n.freecalc: Freecalc sample size for a finite population and specified...

In evansergeant/RSurveillance: Design and Analysis of Disease Surveillance Activities

Description

Calculates sample size required for a specified population sensitivity, for a given population size, cut-point number of positives and other parameters, using Freecalc algorithm. All paramaters must be scalars

Usage

n.freecalc(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

Examples

# examples for n.freecalc
n.freecalc(65,0.95,c=1,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]]
n.freecalc(65,0.95,c=2,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)[[1]]
n.freecalc(65,0.95,c=3,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)

evansergeant/RSurveillance documentation built on Nov. 8, 2019, 1:32 a.m.