lee.mc
From spdep v0.69
by Roger Bivand
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 bynb2listw
 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 bena.omit
orna.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 noneighbour observations. Note that only weights lists created without using the glist argument tonb2listw
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 classhtest
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: 369385
See Also
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)
Community examples
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