sep.hypergeo: Hypergeometric Population sensitivity

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates population sensitivity for detecting disease, assuming imperfect test sensitivity, perfect test specificity and representative sampling, using hypergeometric approximation (assumes known population size)

Usage

sep.hypergeo(N, n, d, se = 1, dig = 5)

Arguments

N	population size, scalar or vector of same length as n
n	sample size (number tested), scalar or vector
d	expected number of infected units in population (=design prevalence*N rounded to next integer)
se	unit sensitivity of test (proportion), scalar or vector of same length as n
dig	number of digits for rounding of results

Value

a vector of population-level sensitivities. if all n <= corresponding N then vector is numeric, otherwise vector is character and elements where n>N are recorded as such

Examples

# examples for sep.hypergeo - checked
sep.hypergeo(N=100, n=50, d=1, se = 0.92)
inf<- 1:5
sens<- 0.8
sep.hypergeo(N=100, n=50, d=inf, se=sens)
N<- c(10, 50, 100, 250, 500)
sep.hypergeo(se=0.8, N=N, n=c(5, 25, 50, 125, 250), d = ceiling(0.01*N))

Example output

[1] 0.46
[1] 0.40000 0.64000 0.78400 0.87040 0.92224
[1] 0.40000 0.40000 0.40000 0.78400 0.92224

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