globalG.test: Global G test for spatial autocorrelation
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description Usage Arguments Value Author(s) References See Also Examples

The global G statistic for spatial autocorrelation, complementing the local Gi LISA measures: localG.

1 2	globalG.test(x, listw, zero.policy=NULL, alternative="greater", spChk=NULL, adjust.n=TRUE, B1correct=TRUE)

`x`	a numeric vector the same length as the neighbours list in listw
`listw`	a `listw` object created for example by `nb2listw`; if a sequence of distance bands is to be used, it is recommended that the weights style be binary (one of `c("B", "C", "U")`).
`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 `get.spChkOption()`
`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)

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.

localG

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 <- 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))))

Loading required package: sp
Loading required package: Matrix

nc.sds> require(maptools)
Loading required package: maptools
Checking rgeos availability: TRUE

nc.sds> nc.sids <- readShapePoly(system.file("etc/shapes/sids.shp", package="spdep")[1],
nc.sds+   ID="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files

nc.sds> rn <- sapply(slot(nc.sids, "polygons"), function(x) slot(x, "ID"))

nc.sds> ncCC89_nb <- read.gal(system.file("etc/weights/ncCC89.gal", package="spdep")[1],
nc.sds+   region.id=rn)

nc.sds> ncCR85_nb <- read.gal(system.file("etc/weights/ncCR85.gal", package="spdep")[1],
nc.sds+   region.id=rn)

nc.sds> ## Not run: 
nc.sds> ##D plot(nc.sids, border="grey")
nc.sds> ##D plot(ncCR85_nb, coordinates(nc.sids), add=TRUE, col="blue")
nc.sds> ##D plot(nc.sids, border="grey")
nc.sds> ##D plot(ncCC89_nb, coordinates(nc.sids), add=TRUE, col="blue")
nc.sds> ## End(Not run)
nc.sds> 
nc.sds> 
nc.sds> 
Warning message:
use rgdal::readOGR or sf::st_read 
    Global G statistic standard deviate   p.value
10         0.000612124       0.81651489 0.2071029
20         0.014698945       0.27046614 0.3934008
30         0.039947466      -0.11849772 0.5471634
33         0.053122713       0.40157023 0.3440002
40         0.074002794      -0.04345713 0.5173314
50         0.114717434       0.58686472 0.2786473
60         0.154575526      -0.35823892 0.6399177
70         0.198390232      -0.27864299 0.6097406
80         0.246069725      -0.18791364 0.5745278
90         0.300734634       0.11457610 0.4543906
100        0.348799963       0.31591356 0.3760341
    Global G statistic standard deviate   p.value
10         0.000612124       0.81651489 0.4142057
20         0.014698945       0.27046614 0.7868017
30         0.039947466      -0.11849772 0.9056733
33         0.053122713       0.40157023 0.6880003
40         0.074002794      -0.04345713 0.9653371
50         0.114717434       0.58686472 0.5572946
60         0.154575526      -0.35823892 0.7201645
70         0.198390232      -0.27864299 0.7805188
80         0.246069725      -0.18791364 0.8509443
90         0.300734634       0.11457610 0.9087811
100        0.348799963       0.31591356 0.7520681