spdep (version 0.1-2)

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=FALSE, alternative="greater", 
  tol=.Machine$double.eps^0.5, maxiter=1000)
print.moransad(x, ...)
summary.moransad(object, ...)
print.summary.moransad(x, ...)

Arguments

model

an object of class lm returned by lm; weights and offsets should not be used

listw

a listw object created for example by nb2listw

zero.policy

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.

tol

the desired accuracy (convergence tolerance) for uniroot

maxiter

the maximum number of iterations for uniroot

x

object to be printed

object

object to be summarised

...

arguments to be passed through

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)

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.

References

Tiefelsdorf, M. 2002 The Saddlepoint approximation of Moran's I and local Moran's Ii reference distributions and their numerical evaluation. Geographical Analysis, forthcoming; see also Tiefelsdorf's SPSS code: http://geog-www.sbs.ohio-state.edu/faculty/tiefelsdorf/GeoStat.htm.

See Also

lm.morantest

Examples

data(eire)
e.lm <- lm(OWNCONS ~ ROADACC, data=eire.df)
lm.morantest(e.lm, nb2listw(eire.nb))
lm.morantest.sad(e.lm, nb2listw(eire.nb))
summary(lm.morantest.sad(e.lm, nb2listw(eire.nb)))