A permutation test for Geary's C statistic calculated by using nsim random permutations of x for the given spatial weighting scheme, to establish the rank of the observed statistic in relation to the nsim simulated values.
geary.mc(x, listw, nsim, zero.policy=attr(listw, "zero.policy"), alternative="greater",
spChk=NULL, adjust.n=TRUE, return_boot=FALSE)
A list with class htest
and mc.sim
containing the following components:
the value of the observed Geary's C.
the rank of the observed Geary's C.
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
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), or "less"; this reversal corresponds to that on geary.test
described in the section on the output statistic value, based on Cliff and Ord 1973, p. 21 (changed 2011-04-11, thanks to Daniel Garavito).
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()
default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted
return an object of class boot
from the equivalent permutation bootstrap rather than an object of class htest
Roger Bivand Roger.Bivand@nhh.no
Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 63-5.
geary
, geary.test
data(oldcol)
sim1 <- geary.mc(COL.OLD$CRIME, nb2listw(COL.nb, style="W"),
nsim=99, alternative="less")
sim1
mean(sim1$res)
var(sim1$res)
summary(sim1$res)
colold.lags <- nblag(COL.nb, 3)
sim2 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[2]],
style="W"), nsim=99)
sim2
summary(sim2$res)
sim3 <- geary.mc(COL.OLD$CRIME, nb2listw(colold.lags[[3]],
style="W"), nsim=99)
sim3
summary(sim3$res)
Run the code above in your browser using DataLab