BayesianFROC (version 1.0.0)

DrawCurves_MRMC_pairwise: Draw the FROC curves with Colour

Description

Draw FROC curves and AFROC curves for user's specified modalities and user's specified readers. Using this function repeatedly, we can draw the different reader and modality in a same plane simultaneously.

Usage

DrawCurves_MRMC_pairwise(
  StanS4class,
  modalityID,
  type_to_be_passed_into_plot = "p",
  title = TRUE,
  readerID,
  Colour = TRUE,
  DrawFROCcurve = TRUE,
  DrawAFROCcurve = FALSE,
  DrawCFPCTP = TRUE,
  Draw.Flexible.upper_y = TRUE,
  Draw.Flexible.lower_y = TRUE,
  new.imaging.device = TRUE,
  summary = TRUE,
  color_is_changed_by_each_reader = FALSE,
  type = 1
)

Arguments

StanS4class

An S4 object of class stanfitExtended which is an inherited class from the S4 class stanfit. This R object is a fitted model object as a return value of the function fit_Bayesian_FROC().

To be passed to DrawCurves() ... etc

modalityID

This is a vector indicating modalityID whose component is natural namber.

type_to_be_passed_into_plot

"l" or "p".

title

Logical: TRUE of FALSE. If TRUE (default), then title of curves are drawn.

readerID

This is a vector indicating readerID whose component is natural namber.

Colour

Logical, that is TRUE or FALSE. Whether plot of curves are with dark theme. Default is TRUE indicating dark theme.

DrawFROCcurve

Logical: TRUE of FALSE. Whether the FROC curve is to be drawn.

DrawAFROCcurve

Logical: TRUE of FALSE. Whether the AFROC curve is to be drawn.

DrawCFPCTP

Logical: TRUE of FALSE. Whether the CFP and CTP points are to be drawn. CFP: Cumulative false positive per lesion (or image) which is also called False Positive Fraction (FPF). CTP Cumulative True Positive per lesion which is also called True Positive Fraction (TPF)..

Draw.Flexible.upper_y

Logical, that is TRUE or FALSE. Whether or not the upper bounds of vertical axis are determined automatically.

Draw.Flexible.lower_y

Logical, that is TRUE or FALSE. Whether or not the lower bounds of vertical axis are determined automatically.

new.imaging.device

Logical: TRUE of FALSE. If TRUE (default), then open a new device to draw curve. Using this we can draw curves in same plain by new.imaging.device=FALSE.

summary

Logical: TRUE of FALSE. Whether to print the verbose summary. If TRUE then verbose summary is printed in the R console. If FALSE, the output is minimal. I regret, this variable name should be verbose.

color_is_changed_by_each_reader

A logical, if TRUE, then the FROC curves, AFROC curves, and FPF, TPF are colored accordingly by each reader. The aim of FROC analysis is to compare the modality and not reader, so the default value is false, and curves and FPF and TPF are colored by each modalities.

type

An integer, for the color of background and etc.

Details

By drawing different modality FROC curves in the same plane, we can compare the modality. E.g., if some modality FROC curve is upper then other modality curves, then we may say that the upper modality is better observer performance, i.e., higher AUC.

Examples

# NOT RUN {
# }
# NOT RUN {
#1) Fit a model to data by the following:



  fit <- fit_Bayesian_FROC(dataList.Chakra.Web, ite = 1111)


#Note that the return value "fit" is an object of an inherited S4 class from stanfit

#2)  Using the above S4 class object, we draw the curves.



 DrawCurves_MRMC_pairwise(fit,
                          modality = 1,
                          reader = 4
                          )



#3) By changing the modality (or reader),
   #we can draw the curves with respect to different  modalities.
   #This shows the comparison of modalites.



 DrawCurves_MRMC_pairwise(fit,
                          modality = 2,
                          reader = 4
                          )

 DrawCurves_MRMC_pairwise(fit,
                          modality = 3,
                          reader = 4
                          )


#4) By repeating in this manner for different modalities or readers,
#    we can draw  AFROC (FROC) curves in a single imaging device.
# Revised 2019 Nov 27



#5) If you want to draw the FROC curves
#for reader ID =1,2,3,4 and modality ID =1,2, then the code is as follows;

DrawCurves_MRMC_pairwise(
                            fit,
                            modalityID = c(1,2,3,4),
                            readerID = c(1,2)
                            )
# Each color of curves corresponds to the modality ID.
# So, even if curves are different readers and same modality, then color is same.














       #   Close the graphic device
           Close_all_graphic_devices()
       
# }
# NOT RUN {
 # dottest
# }