moran.test {spdep} | R Documentation

## Moran's I test for spatial autocorrelation

### Description

Moran's test for spatial autocorrelation using a spatial weights matrix in weights list form. The assumptions underlying the test are sensitive to the form of the graph of neighbour relationships and other factors, and results may be checked against those of `moran.mc` permutations.

### Usage

    moran.test(x, listw, randomisation=TRUE, zero.policy=FALSE,
            alternative="greater", rank = FALSE, na.action=na.fail, 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`
`randomisation` | variance of I calculated under the assumption of randomisation, if FALSE normality
`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.
`rank` | logical value - default FALSE for continuous variables, if TRUE, uses the adaptation of Moran's I for ranks suggested by Cliff and Ord (1981, p. 46)
`na.action` | 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. If `na.pass` is used, zero is substituted for NA values in calculating the spatial lag
`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 Moran's I, its expectation and variance under the method assumption.
`alternative` | a character string describing the alternative hypothesis.
`method` | a character string giving the assumption used for calculating the standard deviate.
`data.name` | a character string giving the name(s) of the data.

### Note

Var(I) is taken from Goodchild's CATMOG 47, see also Upton & Fingleton (1985) p. 171; it agrees with SpaceStat, see Tutorial workbook Chapter 22; VI is as given by Cliff and Ord minus the square of EI. The derivation of the test (Cliff and Ord, 1981, p. 18) assumes that the weights matrix is symmetric. For inherently non-symmetric matrices, such as k-nearest neighbour matrices, `listw2U()` can be used to make the matrix symmetric.

### Author(s)

Roger Bivand Roger.Bivand@nhh.no

### References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 21.

### See Also

`moran`, `moran.mc`, `listw2U`

### Examples

    data(oldcol)
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="W"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="B"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="C"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="S"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="W"),
     randomisation=FALSE)
    colold.lags <- nblag(COL.nb, 3)
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(colold.lags[[2]],
     style="W"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(colold.lags[[3]],
     style="W"))
    print(is.symmetric.nb(COL.nb))
    COL.k4.nb <- knn2nb(knearneigh(coords.OLD, 4))
    print(is.symmetric.nb(COL.k4.nb))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.k4.nb, style="W"))
    moran.test(spNamedVec("CRIME", COL.OLD), nb2listw(COL.k4.nb, style="W"),
     randomisation=FALSE)
    cat("Note: non-symmetric weights matrix, use listw2U()")
    moran.test(spNamedVec("CRIME", COL.OLD), listw2U(nb2listw(COL.k4.nb,
     style="W")))
    moran.test(spNamedVec("CRIME", COL.OLD), listw2U(nb2listw(COL.k4.nb,
     style="W")), randomisation=FALSE)
    ranks <- rank(COL.OLD$CRIME)
    names(ranks) <- rownames(COL.OLD)
    moran.test(ranks, nb2listw(COL.nb, style="W"), rank=TRUE)
    crime <- spNamedVec("CRIME", COL.OLD)
    is.na(crime) <- sample(1:length(crime), 10)
    res <- try(moran.test(crime, nb2listw(COL.nb, style="W"), na.action=na.fail))
    res
    moran.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE,
     na.action=na.omit)
    moran.test(crime, nb2listw(COL.nb, style="W"), zero.policy=TRUE,
     na.action=na.exclude)
    moran.test(crime, nb2listw(COL.nb, style="W"), na.action=na.pass)

[Package spdep version 0.3-25 Index]