# n.hypergeo: Hypergeometric sample size In RSurveillance: Design and Analysis of Disease Surveillance Activities

## Description

Calculates sample size for demonstrating freedom or detecting disease using hypergeometric approximation and assuming imperfect test sensitivity, perfect test specificity and representative sampling

## Usage

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

## Arguments

 `sep` desired population sensitivity (scalar or vector) `N` population size (scalar or vector of same length as sep) `d` expected number of infected units in population, = design prevalence*N rounded to next integer (scalar or vector of same length as sep) `se` unit sensitivity, default = 1 (scalar or vector of same length as sep)

## Value

vector of sample sizes, NA if n>N

## Examples

 ```1 2 3 4 5 6``` ```# examples for n.hypergeo - checked n.hypergeo(0.95, N=100, d=1, se = 0.95) n.hypergeo(sep=0.95, N=c(100, 200, 500, 1000, 10000), d=ceiling(0.01*c(100, 200, 500, 1000, 10000))) n.hypergeo(c(0.5, 0.8, 0.9, 0.95), N=100, d=5) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10)) n.hypergeo(0.95, N=80, d=c(1, 2, 5, 10), se = 0.8) ```

### Example output

```[1] 100
[1]  95 156 226 259 296
[1] 13 28 37 46
[1] 76 63 37 21
[1] NA 78 46 26
```

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