lm.morantest | R Documentation |
Moran's I test for spatial autocorrelation in residuals from an estimated linear model (lm()
).
lm.morantest(model, listw, zero.policy=attr(listw, "zero.policy"),
alternative = "greater", spChk=NULL, resfun=weighted.residuals, naSubset=TRUE)
model |
an object of class |
listw |
a |
zero.policy |
default |
alternative |
a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two.sided". |
spChk |
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use |
resfun |
default: weighted.residuals; the function to be used to extract residuals from the |
naSubset |
default TRUE to subset listw object for omitted observations in model object (this is a change from earlier behaviour, when the |
A list with class htest
containing the following components:
statistic |
the value of the standard deviate of Moran's I. |
p.value |
the p-value of the test. |
estimate |
the value of the observed Moran's I, its expectation and variance under the method assumption. |
alternative |
a character string describing the alternative hypothesis. |
method |
a character string giving the method used. |
data.name |
a character string giving the name(s) of the data. |
Roger Bivand Roger.Bivand@nhh.no
Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 203,
lm.LMtests
, lm
data(oldcol)
oldcrime1.lm <- lm(CRIME ~ 1, data = COL.OLD)
oldcrime.lm <- lm(CRIME ~ HOVAL + INC, data = COL.OLD)
lm.morantest(oldcrime.lm, nb2listw(COL.nb, style="W"))
lm.LMtests(oldcrime.lm, nb2listw(COL.nb, style="W"))
lm.morantest(oldcrime.lm, nb2listw(COL.nb, style="S"))
lm.morantest(oldcrime1.lm, nb2listw(COL.nb, style="W"))
moran.test(COL.OLD$CRIME, nb2listw(COL.nb, style="W"),
randomisation=FALSE)
oldcrime.wlm <- lm(CRIME ~ HOVAL + INC, data = COL.OLD,
weights = I(1/AREA_PL))
lm.morantest(oldcrime.wlm, nb2listw(COL.nb, style="W"),
resfun=weighted.residuals)
lm.morantest(oldcrime.wlm, nb2listw(COL.nb, style="W"),
resfun=rstudent)
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