npv: Compute negative predictive value

In jpfrench81/neastbenchmark: Benchmark Data for Disease Clusters

Compute negative predictive value

Description

Compute the empirical negative predictive value (npv) rate of a method from a series of tests. In this context, the npv is the average proportion of the population that lies outside the most likely cluster that intersects the true null regoin. 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. See Details.

Usage

npv(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).

Details

In this context, the npv is the proportion of the population lying outside the most likely cluster that intersects the true null regoins, averaged over all tests.

Value

A vector of npv values.

Examples

tnull = 1:99
tdata = list(list(tmax = 96, mlc = c(50, 51)),
             list(tmax = 101, mlc = c(48, 57)))
npv(tnull, tdata, 50, pop = rep(10, 100))

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