RSurveillance (version 0.2.1)

n.freecalc: Freecalc sample size for a finite population and specified cut-point number of positives

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

# NOT RUN {
# 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)
# }