runjags (version 2.2.2-1)

template_huiwalter: Create a Hui-Walter model based on paired test data for an arbitrary number of tests and populations

Description

Create a Hui-Walter model based on paired test data for an arbitrary number of tests and populations

Usage

template_huiwalter(
  testdata,
  outfile = "huiwalter_model.txt",
  covariance = FALSE,
  se_priors = "dbeta(1,1)",
  sp_priors = "dbeta(1,1)",
  cov_as_cor = FALSE
)

Arguments

testdata

the input paired test data, where each column name corresponds to a test result - except possibly "ID" which is ignored, and "Population" indicating a population identifier for that row. Each row must represent test results from the same individual either as logical or a factor with two levels (and where the first level indicates a negative test result). Data may be missing at random (except for Population).

outfile

the name of the text file to save the model representation

covariance

should covariance terms be activated or omitted?

se_priors

the priors to use for sensitivity parameters (can be adjusted in the model once it is generated)

sp_priors

the priors to use for specificity parameters (can be adjusted in the model once it is generated)

cov_as_cor

option for the prior for covariance terms to be put on the correlation rather than covariance directly

Examples

N <- 600
status <- rbinom(N, 1, rep(c(0.25,0.5,0.75), each=N/3))
testdata <- data.frame(Population = rep(1:3, each=N/3),
    Test1 = rbinom(N, 1, status*0.75 + (1-status)*0.05),
    Test2 = rbinom(N, 1, status*0.75 + (1-status)*0.05),
    Test3=rbinom(N, 1, status*0.75 + (1-status)*0.05)
)
template_huiwalter(testdata, outfile="huiwalter_model.txt", covariance=TRUE)
if (FALSE) {
results <- run.jags("huiwalter_model.txt")
}
unlink("huiwalter_model.txt")