globalG.test {spdep} | R Documentation

## 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.

### Author(s)

Hisaji ONO hi-ono@mn.xdsl.ne.jp and Roger Bivand Roger.Bivand@nhh.no

### 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)

[Package spdep version 0.3-25 Index]