R: Choynowski probability map values

choynowski {spdep} | R Documentation

## Choynowski probability map values

### Description

Calculates Choynowski probability map values.

### Usage

    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 Roger.Bivand@nhh.no

### 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

    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")

[Package spdep version 1.2-2 Index]