FitCbmRoc | R Documentation |
Fit the CBM-predicted ROC curve for specified treatment and reader
FitCbmRoc(dataset, trt = 1, rdr = 1)
dataset |
The dataset containing the data |
trt |
The desired treatment, default is 1 |
rdr |
The desired reader, default is 1 |
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.
The return value is 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 |
This algorithm is very robust, much more so than the binormal model.
Dorfman DD, Berbaum KS (2000) A contaminated binormal model for ROC data: Part II. A formal model, Acad Radiol, 7:6, 427–437.
## 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
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