RJafroc (version 1.0.1)

SimulateFrocDataset: Simulates an MRMC uncorrelated FROC dataset using the RSM

Description

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

Usage

SimulateFrocDataset(mu, lambda, nu, zeta1, I, J, K1, K2, lesionNum)

Arguments

mu

The intrinsic mu parameter of the RSM

lambda

The intrinsic lambda parameter of the RSM (not the physical parameter)

nu

The intrinsic nu parameter of the RSM (not the physical parameter)

zeta1

The lowest reporting threshold

I

The number of modalities

J

The number of readers

K1

The number of non-diseased cases

K2

The number of diseased cases

lesionNum

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

Value

The return value is 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.crcpress.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840

Examples

# NOT RUN {
set.seed(1) 
K1 <- 5;K2 <- 7;
maxLL <- 2;lesionNum <- floor(runif(K2, 1, maxLL + 1))
mu <- 1;lambda <- 1;nu <- 1 ;zeta1 <- -1
I <- 2; J <- 5

frocDataRaw <- SimulateFrocDataset(
  mu = mu, lambda = lambda, nu = nu, zeta1 = zeta1,
  I = I, J = J, K1 = K1, K2 = K2, lesionNum = lesionNum )
  
## plot the data
ret <- PlotEmpiricalOperatingCharacteristics(frocDataRaw, trts= 1, 
   rdrs = 1, opChType = "FROC")
print(ret$Plot)

# }