sep.rb2.hypergeo: Hypergeometric risk-based population sensitivity for 2 risk...

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates risk-based population sensitivity for two risk factors, using hypergeometric approximation method (assumes a known population size)

Usage

sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)

Arguments

pstar	design prevalence (scalar)
rr1	relative risks for first level risk factor (vector of values corresponding to the number of risk strata)
rr2	relative risks for second level risk factor, matrix, rows = levels of rr1, cols = levels of rr2
N	matrix of population size for each risk group (rows = levels of rr1, cols = levels of rr2)
n	matrix of number tested (sample size) for each risk group (rows = levels of rr1, cols = levels of rr2)
se	test unit sensitivity (scalar)

Value

list of 6 elements, a scalar of population-level sensitivity a matrix of EPI values, a vector of corresponding Adjusted risks for the first risk factor and a matrix of adjusted risks for the second risk factor, a vector of population proportions for the first risk factor and a matrix of population proportions for the second risk factor

Examples

# examples for sep.rb2.hypergeo
pstar<- 0.01
rr1<- c(3, 1)
rr2<- rbind(c(4,1), c(4,1))
N<- rbind(c(100, 500), c(300, 1000))
n<- rbind(c(50, 20), c(20, 10))
se<- 0.8
sep.rb2.hypergeo(pstar, rr1, rr2, N, n, se)

RSurveillance documentation built on July 2, 2020, 2:33 a.m.