`PlotRsmOperatingCharacteristics.Rd`

Visualize RSM predicted ROC, AFROC, wAFROC and FROC curves, and
ROC pdfs, given **equal-length arrays** of search model parameters: mu,
lambda, nu and zeta1.

```
PlotRsmOperatingCharacteristics(
mu,
lambda,
nu,
zeta1,
lesDistr = 1,
relWeights = 0,
OpChType = "ALL",
legendPosition = "bottom",
legendDirection = "horizontal",
legendJustification = c(0, 1),
nlfRange = NULL,
llfRange = NULL,
nlfAlpha = NULL
)
```

- mu
Array: the RSM mu parameter.

- lambda
Array: the RSM lambda parameter.

- nu
Array: the RSM nu parameter.

- zeta1
Array, the lowest reporting threshold; if missing the default is an array of -Inf.

- lesDistr
Array: the probability mass function of the lesion distribution for diseased cases. The default is 1. See UtilLesDistr.

- relWeights
The relative weights of the lesions; a vector of length equal to

`length(maxLL)`

. The default is zero, in which case equal weights are assumed.- OpChType
The type of operating characteristic desired: can be "

`ROC`

", "`AFROC`

", "`wAFROC`

", "`FROC`

" or "`pdfs`

" or "`ALL`

". The default is "`ALL`

".- legendPosition
The positioning of the legend: "

`right`

", "`left`

", "`top`

" or "`bottom`

". Use "`none`

" to suppress the legend.- legendDirection
Allows control on the direction of the legend;

`"horizontal"`

, the default, or`"vertical"`

- legendJustification
Where to position the legend, default is bottom right corner c(0,1)

- nlfRange
**This applies to FROC plot only**. The x-axis range, e.g., c(0,2), for FROC plot. Default is "`NULL`

", which means the maximum NLF range, as determined by the data.- llfRange
**This applies to FROC plot only**. The y-axis range, e.g., c(0,1), for FROC plot. Default is "`NULL`

", which means the maximum LLF range, as determined by the data.- nlfAlpha
Upper limit of the integrated area under the FROC plot. Default is "

`NULL`

", which means the maximum NLF range is used (i.e., lambda). Attempt to integrate outside the maximum NLF will generate an error.

A list containing five ggplot2 objects (ROCPlot, AFROCPlot wAFROCPlot, FROCPlot and PDFPlot) and two area measures (each of which can have up to two elements), the area under the search model predicted ROC curves in up to two treatments, the area under the search model predicted AFROC curves in up to two treatments, the area under the search model predicted wAFROC curves in up to two treatments, the area under the search model predicted FROC curves in up to two treatments.

`ROCPlot`

The predicted ROC plots`AFROCPlot`

The predicted AFROC plots`wAFROCPlot`

The predicted wAFROC plots`FROCPlot`

The predicted FROC plots`PDFPlot`

The predicted ROC pdf plots, highest rating generated`aucROC`

The predicted ROC AUCs, highest rating generated`aucAFROC`

The predicted AFROC AUCs`aucwAFROC`

The predicted wAFROC AUCs`aucFROC`

The predicted FROC AUCs

RSM is the Radiological Search Model described in the book. This
function is vectorized with respect to the first 4 arguments. For
`lesDistr`

the sum must be one. To indicate that all dis. cases
contain 4 lesions, set lesDistr = c(0,0,0,1).

Chakraborty DP (2006) A search model and figure of merit for observer data acquired according to the free-response paradigm, Phys Med Biol 51, 3449-3462.

Chakraborty DP (2006) ROC Curves predicted by a model of visual search, Phys Med Biol 51, 3463--3482.

Chakraborty, DP, Yoon, HJ (2008) Operating characteristics predicted by models for diagnostic tasks involving lesion localization, Med Phys, 35:2, 435.

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

```
## Following example is for mu = 2, lambda = 1, nu = 0.6, in one modality and
## mu = 3, lambda = 1.5, nu = 0.8, in the other modality. 20% of the diseased
## cases have a single lesion, 40% have two lesions, 10% have 3 lesions,
## and 30% have 4 lesions.
res <- PlotRsmOperatingCharacteristics(mu = c(2, 3), lambda = c(1, 1.5), nu = c(0.6, 0.8),
lesDistr = c(0.2, 0.4, 0.1, 0.3), legendPosition = "bottom")
```