lee.mc.Rd
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.
lee.mc(x, y, listw, nsim, zero.policy=attr(listw, "zero.policy"), alternative="greater",
na.action=na.fail, spChk=NULL, return_boot=FALSE)
a numeric vector the same length as the neighbours list in listw
a numeric vector the same length as the neighbours list in listw
a listw
object created for example by nb2listw
number of permutations
default attr(listw, "zero.policy")
as set when listw
was created, if attribute not set, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
a character string specifying the alternative hypothesis, must be one of "greater" (default), "two.sided", or "less".
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.
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()
return an object of class boot
from the equivalent permutation bootstrap rather than an object of class htest
A list with class htest
and mc.sim
containing the following components:
the value of the observed Lee's L.
the rank of the observed Lee's L.
the pseudo p-value of the test.
a character string describing the alternative hypothesis.
a character string giving the method used.
a character string giving the name(s) of the data, and the number of simulations.
nsim simulated values of statistic, final value is observed statistic
Lee (2001). Developing a bivariate spatial association measure: An integration of Pearson's r and Moran's I. J Geograph Syst 3: 369-385
data(boston, package="spData")
lw<-nb2listw(boston.soi)
x<-boston.c$CMEDV
y<-boston.c$CRIM
lee.mc(x, y, nsim=99, lw, zero.policy=TRUE, alternative="two.sided")
#>
#> Monte-Carlo simulation of Lee's L
#>
#> data: x , y
#> weights: lw
#> number of simulations + 1: 100
#>
#> statistic = -0.3263, observed rank = 1, p-value = 0.02
#> alternative hypothesis: two.sided
#>
#Test with missing values
x[1:5]<-NA
y[3:7]<-NA
lee.mc(x, y, nsim=99, lw, zero.policy=TRUE, alternative="two.sided",
na.action=na.omit)
#>
#> Monte-Carlo simulation of Lee's L
#>
#> data: x , y
#> weights: lw
#> omitted: 1, 2, 3, 4, 5, 6, 7
#> number of simulations + 1: 100
#>
#> statistic = -0.32447, observed rank = 1, p-value = 0.02
#> alternative hypothesis: two.sided
#>