sep.hypergeo {RSurveillance} | R Documentation |
Hypergeometric Population sensitivity
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))
[Package RSurveillance version 0.2.1 Index]