sep.freecalc: FreeCalc population sensitivity for imperfect test

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates population sensitivity for a finite population and allowing for imperfect test sensitivity and specificity, using Freecalc method

Usage

sep.freecalc(N, n, c = 1, se, sp = 1, pstar)

Arguments

N	population size (scalar)
n	sample size (scalar)
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)
se	test unit sensitivity (scalar)
sp	test unit specificity, default=1 (scalar)
pstar	design prevalence as a proportion - assumed or target prevalence for detection of disease in the population (scalar)

Value

population-level sensitivity

Examples

# examples of sep.freecalc
sep.freecalc(150, 30, 2, 0.9, 0.98, 0.1)
sep.freecalc(150, 30, 1, 0.9, 0.98, 0.1)

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