lee.test | R Documentation |

Lee's L test for spatial autocorrelation using a spatial weights matrix in weights list form. The assumptions underlying the test are sensitive to the form of the graph of neighbour relationships and other factors, and results may be checked against those of `lee.mc`

permutations.

```
lee.test(x, y, listw, zero.policy=attr(listw, "zero.policy"),
alternative="greater", na.action=na.fail, spChk=NULL)
```

`x` |
a numeric vector the same length as the neighbours list in listw |

`y` |
a numeric vector the same length as the neighbours list in listw |

`listw` |
a |

`zero.policy` |
default |

`alternative` |
a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided. |

`na.action` |
a function (default |

`spChk` |
should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use |

A list with class `htest`

containing the following components:

`statistic` |
the value of the standard deviate of Lee's L. |

`p.value` |
the p-value of the test. |

`estimate` |
the value of the observed Lee's L, its expectation and variance under the method assumption. |

`alternative` |
a character string describing the alternative hypothesis. |

`method` |
a character string giving the assumption used for calculating the standard deviate. |

`data.name` |
a character string giving the name(s) of the data. |

See Lee (2004) for details on how the asymptotic expectation and variance of Lee's L is computed. In particular, check Lee (2004), table 1, page 1690.

This test may fail for large datasets as the computation of the asymptotic expectation and variance requires the use of dense matrices.

Roger Bivand and Virgilio GÃ³mez-Rubio Virgilio.Gomez@uclm.es

Lee (2004). A generalized significance testing method for global measures of spatial association: an extension of the Mantel test. Environment and Planning A 2004, volume 36, pages 1687 - 1703

`lee`

, `lee.mc`

, `listw2U`

```
data(oldcol)
col.W <- nb2listw(COL.nb, style="W")
crime <- COL.OLD$CRIME
lee.test(crime, crime, col.W, zero.policy=TRUE)
#Test with missing values
x<-crime
y<-crime
x[1:5]<-NA
y[3:7]<-NA
lee.test(x, y, col.W, zero.policy=TRUE, na.action=na.omit)
# lee.test(x, y, col.W, zero.policy=TRUE)#Stops with an error
data(boston, package="spData")
lw<-nb2listw(boston.soi)
x<-boston.c$CMEDV
y<-boston.c$CRIM
lee.test(x, y, lw, zero.policy=TRUE, alternative="less")
#Test with missing values
x[1:5]<-NA
y[3:7]<-NA
lee.test(x, y, lw, zero.policy=TRUE, alternative="less", na.action=na.omit)
```

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