This function calculates the expected number of observed pairs in the sample that are linked by the linkage criteria. The function requires 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.
exp_links(eta, chi, rho, M, R = NULL, assumption = "mtml")
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 =
|
scalar or vector giving the expected number of observed links in the sample
Other obs_pairs:
obs_pairs_mtml()
,
obs_pairs_mtsl()
,
obs_pairs_stsl()
John Giles, Shirlee Wohl, and Justin Lessler
# The simplest case: single-transmission, single-linkage, and perfect sensitivity exp_links(eta=1, chi=0.9, rho=0.5, M=100, assumption='stsl')#>#> [1] 49.99926# Multiple-transmission and imperfect sensitivity exp_links(eta=0.99, chi=0.9, rho=1, M=50, R=1, assumption='mtsl')#>#> [1] 57.3837# Small outbreak, larger sampling proportion exp_links(eta=0.99, chi=0.95, rho=1, M=50, R=1, assumption='mtml')#>#> [1] 108.25# Large outbreak, small sampling proportion exp_links(eta=0.99, chi=0.95, rho=0.05, M=1000, R=1, assumption='mtml')#>#> [1] 25022