View source: R/SimulateLrocDataset.R
SimulateLrocDataset | R Documentation |
Simulates an uncorrelated LROC dataset for specified numbers of readers and treatments
SimulateLrocDataset(mu, lambda, nu, zeta1, I, J, K1, K2, lesionVector)
mu |
The mu parameter of the RSM |
lambda |
The RSM lambda parameter |
nu |
The RSM nu parameter |
zeta1 |
The lowest reporting threshold |
I |
The number of treatments |
J |
The number of readers |
K1 |
The number of non-diseased cases |
K2 |
The number of diseased cases |
lesionVector |
A K2 length array containing the numbers of lesions per diseased case |
See book chapters on the Radiological Search Model (RSM) for details. The approach is to first simulate an FROC dataset and then convert it to an Lroc dataset. The correlations between FROC ratings on the same case are assumed to be zero.
An LROC dataset.
Chakraborty DP (2017) Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples, CRC Press, Boca Raton, FL. https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840
set.seed(1)
K1 <- 5; K2 <- 5; mu <- 2; lambda <- 1; lesionVector <- rep(1, 5); nu <- 0.8; zeta1 <- -3
frocData <- SimulateFrocDataset(mu, lambda, nu, zeta1, I = 2, J = 5, K1, K2, lesionVector)
lrocData <- DfFroc2Lroc(frocData)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.