The so-called false positive fraction (FPF) and the true positive fraction (TPF) are calculated from the number of hits (True Positives: TPs) and the number of false alarms (False Positives: FPs)
A list, should include
Logical, that is
For more details, see the author's paper in which I explained per image and per lesion. (for details of models, see vignettes , now, it is omiited from this package, because the size of vignettes are large.)
where N_L is a number of lesions (signal). To emphasize its denominator N_L, we also call it the False Positive Fraction (FPF) per lesion.
On the other hand,
where N_I is the number of images (trial). To emphasize its denominator N_I, we also call it the False Positive Fraction (FPF) per image.
The model is fitted so that
the estimated FROC curve can be ragraded
as the expected pairs of FPF per image and TPF per lesion (
or as the expected pairs of FPF per image and TPF per lesion (
On the other hand, if
So,data of FPF and TPF are changed thus, a fitted model is also changed whether
Revised 2019 Dec 8 Revised 2019 Nov 25 Revised 2019 August 28
From data of number of hits (True Positive: TP) and false alarms (False Positive: FP), we calculate the number of cumulative false positives (FPF) and cumulative hits (TPF).
Because there are three subscripts, reader, modality, and image, we create array format and vector format etc...
A metadata such as number of cumulative false alarms and hits to create and draw the curve.
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## Not run: #======================================================================================== # TP and FP #======================================================================================== dat <- BayesianFROC::dataList.Chakra.Web #======================================================================================== # Calculates TPF and FPF from TP and FP #======================================================================================== metadata_srsc_per_image(dat) # Revised 2019 Nov. ## End(Not run)# dottest
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