# choynowski: Choynowski probability map values In r-spatial/spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

## Description

Calculates Choynowski probability map values.

## Usage

 `1` ```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:

 `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

## Author(s)

Roger Bivand [email protected]

## 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`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```auckland <- st_read(system.file("shapes/auckland.shp", package="spData"), 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") ```

r-spatial/spdep documentation built on April 6, 2019, 2:16 a.m.