Create a Hui-Walter model based on paired test data for an arbitrary number of tests and populations
Create a Hui-Walter model based on paired test data for an arbitrary number of tests and populations
template_huiwalter( testdata, outfile = "huiwalter_model.txt", covariance = FALSE, se_priors = "dbeta(1,1)", sp_priors = "dbeta(1,1)" )
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) |
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.