localmoran {spdep} | R Documentation |
The local spatial statistic Moran's I is calculated for each zone based on the spatial weights object used. The values returned include a Z-value, and may be used as a diagnostic tool. The statistic is:
I_i = \frac{(x_i-\bar{x})}{{∑_{k=1}^{n}(x_k-\bar{x})^2}/(n-1)}{∑_{j=1}^{n}w_{ij}(x_j-\bar{x})}
, and its expectation and variance are given in Anselin (1995).
localmoran(x, listw, zero.policy=NULL, na.action=na.fail, alternative = "greater", p.adjust.method="none", mlvar=TRUE, spChk=NULL)
x |
a numeric vector the same length as the neighbours list in listw |
listw |
a |
zero.policy |
default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA |
na.action |
a function (default |
alternative |
a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided. |
p.adjust.method |
a character string specifying the probability value adjustment for multiple tests, default "none"; see |
mlvar |
default TRUE: values of local Moran's I are reported using the variance of the variable of interest (sum of squared deviances over n), but can be reported as the sample variance, dividing by (n-1) instead; both are used in other implementations. |
spChk |
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use |
Ii |
local moran statistic |
E.Ii |
expectation of local moran statistic |
Var.Ii |
variance of local moran statistic |
Z.Ii |
standard deviate of local moran statistic |
Pr() |
p-value of local moran statistic |
Roger Bivand Roger.Bivand@nhh.no
Anselin, L. 1995. Local indicators of spatial association, Geographical Analysis, 27, 93–115; Getis, A. and Ord, J. K. 1996 Local spatial statistics: an overview. In P. Longley and M. Batty (eds) Spatial analysis: modelling in a GIS environment (Cambridge: Geoinformation International), 261–277.
data(afcon) oid <- order(afcon$id) resI <- localmoran(afcon$totcon, nb2listw(paper.nb)) printCoefmat(data.frame(resI[oid,], row.names=afcon$name[oid]), check.names=FALSE) hist(resI[,5]) resI <- localmoran(afcon$totcon, nb2listw(paper.nb), p.adjust.method="bonferroni") printCoefmat(data.frame(resI[oid,], row.names=afcon$name[oid]), check.names=FALSE) hist(resI[,5]) totcon <-afcon$totcon is.na(totcon) <- sample(1:length(totcon), 5) totcon resI.na <- localmoran(totcon, nb2listw(paper.nb), na.action=na.exclude, zero.policy=TRUE) if (class(attr(resI.na, "na.action")) == "exclude") { print(data.frame(resI.na[oid,], row.names=afcon$name[oid]), digits=2) } else print(resI.na, digits=2) resG <- localG(afcon$totcon, nb2listw(include.self(paper.nb))) print(data.frame(resG[oid], row.names=afcon$name[oid]), digits=2)