falsediscoveryrate {phylosamp}

Calculate false discovery rate of a sample

Description

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.

Usage

falsediscoveryrate(eta, chi, rho, M, R = NULL, assumption = "mtml")

Arguments

eta

scalar or vector giving the sensitivity of the linkage criteria

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 = 'mtml'. Accepted arguments are:

1. 'stsl' for the single-transmission single-linkage assumption (prob_trans_stsl()).

2. 'mtsl' for the multiple-transmission single-linkage assumption (prob_trans_mtsl()).

3. 'mtml' for the multiple-transmission multiple-linkage assumption (prob_trans_mtml()).

Value

scalar or vector giving the true discovery rate

Author(s)

John Giles, Shirlee Wohl, and Justin Lessler

See Also

Other discovery_rate: truediscoveryrate()

Examples

# The simplest case: single-transmission, single-linkage, and perfect sensitivity
falsediscoveryrate(eta=1, chi=0.9, rho=0.5, M=100, assumption='stsl')

# Multiple-transmission and imperfect sensitivity
falsediscoveryrate(eta=0.99, chi=0.9, rho=1, M=50, R=1, assumption='mtsl')

# Small outbreak, larger sampling proportion
falsediscoveryrate(eta=0.99, chi=0.95, rho=1, M=50, R=1, assumption='mtml')

# Large outbreak, small sampling proportion
falsediscoveryrate(eta=0.99, chi=0.95, rho=0.5, M=1000, R=1, assumption='mtml')