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)

### 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

### 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

    data(auckland)
    res <- choynowski(auckland$Deaths.1977.85, 9*auckland$Under.5.1981)
    res1 <- probmap(auckland$Deaths.1977.85, 9*auckland$Under.5.1981)
    table(abs(res$pmap - res1$pmap) < 0.00001, res$type)
    plot(auckpolys, forcefill=FALSE)
    lt005 <- (res$pmap < 0.05) & (res$type)
    ge005 <- (res$pmap < 0.05) & (!res$type)
    plot(subset(auckpolys, lt005), add=TRUE, col=grey(2/7), forcefill=FALSE)
    plot(subset(auckpolys, ge005), add=TRUE, col=grey(5/7), forcefill=FALSE)
    legend(c(70,90), c(70,95), fill=grey(c(2,5)/7),
     legend=c("low", "high"), bty="n")

[Package spdep version 0.3-25 Index]