This function calculates the false discovery rate (proportion of linked pairs that are false positives) in a sample given the sensitivity \(\eta\) and specificity \(\chi\) of the linkage criteria, and sample size \(M\). Assumptions about transmission and linkage (single or multiple) can be specified.
falsediscoveryrate(eta, chi, rho, M, R = NULL, assumption = "mtml")
eta | scalar or vector giving the sensitivity of the linkage criteria |
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chi | scalar or vector giving the specificity of the linkage criteria |
rho | scalar or vector giving the proportion of the final outbreak size that is sampled |
M | scalar or vector giving the number of cases sampled |
R | scalar or vector giving the effective reproductive number of the pathogen (default=NULL) |
assumption | a character vector indicating which assumptions about transmission and linkage criteria. Default =
|
scalar or vector giving the true discovery rate
Other discovery_rate:
truediscoveryrate()
John Giles, Shirlee Wohl, and Justin Lessler
# The simplest case: single-transmission, single-linkage, and perfect sensitivity falsediscoveryrate(eta=1, chi=0.9, rho=0.5, M=100, assumption='stsl')#> [1] 0.4999926# Multiple-transmission and imperfect sensitivity falsediscoveryrate(eta=0.99, chi=0.9, rho=1, M=50, R=1, assumption='mtsl')#> [1] 0.1373857# Small outbreak, larger sampling proportion falsediscoveryrate(eta=0.99, chi=0.95, rho=1, M=50, R=1, assumption='mtml')#> [1] 0.5427252# Large outbreak, small sampling proportion falsediscoveryrate(eta=0.99, chi=0.95, rho=0.5, M=1000, R=1, assumption='mtml')#> [1] 0.9805463