RSurveillance (version 0.2.1)

sep.binom.imperfect: Binomial population sensitivity for imperfect test

Description

Calculates population sensitivity for a large or unknown population and allowing for imperfect test sensitivity and specificity, using Binomial distribution an allowing for a variable cut-point number of positives to classify as positive

Usage

sep.binom.imperfect(n, c = 1, se, sp = 1, pstar)

Arguments

n

sample size (scalar or vector)

c

The cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive (scalar or vector of same length as n)

se

test unit sensitivity (scalar or vector of same length as n)

sp

test unit specificity, default=1 (scalar or vector of same length as n)

pstar

design prevalence as a proportion (scalar or vector of same length as n)

Value

a vector of population-level sensitivities

Examples

# NOT RUN {
# examples for sep.imperfect.binom
sep.binom.imperfect(1:10*5, 2, 0.95, 0.98, 0.1)
sep.binom.imperfect(50, 1:5, 0.95, 0.98, 0.1)
sep.binom.imperfect(30, 2, 0.9, 0.98, 0.1)
sep.binom.imperfect(30, 1, 0.9, 0.98, 0.1)
# }