lee.mc: Permutation test for Lee's L statistic

Description

A permutation test for Lee's L statistic calculated by using nsim random permutations of x and y for the given spatial weighting scheme, to establish the rank of the observed statistic in relation to the nsim simulated values.

Usage

lee.mc(x, y, listw, nsim, zero.policy=NULL, alternative="greater",
 na.action=na.fail, spChk=NULL, return_boot=FALSE)

Arguments

a numeric vector the same length as the neighbours list in listw

listw

a listw object created for example by nb2listw

nsim

number of permutations

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), or "less".

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 ma

spChk

should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

return_boot

return an object of class boot from the equivalent permutation bootstrap rather than an object of class htest

Value

A list with class htest and mc.sim containing the following components:
statisticthe value of the observed Lee's L.
parameterthe rank of the observed Lee's L.
p.valuethe pseudo p-value of the test.
alternativea character string describing the alternative hypothesis.
methoda character string giving the method used.
data.namea character string giving the name(s) of the data, and the number of simulations.
resnsim simulated values of statistic, final value is observed statistic

encoding

latin1

References

Lee (2001). Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I. J Geograph Syst 3: 369-385

Examples

Run this code

data(boston)
lw<-nb2listw(boston.soi)

x<-boston.c$CMEDV
y<-boston.c$CRIM

lee.mc(x, y, nsim=99, lw, zero.policy=TRUE, alternative="less")

#Test with missing values
x[1:5]<-NA
y[3:7]<-NA

lee.mc(x, y, nsim=99, lw, zero.policy=TRUE, alternative="less", 
   na.action=na.omit)

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