Description Usage Arguments Value Examples

View source: R/freedom_functions_1.R

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

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

`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) |

vector of sample sizes, NA if n>N

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)
``` |

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

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