spdep: lm.morantest.sad – R documentation

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lm.morantest.sad

Saddlepoint approximation of global Moran's I test

Description

The function implements Tiefelsdorf's application of the Saddlepoint approximation to global Moran's I's reference distribution.

Usage

lm.morantest.sad(model, listw, zero.policy=NULL, alternative="greater", 
  spChk=NULL, resfun=weighted.residuals, tol=.Machine$double.eps^0.5,
  maxiter=1000, tol.bounds=0.0001, zero.tol = 1e-07, Omega=NULL,
  save.M=NULL, save.U=NULL)
## S3 method for class 'moransad'
print(x, ...)
## S3 method for class 'moransad'
summary(object, ...)
## S3 method for class 'summary.moransad'
print(x, ...)

Arguments

`model`	an object of class `lm` returned by `lm`; weights may be specified in the `lm` fit, but offsets should not be used
`listw`	a `listw` object created for example by `nb2listw`
`zero.policy`	default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
`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 `get.spChkOption()`
`resfun`	default: weighted.residuals; the function to be used to extract residuals from the `lm` object, may be `residuals`, `weighted.residuals`, `rstandard`, or `rstudent`
`tol`	the desired accuracy (convergence tolerance) for `uniroot`
`maxiter`	the maximum number of iterations for `uniroot`
`tol.bounds`	offset from bounds for `uniroot`
`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 `Omega <- invIrW(listw, rho=0.1); Omega <- tcrossprod(Omega)`, `chol()` is taken internally
`save.M`	return the full M matrix for use in `spdep:::exactMoranAlt`
`save.U`	return the full U matrix for use in `spdep:::exactMoranAlt`
`x`	object to be printed
`object`	object to be summarised
`...`	arguments to be passed through

Details

The function involves finding the eigenvalues of an n by n matrix, and numerically finding the root for the Saddlepoint approximation, and should therefore only be used with care when n is large.

Value

A list of class moransad with the following components:

`statistic`	the value of the saddlepoint approximation of the 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.
`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.
`internal1`	Saddlepoint omega, r and u
`internal2`	f.root, iter and estim.prec from `uniroot`
`df`	degrees of freedom
`tau`	eigenvalues (excluding zero values)

Author(s)

Roger Bivand Roger.Bivand@nhh.no

References

Tiefelsdorf, M. 2002 The Saddlepoint approximation of Moran's I and local Moran's Ii reference distributions and their numerical evaluation. Geographical Analysis, 34, pp. 187–206; Bivand RS, Wong DWS 2018 Comparing implementations of global and local indicators of spatial association. TEST, 27(3), 716–748 doi: 10.1007/s11749-018-0599-x

Examples

eire <- st_read(system.file("shapes/eire.shp", package="spData")[1])
row.names(eire) <- as.character(eire$names)
st_crs(eire) <- "+proj=utm +zone=30 +ellps=airy +units=km"
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))
summary(lm.morantest.sad(e.lm, nb2listw(eire.nb)))
e.wlm <- lm(OWNCONS ~ ROADACC, data=eire, weights=RETSALE)
lm.morantest(e.wlm, nb2listw(eire.nb), resfun=rstudent)
lm.morantest.sad(e.wlm, nb2listw(eire.nb), resfun=rstudent)

spdep

Spatial Dependence: Weighting Schemes, Statistics

v1.1-11

GPL (>= 2)

Authors

Roger Bivand [cre, aut] (<https://orcid.org/0000-0003-2392-6140>), Micah Altman [ctb], Luc Anselin [ctb], Renato Assunção [ctb], Olaf Berke [ctb], Andrew Bernat [ctb], Guillaume Blanchet [ctb], Eric Blankmeyer [ctb], Marilia Carvalho [ctb], Bjarke Christensen [ctb], Yongwan Chun [ctb], Carsten Dormann [ctb], Stéphane Dray [ctb], Virgilio Gómez-Rubio [ctb], Martin Gubri [ctb], Rein Halbersma [ctb], Elias Krainski [ctb], Pierre Legendre [ctb], Nicholas Lewin-Koh [ctb], Angela Li [ctb], Hongfei Li [ctb], Jielai Ma [ctb], Abhirup Mallik [ctb, trl], Giovanni Millo [ctb], Werner Mueller [ctb], Hisaji Ono [ctb], Pedro Peres-Neto [ctb], Gianfranco Piras [ctb], Markus Reder [ctb], Jeff Sauer [ctb], Michael Tiefelsdorf [ctb], René Westerholt [ctb], Levi Wolf [ctb], Danlin Yu [ctb]

Initial release

2021-09-07