joincount.multi | R Documentation |

A function for tallying join counts between same-colour and different colour spatial objects, where neighbour relations are defined by a weights list. Given the global counts in each colour, expected counts and variances are calculated under non-free sampling, and a z-value reported. Since multiple tests are reported, no p-values are given, allowing the user to adjust the significance level applied. Jtot is the count of all different-colour joins.

joincount.multi(fx, listw, zero.policy = FALSE, spChk = NULL, adjust.n=TRUE) ## S3 method for class 'jcmulti' print(x, ...)

`fx` |
a factor of the same length as the neighbours and weights objects in listw |

`listw` |
a |

`zero.policy` |
if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA |

`adjust.n` |
default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted consistently (up to and including spdep 0.3-28 the adjustment was inconsistent - thanks to Tomoki NAKAYA for a careful bug report) |

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

`x` |
object to be printed |

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

A matrix with class `jcmulti`

with row and column names for observed and expected counts, variance, and z-value.

The derivation of the test (Cliff and Ord, 1981, p. 18) assumes that the weights matrix is symmetric. For inherently non-symmetric matrices, such as k-nearest neighbour matrices, `listw2U()`

can be used to make the matrix symmetric. In non-symmetric weights matrix cases, the variance of the test statistic may be negative.

Roger Bivand Roger.Bivand@nhh.no

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, p. 20; Upton, G., Fingleton, B. 1985 Spatial data analysis by example: point pattern and qualitative data, Wiley, pp. 158–170.

`joincount.test`

data(oldcol) HICRIME <- cut(COL.OLD$CRIME, breaks=c(0,35,80), labels=c("low","high")) names(HICRIME) <- rownames(COL.OLD) joincount.multi(HICRIME, nb2listw(COL.nb, style="B")) ## Not run: data(hopkins, package="spData") image(1:32, 1:32, hopkins[5:36,36:5], breaks=c(-0.5, 3.5, 20), col=c("white", "black")) box() hopkins.rook.nb <- cell2nb(32, 32, type="rook") unlist(spweights.constants(nb2listw(hopkins.rook.nb, style="B"))) hopkins.queen.nb <- cell2nb(32, 32, type="queen") hopkins.bishop.nb <- diffnb(hopkins.rook.nb, hopkins.queen.nb, verbose=FALSE) hopkins4 <- hopkins[5:36,36:5] hopkins4[which(hopkins4 > 3, arr.ind=TRUE)] <- 4 hopkins4.f <- factor(hopkins4) table(hopkins4.f) joincount.multi(hopkins4.f, nb2listw(hopkins.rook.nb, style="B")) cat("replicates Upton & Fingleton table 3.4 (p. 166)\n") joincount.multi(hopkins4.f, nb2listw(hopkins.bishop.nb, style="B")) cat("replicates Upton & Fingleton table 3.6 (p. 168)\n") joincount.multi(hopkins4.f, nb2listw(hopkins.queen.nb, style="B")) cat("replicates Upton & Fingleton table 3.7 (p. 169)\n") ## End(Not run)

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