globalG.test {spdep} | R Documentation |
The global G statistic for spatial autocorrelation, complementing the local Gi LISA measures: localG
.
globalG.test(x, listw, zero.policy=NULL, alternative="greater", spChk=NULL, adjust.n=TRUE)
x |
a numeric vector the same length as the neighbours list in listw |
listw |
a |
zero.policy |
default NULL, use global option value; 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 |
adjust.n |
default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted |
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. |
Hisaji ONO hi-ono@mn.xdsl.ne.jp and Roger Bivand Roger.Bivand@nhh.no
Getis. A, Ord, J. K. 1992 The analysis of spatial association by use of distance statistics, Geographical Analysis, 24, p. 195; see also Getis. A, Ord, J. K. 1993 Erratum, Geographical Analysis, 25, p. 276.
example(nc.sids) sidsrate79 <- (1000*nc.sids$SID79)/nc.sids$BIR79 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]) thislw <- nb2listw(thisnb, style="B", zero.policy=TRUE) ZG[i] <- globalG.test(sidsrate79, thislw, zero.policy=TRUE)$statistic } cbind(dists, ZG)