globalG.test.RdThe global G statistic for spatial autocorrelation, complementing the local Gi LISA measures: localG.
globalG.test(x, listw, zero.policy=attr(listw, "zero.policy"), alternative="greater",
spChk=NULL, adjust.n=TRUE, B1correct=TRUE, adjust.x=TRUE, Arc_all_x=FALSE,
na.action=na.fail)a numeric vector the same length as the neighbours list in 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")).
default attr(listw, "zero.policy") as set when listw was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two.sided".
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()
default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted
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)
default TRUE, if TRUE, x values of observations with no neighbours are omitted in the denominator of G
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 function (default na.fail), can also be na.omit or na.exclude - in these cases the weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted. na.pass is not permitted.
A list with class htest containing the following components:
the value of the standard deviate of Getis-Ord G.
the p-value of the test.
the value of the observed statistic, its expectation and variance.
a character string describing the alternative hypothesis.
a character string giving the name(s) of the data.
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$p.value))))
#> Global G statistic standard deviate p.value
#> 10 0.33548581 0.04859237 0.4806221
#> 20 0.02024945 -0.73262399 0.7681061
#> 30 0.04032434 -0.75011405 0.7734070
#> 33 0.05312271 0.40157023 0.3440002
#> 40 0.07400279 -0.04345713 0.5173314
#> 50 0.11471743 0.58686472 0.2786473
#> 60 0.15457553 -0.35823892 0.6399177
#> 70 0.19839023 -0.27864299 0.6097406
#> 80 0.24606972 -0.18791364 0.5745278
#> 90 0.30073463 0.11457610 0.4543906
#> 100 0.34879996 0.31591356 0.3760341
for (i in 1:ndists) {
thisnb <- dnearneigh(milesxy, 0, dists[i])
thislw <- nb2listw(thisnb, style="B", zero.policy=TRUE)
ZG[[i]] <- globalG.test(sidsrate79, thislw, zero.policy=TRUE, alternative="two.sided")
}
t(sapply(ZG, function(x) c(x$estimate[1], x$statistic, p.value=unname(x$p.value))))
#> Global G statistic standard deviate p.value
#> 10 0.33548581 0.04859237 0.9612441
#> 20 0.02024945 -0.73262399 0.4637878
#> 30 0.04032434 -0.75011405 0.4531860
#> 33 0.05312271 0.40157023 0.6880003
#> 40 0.07400279 -0.04345713 0.9653371
#> 50 0.11471743 0.58686472 0.5572946
#> 60 0.15457553 -0.35823892 0.7201645
#> 70 0.19839023 -0.27864299 0.7805188
#> 80 0.24606972 -0.18791364 0.8509443
#> 90 0.30073463 0.11457610 0.9087811
#> 100 0.34879996 0.31591356 0.7520681
data(oldcol)
crime <- COL.OLD$CRIME
is.na(crime) <- sample(1:length(crime), 10)
res <- try(globalG.test(crime, nb2listw(COL.nb, style="B"),
na.action=na.fail))
#> Error in na.fail.default(x) : missing values in object
res
#> [1] "Error in na.fail.default(x) : missing values in object\n"
#> attr(,"class")
#> [1] "try-error"
#> attr(,"condition")
#> <simpleError in na.fail.default(x): missing values in object>
globalG.test(crime, nb2listw(COL.nb, style="B"), zero.policy=TRUE,
na.action=na.omit)
#> Warning: subsetting caused increase in subgraph count
#>
#> Getis-Ord global G statistic
#>
#> data: crime
#> weights: nb2listw(COL.nb, style = "B")
#> omitted: 2, 3, 5, 6, 7, 10, 13, 30, 39, 49
#> n reduced by no-neighbour observations
#>
#> standard deviate = 3.8084, p-value = 6.993e-05
#> alternative hypothesis: greater
#> sample estimates:
#> Global G statistic Expectation Variance
#> 1.441233e-01 1.123755e-01 6.949287e-05
#>
globalG.test(crime, nb2listw(COL.nb, style="B"), zero.policy=TRUE,
na.action=na.exclude)
#> Warning: subsetting caused increase in subgraph count
#>
#> Getis-Ord global G statistic
#>
#> data: crime
#> weights: nb2listw(COL.nb, style = "B")
#> omitted: 2, 3, 5, 6, 7, 10, 13, 30, 39, 49
#> n reduced by no-neighbour observations
#>
#> standard deviate = 3.8084, p-value = 6.993e-05
#> alternative hypothesis: greater
#> sample estimates:
#> Global G statistic Expectation Variance
#> 1.441233e-01 1.123755e-01 6.949287e-05
#>
try(globalG.test(crime, nb2listw(COL.nb, style="B"), na.action=na.pass))
#> Error in globalG.test(crime, nb2listw(COL.nb, style = "B"), na.action = na.pass) :
#> na.pass not permitted