PlotRsmOperatingCharacteristics.RdVisualize 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
)Array: the RSM mu parameter.
Array: the RSM lambda parameter.
Array: the RSM nu parameter.
Array, the lowest reporting threshold; if missing the default is an array of -Inf.
Array: the probability mass function of the lesion distribution for diseased cases. The default is 1. See UtilLesDistr.
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.
The type of operating characteristic desired: can be
"ROC", "AFROC", "wAFROC", "FROC" or
"pdfs" or "ALL". The default is "ALL".
The positioning of the legend: "right",
"left", "top" or "bottom". Use "none" to
suppress the legend.
Allows control on the direction of the legend;
"horizontal", the default, or "vertical"
Where to position the legend, default is bottom right corner c(0,1)
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.
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.
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")