DCluster (version 0.2-2)

moranI: Compute Moran's I autocorrelation statistic

Description

Compute Moran's I autocorrelation statistic using residuals or SMRs by means of function moran from package spdep.

moranI.stat computes the test statistic and the test using a hi-square distribution whilst moranI.test performs a bootstrap test.

Usage

moranI.stat(data, applyto="SMR", ...)
moranI.test(formula, data, model, R, ...)

Arguments

formula

Formula that specifies the underlying model. The observed cases are the response and the expected number of cases must be specified as an offset in the log scale (see example below). Note that now it is not necessary to use Observed and Expected and that

model

Parametric model to be used in the bootstrap test. One of "param", "multinom", "poisson" or "negbin". See the DCluster manpage for details.

...

Arguments needed by function moran from package spdep. In addition, when calling 'moranI.test' the remaining arguments in 'moranI.stat' not included in 'moranI.test'. This is done so because moranI.test calls moranI.stat in order to per

R

Number of replicates used in the test to compute the significance of the observed value of the test statistic.

data

A dataframe containing the data, as specified in the DCluster manpage.

applyto

A string with the name of the statistic with which calculate Moran's Index. It may be either residulas or SMR.

Value

• The value of the statistic computed.

References

Moran, P. A. P. (1948). The interpretation os statistical maps. Journal of the Royal Statistical Society, Series B 10, 243-251.

See Also

DCluster, moran, moranI, moranI.boot, MoranI.pboot

Examples

library(spdep)
data(nc.sids)
col.W <- nb2listw(ncCR85.nb, zero.policy=TRUE)


sids<-data.frame(Observed=nc.sids$SID74)
sids<-cbind(sids, Expected=nc.sids$BIR74*sum(nc.sids$SID74)/sum(nc.sids$BIR74) )

moranI.stat(data=sids, listw=col.W, n=length(ncCR85.nb), S0=Szero(col.W) )

moranI.stat(data=sids, applyto="residuals", listw=col.W, n=length(ncCR85.nb), 
	S0=Szero(col.W) )

moranI.test(Observed~offset(log(Expected)), sids, model="poisson", R=99,
   listw=col.W, n=length(ncCR85.nb), S0=Szero(col.W) )