# sep: Population sensitivity In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates population sensitivity using appropriate method, depending on whether or not N provided (hypergeometric if N provided, binomial otherwise), assuming perfect test specificity and representative sampling

## Usage

 `1` ```sep(N = NA, n, pstar, se = 1, dig = 5) ```

## Arguments

 `N` population size, NA or vector of same length as n `n` sample size (number tested), scalar or vector `pstar` design prevalence as a proportion or integer, scalar or vector of same length as n `se` unit sensitivity, scalar or vector of same length as n `dig` number of digits for rounding of results

## Value

a vector of population-level sensitivities

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# examples for sep - checked sep(n=300, pstar=0.01, se=1) sep(NA, 300, 0.01, 1) sep(10000, 150, 0.02, 1) sep(n=1:100, pstar = 0.05, se=0.95) N<- seq(30, 100, by = 5) se<- 0.95 pstar<- 0.1 n<- rep(30, length(N)) sep(N, n, pstar, se = se) sep(rep(100, 10), seq(10, 100, by = 10), pstar = 1, se=0.99) N<- c(55, 134, NA, 44, 256) n<- c(15, 30, 28, 15, 33) sep(N, n, 0.1, 0.95) ```

RSurveillance documentation built on May 29, 2017, 11:52 p.m.