Global G test for spatial autocorrelation
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, B1correct=TRUE, adjust.x=TRUE, Arc_all_x=FALSE)
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 |
B1correct |
default TRUE, if TRUE, the erratum referenced below: "On page 195, the coefficient of W2 in B1, (just below center of the page) should be 6, not 3." is applied; if FALSE, 3 is used (as in CrimeStat IV) |
adjust.x |
default TRUE, if TRUE, x values of observations with no neighbours are omitted in the denominator of G |
Arc_all_x |
default FALSE, if Arc_all_x=TRUE and adjust.x=TRUE, use the full x vector in part of the denominator term for G |
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; Bivand RS, Wong DWS 2018 Comparing implementations of global and local indicators of spatial association. TEST, 27(3), 716–748 doi: 10.1007/s11749-018-0599-x
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
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 <- vector(mode="list", length=ndists)
names(ZG) <- as.character(dists)
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)
}
t(sapply(ZG, function(x) c(x$estimate[1], x$statistic, p.value=unname(x$p.value))))
for (i in 1:ndists) {
thisnb <- dnearneigh(milesxy, 0, dists[i])
thislw <- nb2listw(thisnb, style="B", zero.policy=TRUE)
ZG[[i]] <- globalG.test(sidsrate79, thislw, zero.policy=TRUE, alternative="two.sided")
}
t(sapply(ZG, function(x) c(x$estimate[1], x$statistic, p.value=unname(x$p.value))))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.