summary.localmoransad. Values of local Moran's Ii
agree with those from localmoran(), but in that function, the
standard deviate - here the Saddlepoint approximation - is based on the
randomisation assumption.localmoran.sad(model, select, nb, glist=NULL, style="W", zero.policy=FALSE,
alternative="greater", spChk=NULL, save.Vi=FALSE)
as.data.frame.localmoransad(x, row.names=NULL, optional=FALSE)
print.localmoransad(x, ...)
summary.localmoransad(object, ...)
print.summary.localmoransad(x, ...)
listw2star(listw, ireg, style, n, D, a, zero.policy=FALSE)lm returned by lm; weights
and offsets should not be usednbget.spChkOption()as.data.frame.localmoransad;
row names assigned from localmoransad objectas.data.frame.localmoransad;
row names assigned from localmoransad objectlistw object created for example by nb2listwlocalmoransad containing "select" lists, each with
class moransad with the following components:localmoran, lm.morantest,
lm.morantest.saddata(eire)
e.lm <- lm(OWNCONS ~ ROADACC, data=eire.df)
e.locmor <- summary(localmoran.sad(e.lm, eire.nb, select=1:nrow(eire.df)))
mean(e.locmor[,1])
lm.morantest(e.lm, nb2listw(eire.nb))Run the code above in your browser using DataLab