| n.hp {RSurveillance} | R Documentation |
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
# 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)