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 usednb
get.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 nb2listw
localmoransad
containing "select" lists, each with
class moransad
with the following components:localmoran
, lm.morantest
,
lm.morantest.sad
data(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