lee.mc | R Documentation |
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=NULL, alternative="greater", na.action=na.fail, spChk=NULL, return_boot=FALSE)
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 |
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), "two.sided", or "less". |
na.action |
a function (default |
spChk |
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use |
return_boot |
return an object of class |
A list with class htest
and mc.sim
containing the following components:
statistic |
the value of the observed Lee's L. |
parameter |
the rank of the observed Lee's L. |
p.value |
the pseudo p-value of the test. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string giving the method used. |
data.name |
a character string giving the name(s) of the data, and the number of simulations. |
res |
nsim simulated values of statistic, final value is observed statistic |
Roger Bivand, Virgilio GÃ³mez-Rubio Virgilio.Gomez@uclm.es
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
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") #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)
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