n.c.hp: Hypergeometric (HerdPlus) optimum sample size and cut-point...

In RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates optimum sample size and cut-point positives to achieve specified population sensitivity, for given population size and other parameters, all paramaters must be scalars

Usage

n.c.hp(N, sep = 0.95, c = 1, se, sp = 1, pstar, minSpH = 0.95)

Arguments

N	population size
sep	target population sensitivity
c	The maximum allowed cut-point number of positives to classify a cluster as positive, default=1, if positives < c result is negative, >= c is positive
se	test unit sensitivity
sp	test unit specificity, default=1
pstar	design prevalence as a proportion or integer (number of infected units)
minSpH	minimium desired population specificity

Value

a list of 3 elements, a dataframe with 1 row and six columns for the recommended sample size and corresponding values for population sensitivity (SeP), population specificity (SpP), N, c and pstar, a vector of SeP values and a vector of SpP values, for n = 1:N

Examples

# examples for n.c.hp
n.c.hp(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]]
tmp<- n.c.hp(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)

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