# lee.mc

0th

Percentile

##### Permutation test for Lee's L statistic

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.

Keywords
spatial
##### Usage
lee.mc(x, y, listw, nsim, zero.policy=NULL, alternative="greater", na.action=na.fail, spChk=NULL, return_boot=FALSE)
##### Arguments
x
a numeric vector the same length as the neighbours list in listw
y
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 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 because it is meaningless in a permutation test.
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:

##### 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

lee

• lee.mc
##### Examples
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)


Documentation reproduced from package spdep, version 0.6-9, License: GPL (>= 2)

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