n.c.freecalc function

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.hp n.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)