RSurveillance (version 0.2.1)

n.hp: Hypergeometric (HerdPlus) sample size for finite population and specified cut-point number of positives

Description

Calculates sample size to achieve specified population sensitivity with population specificity >= specified minimum value, for given population size, cut-point number of positives and other parameters, all paramaters must be scalars

Usage

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

Arguments

N

population size

sep

target population sensitivity

c

The 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 2 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 and a dataframe of n rows with SeP and SpP values for each value of n up to the recommended value. Returns sample size for maximum achievable sep if it is not possible to achieve target sep AND SpP>= minSpH.

Examples

# NOT RUN {
# examples for n.hp
n.hp(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]]
n.hp(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)
# }