lm.LMtests {spdep} | R Documentation |
The function reports the estimates of tests chosen among five statistics for testing for spatial dependence in linear models. The statistics are the simple LM test for error dependence (LMerr), the simple LM test for a missing spatially lagged dependent variable (LMlag), variants of these robust to the presence of the other (RLMerr, RLMlag - RLMerr tests for error dependence in the possible presence of a missing lagged dependent variable, RLMlag the other way round), and a portmanteau test (SARMA, in fact LMerr + RLMlag).
lm.LMtests(model, listw, zero.policy=FALSE, test="LMerr", spChk=NULL) print.LMtestlist(x, ...)
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 ,
expected to be row-standardised (W-style) |
zero.policy |
if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA |
test |
a character vector of tests requested chosen from LMerr, LMlag, RLMerr, RLMlag, SARMA; test="all" computes all the tests. |
spChk |
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption() |
x |
object to be printed |
... |
printing arguments to be passed through |
The two types of dependence are for spatial lag $rho$ and spatial error $λ$:
y = X beta + rho W1 y + u
u = lambda W2 u + e
where e is a well-behaved, uncorrelated error term. Tests for a missing spatially lagged dependent variable test that rho = 0, tests for spatial autocorrelation of the error u test whether lambda = 0. W is a spatial weights matrix; for the tests used here they are identical.
A list of class LMtestlist
of htest
objects, each with:
statistic |
the value of the Lagrange Multiplier test. |
parameter |
number of degrees of freedom |
p.value |
the p-value of the test. |
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 and Andrew Bernat
Anselin, L. 1988 Spatial econometrics: methods and models. (Dordrecht: Kluwer); Anselin, L., Bera, A. K., Florax, R. and Yoon, M. J. 1996 Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics, 26, 77–104.
data(oldcol) oldcrime.lm <- lm(CRIME ~ HOVAL + INC, data = COL.OLD) summary(oldcrime.lm) lm.LMtests(oldcrime.lm, nb2listw(COL.nb), test=c("LMerr", "LMlag", "RLMerr", "RLMlag", "SARMA"))