FitCbmRoc: Fit the contaminated binormal model (CBM) to selected...

In dpc10ster/rjafroc-master: Artificial Intelligence Systems and Observer Performance

Fit the contaminated binormal model (CBM) to selected modality and reader in an ROC dataset

Description

Fit the CBM-predicted ROC curve for specified modality and reader

Usage

FitCbmRoc(dataset, trt = 1, rdr = 1)

Arguments

dataset	The dataset containing the data
trt	The desired modality, default is 1
rdr	The desired reader, default is 1

Details

In CBM ratings from diseased cases are sampled from a mixture distribution with two components: (1) distributed normal with mean mu and unit variance with integrated area alpha, and (2) from a unit-normal distribution with integrated area 1-alpha. Ratings for non-diseased cases are sampled from a unit-normal distribution. The ChisqrFitStats consists of a list containing the chi-square value, the p-value and the degrees of freedom.

Value

A list with the following elements:

mu	The mean of the visible diseased distribution (the non-diseased) has zero mean
alpha	The proportion of diseased cases where the disease is visible
zetas	The cutoffs, zetas or thresholds
AUC	The AUC of the fitted ROC curve
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

Note

This algorithm is very robust, especially compared to the binormal model.

References

Dorfman DD, Berbaum KS (2000) A contaminated binormal model for ROC data: Part II. A formal model, Acad Radiol, 7:6, 427–437.

Examples

## CPU time 8.7 sec on Ubuntu (#13)
## Test with included ROC data
retFit <- FitCbmRoc(dataset02);## print(retFit$fittedPlot)

## Test with included degenerate ROC data (yes! CBM can fit such data)
retFit <- FitCbmRoc(datasetDegenerate);## print(retFit$fittedPlot)

## Test with single interior point data
fp <- c(rep(1,7), rep(2, 3))
tp <- c(rep(1,5), rep(2, 5))
dataset <- Df2RJafrocDataset(fp, tp)
retFit <- FitCbmRoc(dataset);## print(retFit$fittedPlot)

## 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))
dataset <- Df2RJafrocDataset(fp, tp)
retFit <- FitCbmRoc(dataset);
## print(retFit$fittedPlot)

## Test with included ROC data (some bins have zero counts)
retFit <- FitCbmRoc(dataset02, 2, 1);## print(retFit$fittedPlot)

## Test with TONY data for which chisqr can be calculated
ds <- DfFroc2Roc(dataset01)
retFit <- FitCbmRoc(ds, 2, 3);## print(retFit$fittedPlot)
retFit$ChisqrFitStats

dpc10ster/rjafroc-master documentation built on Jan. 31, 2024, 1:07 p.m.