lm.morantest.exact | R Documentation |
The function implements Tiefelsdorf's exact global Moran's I test.
lm.morantest.exact(model, listw, zero.policy = attr(listw, "zero.policy"),
alternative = "greater", spChk = NULL, resfun = weighted.residuals,
zero.tol = 1e-07, Omega=NULL, save.M=NULL, save.U=NULL, useTP=FALSE,
truncErr=1e-6, zeroTreat=0.1)
## S3 method for class 'moranex'
print(x, ...)
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 |
zero.tol |
tolerance used to find eigenvalues close to absolute zero |
Omega |
A SAR process matrix may be passed in to test an alternative hypothesis, for example |
save.M |
return the full M matrix for use in |
save.U |
return the full U matrix for use in |
useTP |
default FALSE, if TRUE, use truncation point in integration rather than upper=Inf, see Tiefelsdorf (2000), eq. 6.7, p.69 |
truncErr |
when useTP=TRUE, pass truncation error to truncation point function |
zeroTreat |
when useTP=TRUE, pass zero adjustment to truncation point function |
x |
a moranex object |
... |
arguments to be passed through |
A list of class moranex
with the following components:
statistic |
the value of the exact standard deviate of global Moran's I. |
p.value |
the p-value of the test. |
estimate |
the value of the observed global Moran's I. |
method |
a character string giving the method used. |
alternative |
a character string describing the alternative hypothesis. |
gamma |
eigenvalues (excluding zero values) |
oType |
usually set to "E" |
data.name |
a character string giving the name(s) of the data. |
df |
degrees of freedom |
Markus Reder and Roger Bivand
Roger Bivand, Werner G. Müller and Markus Reder (2009) "Power calculations for global and local Moran's I." Computational Statistics & Data Analysis 53, 2859-2872.
lm.morantest.sad
eire <- st_read(system.file("shapes/eire.gpkg", package="spData")[1])
row.names(eire) <- as.character(eire$names)
eire.nb <- poly2nb(eire)
e.lm <- lm(OWNCONS ~ ROADACC, data=eire)
lm.morantest(e.lm, nb2listw(eire.nb))
lm.morantest.sad(e.lm, nb2listw(eire.nb))
lm.morantest.exact(e.lm, nb2listw(eire.nb))
lm.morantest.exact(e.lm, nb2listw(eire.nb), useTP=TRUE)
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