Freecalc optimum sample size and cut-point number of positives
Freecalc optimum sample size and cut-point number of positives
Calculates optimum sample size and cut-point number of positives to achieve specified population sensitivity, for given population size and other parameters, using freecalc algorithm, all paramaters must be scalars
n.c.freecalc(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
Returns
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.hpn.c.freecalc(120,0.95,c=5,se=0.9,sp=0.99,pstar=0.1, minSpH=0.9)[[1]]n.c.freecalc(65,0.95,c=5,se=0.95,sp=0.99,pstar=0.05, minSpH=0.9)