# choynowski

0th

Percentile

##### Choynowski probability map values

Calculates Choynowski probability map values.

Keywords
spatial
choynowski(n, x, row.names=NULL, tol = .Machine$double.eps^0.5, legacy=FALSE) ##### Arguments n a numeric vector of counts of cases x a numeric vector of populations at risk row.names row names passed through to output data frame tol accumulate values for observed counts >= expected until value less than tol legacy default FALSE using vectorised alternating side ppois version, if true use original version written from sources and iterating down to tol ##### Value A data frame with columns: ##### References Choynowski, M (1959) Maps based on probabilities, Journal of the American Statistical Association, 54, 385--388; Cressie, N, Read, TRC (1985), Do sudden infant deaths come in clusters? Statistics and Decisions, Supplement Issue 2, 333--349; Bailey T, Gatrell A (1995) Interactive Spatial Data Analysis, Harlow: Longman, pp. 300--303. ##### See Also probmap ##### Aliases • choynowski ##### Examples example(auckland) res <- choynowski(auckland$M77_85, 9*auckland$Und5_81) resl <- choynowski(auckland$M77_85, 9*auckland$Und5_81, legacy=TRUE) all.equal(res, resl) rt <- sum(auckland$M77_85)/sum(9*auckland$Und5_81) ch_ppois_pmap <- numeric(length(auckland$Und5_81))
side <- c("greater", "less")
for (i in seq(along=ch_ppois_pmap)) {
ch_ppois_pmap[i] <- poisson.test(auckland$M77_85[i], r=rt, T=(9*auckland$Und5_81[i]), alternative=side[(res$type[i]+1)])$p.value
}
all.equal(ch_ppois_pmap, res$pmap) res1 <- probmap(auckland$M77_85, 9*auckland$Und5_81) table(abs(res$pmap - res1$pmap) < 0.00001, res$type)
lt005 <- (res$pmap < 0.05) & (res$type)
ge005 <- (res$pmap < 0.05) & (!res$type)
cols <- rep("white", length(lt005))
cols[lt005] <- grey(2/7)
cols[ge005] <- grey(5/7)
plot(auckland, col=cols)
legend("bottomleft", fill=grey(c(2,5)/7), legend=c("low", "high"), bty="n")

Documentation reproduced from package spdep, version 0.6-9, License: GPL (>= 2)

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