Free Access Week - Data Engineering + BI
Data Engineering and BI courses are free this week!
Free Access Week - Jun 2-8
Learn R Programming
spdep (version 0.2-14)

moran.mc: Permutation test for Moran's I statistic

Description

A permutation test for Moran's I 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.

Usage

moran.mc(x, listw, nsim, zero.policy=FALSE, alternative="greater", na.action=na.fail, spChk=NULL)

Arguments

x
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
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 ma
spChk
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

Value

  • A list with class htest and mc.sim containing the following components:
  • statisticthe value of the observed Moran's I.
  • parameterthe rank of the observed Moran's I.
  • p.valuethe pseudo p-value of the test.
  • alternativea character string describing the alternative hypothesis.
  • methoda character string giving the method used.
  • data.namea character string giving the name(s) of the data, and the number of simulations.
  • resnsim simulated values of statistic, final value is observed statistic

References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 63-5.

See Also

moran, moran.test

Examples

Run this code
data(oldcol)
sim1 <- moran.mc(spNamedVec("CRIME", COL.OLD), nb2listw(COL.nb, style="W"),
 nsim=99)
mean(sim1$res)
var(sim1$res)
summary(sim1$res)
colold.lags <- nblag(COL.nb, 3)
sim2 <- moran.mc(spNamedVec("CRIME", COL.OLD), nb2listw(colold.lags[[2]],
 style="W"), nsim=99)
summary(sim2$res)
sim3 <- moran.mc(spNamedVec("CRIME", COL.OLD), nb2listw(colold.lags[[3]],
 style="W"), nsim=99)
summary(sim3$res)

Run the code above in your browser using DataLab