n.hypergeo: Hypergeometric sample size

Description Usage Arguments Value Examples

View source: R/freedom_functions_1.R

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

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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

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# 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.