spdep (version 0.2-3)

moran.test: 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, 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)

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.

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)