BB, BW and Jtot join count statistic for k-coloured factors
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) "print"(x, ...)
- a factor of the same length as the neighbours and weights objects in listw
listwobject created for example by
- if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA
- 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)
- should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use
- object to be printed
- arguments to be passed through for printing
A matrix with class
jcmultiwith 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.
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 quatitative data, Wiley, pp. 158--170.
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) # 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)