SimulateFrocDataset {RJafroc}

Simulates an MRMC uncorrelated FROC dataset using the RSM

Description

Simulates an uncorrelated MRMC FROC dataset for specified numbers of readers and treatments

Usage

SimulateFrocDataset(mu, lambda, nu, zeta1, I, J, K1, K2, perCase, seed = NULL)

Arguments

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

perCase

A K2 length array containing the numbers of lesions per diseased case

seed

The initial seed for the random number generator, the default is NULL, as if no seed has been specified.

Details

See book chapters on the Radiological Search Model (RSM) for details. In this code correlations between ratings on the same case are assumed to be zero.

Value

The return value is an FROC dataset.

References

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

Examples

set.seed(1) 
K1 <- 5;K2 <- 7;
maxLL <- 2;perCase <- floor(runif(K2, 1, maxLL + 1))
mu <- 1;lambda <- 1;nu <- 0.99 ;zeta1 <- -1
I <- 2; J <- 5

frocDataRaw <- SimulateFrocDataset(
  mu = mu, lambda = lambda, nu = nu, zeta1 = zeta1,
  I = I, J = J, K1 = K1, K2 = K2, perCase = perCase )
  
## plot the data
ret <- PlotEmpiricalOperatingCharacteristics(frocDataRaw, opChType = "FROC")
## print(ret$Plot)