R/vartrack_prob_detect_xsect.R

In HopkinsIDD/phylosamp: Sample Size Calculations for Molecular and Phylogenetic Studies

##' Calculate probability of detecting a variant assuming cross-sectional sampling
##'
##' This function calculates the probability of detecting the presence of a variant
##' given a sample size and assuming a single, cross-sectional sample of detected infections.
##'
##' @param p_v1 variant prevalence (proportion)
##' @param n sample size
##' @param omega probability of sequencing (or other characterization) success
##' @param c_ratio coefficient of detection ratio, calculated as the ratio of the coefficients of variant 1 to variant 2. Default = 1 (no bias)
##' @return scalar of expected sample size
##'
##' @author Shirlee Wohl, Elizabeth C. Lee, Bethany L. DiPrete, and Justin Lessler
##'
##' @examples
##' vartrack_prob_detect_xsect(p_v1 = 0.02, n = 100, omega = 0.8, c_ratio = 1)
##'
##' @family variant detection functions
##' @family variant tracking functions
##'
##' @export


vartrack_prob_detect_xsect <- function(p_v1, n, omega, c_ratio = 1) {
  if (!all(is.numeric(p_v1), p_v1 > 0 & p_v1 < 1)) stop("Variant prevalence must be numeric and between 0 and 1.")
  if (!all(is.numeric(n), n > 0)) stop("Sample size must be numeric and greater than 0.")
  if (!all(is.numeric(omega), omega > 0 & omega <= 1)) stop("Probability of characterization success must be numeric and between 0 and 1.")
  if (!all(is.numeric(c_ratio), c_ratio > 0)) stop("Coefficient of detection ratio must be numeric and greater than 0.")

  p_star <- varfreq_obs_freq(p_v1, c_ratio)
  n_star <- n * omega
  prob <- 1 - ((1-p_star)^n_star)
  return(prob)
}