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

In RSurveillance: Design and Analysis of Disease Surveillance Activities

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

# 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)

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