Probability mapping for rates

The function returns a data frame of rates for counts in populations at risk with crude rates, expected counts of cases, relative risks, and Poisson probabilities.

probmap(n, x, row.names=NULL, alternative="less")

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

alternative

default “less”, may be set to “greater”

Details

The function returns a data frame, from which rates may be mapped after class intervals have been chosen. The class intervals used in the examples are mostly taken from the referenced source.

Value

raw

raw (crude) rates

expCount

expected counts of cases assuming global rate

relRisk

relative risks: ratio of observed and expected counts of cases multiplied by 100

pmap

Poisson probability map values: probablility of getting a more ``extreme'' count than actually observed - one-tailed, default alternative observed “less” than expected

References

Bailey T, Gatrell A (1995) Interactive Spatial Data Analysis, Harlow: Longman, pp. 300--303.

Author

Roger Bivand Roger.Bivand@nhh.no

See also

EBest, EBlocal, ppois

Examples

auckland <- st_read(system.file("shapes/auckland.shp", package="spData")[1], quiet=TRUE)
res <- probmap(auckland$M77_85, 9*auckland$Und5_81)
rt <- sum(auckland$M77_85)/sum(9*auckland$Und5_81)
ppois_pmap <- numeric(length(auckland$Und5_81))
for (i in seq(along=ppois_pmap)) {
ppois_pmap[i] <- poisson.test(auckland$M77_85[i], r=rt,
  T=(9*auckland$Und5_81[i]), alternative="less")$p.value
all.equal(ppois_pmap, res$pmap)
}
res$id <- 1:nrow(res)
auckland$id <- res$id <- 1:nrow(res)
auckland_res <- merge(auckland, res, by="id")
plot(auckland_res[, "raw"], main="Crude (raw) estimates")

plot(auckland_res[, "relRisk"], main="Standardised mortality ratios")

plot(auckland_res[, "pmap"], main="Poisson probabilities",
 breaks=c(0, 0.05, 0.1, 0.5, 0.9, 0.95, 1))