ppv: Compute positive predictive value

In jpfrench81/neastbenchmark: Benchmark Data for Disease Clusters

Compute positive predictive value

Description

Compute the empirical positive predictive value (ppv) of a method from a series of tests. In this context, the ppv is the average proportion of the true hostpot that intersects the most likely cluster. The function requires the null test statistics, the results from the observed data sets (i.e., the maximum test statistic and most likely cluster from each data set), the true hotspot locations, and the vector of population sizes for each region.

Usage

ppv(tnull, tdata, hotspot, pop, alpha = c(0.05, 0.01))

precision(tnull, tdata, hotspot, pop, alpha = c(0.05, 0.01))

Arguments

tnull	The set of null test statistics
tdata	The list of maximum test statistics (tmax) and most likely cluster (mlc) for each simulated data set.
hotspot	A vector containing the hotspot indices for the current data set.
pop	A vector with the populations associated with each region.
alpha	The type I error rate. Default is c(0.05, 0.01).

Value

A vector of ppvs.

Examples

tnull = 1:99
tdata = list(list(tmax = 96, mlc = c(50, 51)),
             list(tmax = 101, mlc = c(48, 57)))
ppv(tnull, tdata, 50, pop = rep(1, 100))

jpfrench81/neastbenchmark documentation built on July 26, 2023, 3:07 p.m.