plot_curve_and_hit_rate_and_false_rate_simultaneously: Curve and signal distribution and noise d log Phi() for a...
In BayesianFROC: FROC Analysis by Bayesian Approaches

Description Usage Arguments Details Value See Also Examples

View source: R/draw_latent_distribution.R

Draws FROC curve and signal and noise ( noise distribution is the differential of the logarithmic of the cumulative standard Gaussian denoted by d \log Φ) are drawn in a same plain. The author of this pacakage developed the FROC theory, and find that the noise distribution is not the so-called bi normal assumption. But instead, we use the differential logarithmic Gaussian for the noise distribution.

Note that MRMC data is not allowed.

1	plot_curve_and_hit_rate_and_false_rate_simultaneously(StanS4class)

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

This function is made to pass this plot to Shiny.

With pain from all my body, but today 2019 July 23 is good. Neuralgia or muscle aches makes my feeling down and down. If I can transform into Anpanman, then I want to give my head.

I fails, this is very small plot, so I cannot use this function for my package. I will remove this function or extende plot region for more confortable exhibition.

None

DrawCurves

draw_latent_noise_distribution

## Not run: 

#========================================================================================
#            1)             Build the data
#========================================================================================

# For singler reader and single modality  case.

dat <- list(c=c(3,2,1),    #Confidence level. Note that c is ignored.
            h=c(97,32,31), #Number of hits for each confidence level
            f=c(1,14,74),  #Number of false alarms for each confidence level

            NL=259,        #Number of lesions
            NI=57,         #Number of images
            C=3)           #Number of confidence level




#  where,
#      c denotes confidence level, i.e., rating of reader.
#                3 = Definitely deseased,
#                2 = subtle,.. deseased
#                1 = very subtle
#      h denotes number of hits (True Positives: TP) for each confidence level,
#      f denotes number of false alarms (False Positives: FP) for each confidence level,
#      NL denotes number of lesions,
#      NI denotes number of images,


# For example, in the above example data,
#  the number of hits with confidence level 3 is 97,
#  the number of hits with confidence level 2 is 32,
#  the number of hits with confidence level 1 is 31,

#  the number of false alarms with confidence level 3 is 1,
#  the number of false alarms with confidence level 2 is 14,
#  the number of false alarms with confidence level 1 is 74,



#--------------------------------------------------------------------------------------
#                         2)       Fit a model to the above data-set
#--------------------------------------------------------------------------------------



  #Because dataset named dat is a single reader and a single modality,
  #the function fit such a model by running the following code.





          fit <-   BayesianFROC::fit_Bayesian_FROC(
                               dat,       # dataset
                               ite=1111,  #To run in time <5s.
                               cha=1      # number of chains, it is better more large.
                               )






#--------------------------------------------------------------------------------------
#             3)  Draw the FROC curve and signal and noise (logarithmic Gaussian)
#--------------------------------------------------------------------------------------


#   Using the fitted model object of class stanfitExtended, we can draw curves.

    plot_curve_and_hit_rate_and_false_rate_simultaneously(fit)



     Close_all_graphic_devices() # 2020 August

## End(Not run)