FitRsmRoc: Fit the radiological search model (RSM) to an ROC dataset

In dpc10ster/RJafroc: Artificial Intelligence Systems and Observer Performance

Fit the radiological search model (RSM) to an ROC dataset

Description

Fit an RSM-predicted ROC curve to a binned single-modality single-reader ROC dataset

Usage

FitRsmRoc(binnedRocData, lesDistr, trt = 1, rdr = 1)

Arguments

binnedRocData	A binned ROC dataset
lesDistr	The lesion distribution 1D array.
trt	The selected modality, default is 1
rdr	The selected reader, default is 1

Details

If dataset is FROC, first convert it to ROC, using DfFroc2Roc. MLE ROC algorithms require binned datasets. Use DfBinDataset to perform the binning prior to calling this function. In the RSM: (1) The (random) number of latent NLs per case is Poisson distributed with mean parameter lambda, and the corresponding ratings are sampled from N(0,1). The (2) The (random) number of latent LLs per diseased case is binomial distributed with success probability nu and trial size equal to the number of lesions in the case, and the corresponding ratings are sampled from N(mu,1). (3) A latent NL or LL is actually marked if its rating exceeds the lowest threshold zeta1. To avoid clutter error bars are only shown for the lowest and uppermost operating points. Because of the extra parameter, and the requirement to have five counts, the chi-square statistic often cannot be calculated.

Value

A list with the following elements

mu	The mean of the diseased distribution relative to the non-diseased one
lambda	The Poisson parameter describing the distribution of latent NLs per case
nu	The binomial success probability describing the distribution of latent LLs per diseased case
zetas	The RSM cutoffs, zetas or thresholds
AUC	The RSM fitted ROC-AUC
StdAUC	The standard deviation of AUC
NLLIni	The initial value of negative LL
NLLFin	The final value of negative LL
ChisqrFitStats	The chisquare goodness of fit results
covMat	The covariance matrix of the parameters
fittedPlot	A ggplot2 object containing the fitted operating characteristic along with the empirical operating points. Use print to display the object

References

Chakraborty DP (2006) A search model and figure of merit for observer data acquired according to the free-response paradigm. Phys Med Biol 51, 3449-3462.

Chakraborty DP (2006) ROC Curves predicted by a model of visual search. Phys Med Biol 51, 3463–3482.

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

## Test with included ROC data (some bins have zero counts)
lesDistr <- UtilLesDistr(dataset02)$Freq
retFit <- FitRsmRoc(dataset02, lesDistr)
## print(retFit$fittedPlot)

## Test with included degenerate ROC data
lesDistr <- UtilLesDistr(datasetDegenerate)$Freq
retFit <- FitRsmRoc(datasetDegenerate, lesDistr)

## Test with single interior point data
fp <- c(rep(1,7), rep(2, 3))
tp <- c(rep(1,5), rep(2, 5))
binnedRocData <- Df2RJafrocDataset(fp, tp)
lesDistr <- UtilLesDistr(binnedRocData)$Freq
retFit <- FitRsmRoc(binnedRocData, lesDistr)

## Test with two interior data points
fp <- c(rep(1,7), rep(2, 5), rep(3, 3))
tp <- c(rep(1,3), rep(2, 5), rep(3, 7))
binnedRocData <- Df2RJafrocDataset(fp, tp)
lesDistr <- UtilLesDistr(binnedRocData)$Freq
retFit <- FitRsmRoc(binnedRocData, lesDistr)


## Test with three interior data points
fp <- c(rep(1,12), rep(2, 5), rep(3, 3), rep(4, 5))
tp <- c(rep(1,3), rep(2, 5), rep(3, 7), rep(4, 10))
binnedRocData <- Df2RJafrocDataset(fp, tp)
lesDistr <- UtilLesDistr(binnedRocData)$Freq
retFit <- FitRsmRoc(binnedRocData, lesDistr)

## test for TONY data, i = 2 and j = 3 
## only case permitting chisqure calculation
lesDistr <- UtilLesDistr(dataset01)$Freq
rocData <- DfFroc2Roc(dataset01)
retFit <- FitRsmRoc(rocData, lesDistr, trt = 2, rdr = 3)
## print(retFit$fittedPlot)
## retFit$ChisqrFitStats

dpc10ster/RJafroc documentation built on June 13, 2025, 10:07 p.m.