Simulates an MRMC uncorrelated FROC dataset using the RSM

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

SimulateFrocDataset(
  mu,
  lambda,
  nu,
  zeta1,
  I,
  J,
  K1,
  K2,
  perCase,
  seed = NULL,
  deltaMu = 0
)

Arguments

mu

mu parameter of the RSM

lambda

RSM lambda parameter

nu

RSM nu parameter

zeta1

Lowest reporting threshold

I

Number of treatments, default is 1

J

Number of readers

K1

Number of non-diseased cases

K2

Number of diseased cases

perCase

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

seed

Initial seed for random number generator, default NULL, for random seed.

deltaMu

Inter-modality increment in mu, default zero

Value

An FROC dataset.

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.

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)