choynowski | R Documentation |

Calculates Choynowski probability map values.

choynowski(n, x, row.names=NULL, tol = .Machine$double.eps^0.5, legacy=FALSE)

`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 |

A data frame with columns:

`pmap` |
Poisson probability map values: probablility of getting a more “extreme” count than actually observed, one-tailed with less than expected and more than expected folded together |

`type` |
logical: TRUE if observed count less than expected |

Roger Bivand Roger.Bivand@nhh.no

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.

`probmap`

auckland <- st_read(system.file("shapes/auckland.shp", package="spData")[1], quiet=TRUE) auckland.nb <- poly2nb(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$type) cols <- rep("nonsig", length(lt005)) cols[lt005] <- "low" cols[ge005] <- "high" auckland$cols <- factor(cols) plot(auckland[,"cols"], main="Probability map")

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