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

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

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

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

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.

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

# \donttest{
## 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
# }