a character string describing the alternative hypothesis.
• data.name
  a character string giving the name(s) of the data.

References

Getis. A, Ord, J. K. 1992 The analysis of spatial association by use of distance statistics, Geographical Analysis, 24, p. 195.

See Also

localG

Examples

data(nc.sids)
sidsrate79 <- (1000*nc.sids$SID79)/nc.sids$BIR79
names(sidsrate79) <- rownames(nc.sids)
dists <- c(10, 20, 30, 33, 40, 50, 60, 70, 80, 90, 100)
ndists <- length(dists)
ZG <- numeric(length=ndists)
milesxy <- cbind(nc.sids$east, nc.sids$north)
for (i in 1:ndists) {
  thisnb <- dnearneigh(milesxy, 0, dists[i], row.names=rownames(nc.sids))
  thislw <- nb2listw(thisnb, style="B", zero.policy=TRUE)
  ZG[i] <- globalG.test(sidsrate79, thislw, zero.policy=TRUE)$statistic
}
cbind(dists, ZG)