pstar.calc {RSurveillance} | R Documentation |
Design prevalence back-calculation
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))
[Package RSurveillance version 0.2.1 Index]