vartrack_samplesize_detect {phylosamp}

Calculate sample size needed for variant detection given a desired probability of detection

Description

This function calculates the sample size needed for detecting the presence of a variant given a desired probability of detection and sampling strategy.

Usage

vartrack_samplesize_detect(
  prob,
  t = NA,
  p_v1 = NA,
  omega,
  p0_v1 = NA,
  r_v1 = NA,
  c_ratio = 1,
  sampling_freq
)

Arguments

prob

desired probability of detection

t

time step number (e.g., days) at which variant should be detected by. Default = NA (either 't' or 'p_v1' should be provided, not both)

p_v1

the desired prevalence to detect a variant by. Default = NA (either 't' or 'p_v1' should be provided, not both)

omega

probability of sequencing (or other characterization) success

p0_v1

initial variant prevalence (# introductions / infected population size)

r_v1

logistic growth rate

c_ratio

coefficient of detection ratio, calculated as the ratio of the coefficients of variant 1 to variant 2. Default = 1 (no bias)

sampling_freq

the sampling frequency (must be either 'xsect' or 'cont')

Value

scalar of expected sample size

Author(s)

Shirlee Wohl, Elizabeth C. Lee, Bethany L. DiPrete, and Justin Lessler

See Also

Other variant detection functions: vartrack_prob_detect_cont(), vartrack_prob_detect_xsect(), vartrack_prob_detect(), vartrack_samplesize_detect_cont(), vartrack_samplesize_detect_xsect()

Other variant tracking functions: vartrack_cod_ratio(), vartrack_prob_detect_cont(), vartrack_prob_detect_xsect(), vartrack_prob_detect(), vartrack_prob_prev_xsect(), vartrack_prob_prev(), vartrack_samplesize_detect_cont(), vartrack_samplesize_detect_xsect(), vartrack_samplesize_prev_xsect(), vartrack_samplesize_prev()

Examples

# Cross-sectional sampling
vartrack_samplesize_detect(p_v1 = 0.1, prob = 0.95, omega = 0.8,
                           c_ratio = 1, sampling_freq = 'xsect')

# Periodic sampling
vartrack_samplesize_detect(prob = 0.95, t = 30, omega = 0.8, p0_v1 = 1/10000,
                           r_v1 = 0.1, c_ratio = 1, sampling_freq = 'cont')