# choynowski: Choynowski probability map values In spdep: Spatial Dependence: Weighting Schemes, Statistics

 choynowski 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.

`probmap`

### Examples

```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")
```

spdep documentation built on March 7, 2023, 7:27 p.m.