FigureOfMerit(dataset, fom = "wJAFROC")"wJAFROC". See "Details".c(I, J) array, where the row names are the IDs of the treatments and column names are the IDs of the readers.
"Wilcoxon" for ROC data; (2) "JAFROC1",
"JAFROC", "wJAFROC1", "wJAFROC" (the default), "HrAuc", "SongA1", "SongA2"** ,
"HrSe", "HrSp", "MaxLLF", "MaxNLF", "MaxNLFAllCases", "ExpTrnsfmSp",
for free-response data and (3) "ROI" for ROI data.
The JAFROC FOMs are described in the paper by Chakraborty and Berbaum. The Song FOMs are described in the paper by Song et al.
The "MaxLLF", "MaxNLF" and "MaxNLFAllCases" FOMs correspond to ordinate, abscissa and abscissa, respectively, of the highest
point on the FROC operating characteristic obtained by counting all the LL marks on diseased, all NL marks on non-diseased cases,
and all NL marks on all cases, respectively). The "ExpTrnsfmSp" FOM is described in the paper by Popescu.
The "ROI" FOM is described in the paper by Obuchowski et al.** The Song A2 figure of merit is computationally very intensive.
Song T, Bandos AI, Rockette HE, Gur D (2008) On comparing methods for discriminating between actually negative and actually positive subjects with FROC type data. Medical Physics 35: 1547-1558.
Popescu, L. M. (2011). Nonparametric signal detectability evaluation using an exponential transformation of the FROC curve. Medical Physics, 38(10), 5690.
Obuchowski, N. A., Lieber, M. L., & Powell, K. A. (2000). Data Analysis for Detection and Localization of Multiple Abnormalities with Application to Mammography. Academic Radiology, 553-554.
FigureOfMerit(dataset = rocData, fom = "Wilcoxon")
FigureOfMerit(dataset = frocData)
Run the code above in your browser using DataLab