pstar.calc: Design prevalence back-calculation

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates design prevalence required for given sample size and desired population-level sensitivity, assuming imperfect test sensitivity, perfect test specificity and representative sampling

Usage

pstar.calc(N = NA, n, sep, se)

Arguments

N	populaton size if known (scalar or vector of same length as n)
n	sample size (scalar or vector)
sep	desired population sensitivity (scalar or vector of same length as n)
se	unit sensitivity (scalar or vector of same length as n)

Value

vector of design prevalence values

Examples

# examples of pstar.calc- checked
pstar.calc(NA, 280, 0.95, 0.98)
pstar.calc(500, 250, sep=0.95, se=1)
pstar.calc(N=c(100, 500, 1000, 5000, 10000, 100000, NA), n=30, sep=0.95, se=1)
pstar.calc(500, n=30, sep=0.95, se=c(0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1))

RSurveillance documentation built on July 2, 2020, 2:33 a.m.