lm.LMtests | 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). Note: from spdep 0.3-32, the value of the weights matrix trace term is returned correctly for both underlying symmetric and asymmetric neighbour lists, before 0.3-32, the value was wrong for listw objects based on asymmetric neighbour lists, such as k-nearest neighbours (thanks to Luc Anselin for finding the bug).

lm.LMtests(model, listw, zero.policy=NULL, test="LMerr", spChk=NULL, naSubset=TRUE) ## S3 method for class 'LMtestlist' print(x, ...) ## S3 method for class 'LMtestlist' summary(object, p.adjust.method="none", ...) ## S3 method for class 'LMtestlist.summary' print(x, digits=max(3, getOption("digits") - 2), ...)

`model` |
an object of class |

`listw` |
a |

`zero.policy` |
default NULL, use global option value; 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 |

`naSubset` |
default TRUE to subset listw object for omitted observations in model object (this is a change from earlier behaviour, when the |

`x, object` |
object to be printed |

`p.adjust.method` |
a character string specifying the probability value adjustment (see |

`digits` |
minimum number of significant digits to be used for most numbers |

`...` |
printing arguments to be passed through |

The two types of dependence are for spatial lag *rho* and spatial error *lambda*:

*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.

`lm`

data(oldcol) oldcrime.lm <- lm(CRIME ~ HOVAL + INC, data = COL.OLD) summary(oldcrime.lm) res <- lm.LMtests(oldcrime.lm, nb2listw(COL.nb), test=c("LMerr", "LMlag", "RLMerr", "RLMlag", "SARMA")) summary(res) lm.LMtests(oldcrime.lm, nb2listw(COL.nb)) lm.LMtests(residuals(oldcrime.lm), nb2listw(COL.nb))

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