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)
"print"(x, ...)
"summary"(object, ...)
"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
object
object to be summarised
...
arguments to be passed through