`UtilFigureOfMerit.Rd`

Calculate the specified empirical figure of merit for each modality-reader combination in a standard (1T) or cross-modality (2T) dataset

`UtilFigureOfMerit(dataset, FOM = "wAFROC", FPFValue = 0.2)`

- dataset
The dataset to be analyzed,

`RJafroc-package`

- FOM
The figure of merit; the default is

`"wAFROC"`

- FPFValue
Only needed for

`LROC`

data**and**FOM = "PCL" or "ALROC"; where to evaluate a partial curve based figure of merit. The default is 0.2.

For standard IT dataset: A `c(I, J)`

dataframe, where the row
names are `modalityID`

's of the treatments and column names are the

`readerID`

's of the readers. For cross-modality dataset: A `list`

containing two data frames are returned:
* `c(I2, J)`

data frame, FOMs averaged over the first modality,
where the row names are modality IDS of the second modality
* `c(I1, J)`

data frames, FOMs averaged over the second modality,
where the row names are modality IDs of the first modality,
* For either 1T or 2T the column names are the `readerID`

's.

The allowed FOMs depend on the `dataType`

field of the
`dataset`

object.

**For dataset$descriptions$type = "ROC" only FOM = "Wilcoxon" is allowed**.

`dataset$descriptions$type = "FROC"`

the following FOMs are allowed`FOM = "AFROC1"`

(use only if no non-diseased cases are available)`FOM = "AFROC"`

`FOM = "wAFROC1"`

(use only if no non-diseased cases are available)`FOM = "wAFROC"`

(the default)`FOM = "HrAuc"`

`FOM = "HrSe"`

(example of an end-point based FOM)`FOM = "HrSp"`

(do:)`FOM = "MaxLLF"`

(do:)`FOM = "MaxNLF"`

(do:)`FOM = "MaxNLFAllCases"`

(do:)

`"MaxLLF"`

, `"MaxNLF"`

and `"MaxNLFAllCases"`

correspond to ordinate, and abscissa, respectively, of the highest point
on the FROC operating characteristic obtained by counting all the marks.
Given the number of FOMs possible with FROC data, it is appropriate
to make a recommendation: **it is recommended the wAFROC FOM be used
whenever possible. One should use the wAFROC1 FOM only if the dataset has
no non-diseased cases**.

For ** dataType = "ROI" dataset only FOM = "ROI" is allowed**.

For ** dataType = "LROC"** dataset the following FOMs are allowed:

`FOM = "Wilcoxon"`

for ROC data inferred from LROC data`FOM = "PCL"`

the probability of correct localization at specified`FPFValue`

`FOM = "ALROC"`

the area under the LROC from zero to specified`FPFValue`

`FPFValue`

The FPF at which to evaluate `PCL`

or `ALROC`

;
the default is 0.2; only needed for LROC data.
For cross-modality analysis ROI and LROC datasets are not supported.

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

```
res <- UtilFigureOfMerit(dataset02, FOM = "Wilcoxon") # ROC data
res <- UtilFigureOfMerit(dataset01) # FROC dataset, default wAFROC FOM
res <- UtilFigureOfMerit(datasetX, FOM = "wAFROC")
```