BayesianFROC (version 0.2.1)

ddddddd: Multiple reader and 2 modalities data such that all modalities have same AUC.

Description

This is a subset of dataList.Chakra.Web.orderd

Details

The author made this dataset to validate the scheme of Bayes factor well works in our Bayesian FROC models

This dataset is made for validation that wheter Bayes factor well work which is a subset of data dataList.Chakra.Web.orderd

dddddd$M

2 modalities of almost same AUC

dddddd$C

3 Confidence levels

dddddd$Q

2 readers

If Bayes factor admit the null hypothesis that all modality are same, that is, 1-st and 2-nd modality of dataList.Chakra.Web.orderd are same, then, the Bayes factor well works.

Contents of dddddd

NL = 142 (Number of Lesions)

NI = 199 (Number of Images)#'

Contents:

Multiple readers and multiple modalities case, i.e., MRMC case

References

Example data of Jafroc software

See Also

Not dataList.Chakra.Web But dataList.Chakra.Web.orderd Not dd

Examples

# NOT RUN {

#----------------------------------------------------------------------------------------
#                        Show data by table
#----------------------------------------------------------------------------------------



                        viewdata(ddddddd)




####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#----------------------------------------------------------------------------------------
#                       make an object dddd from an object dataList.Chakra.Web.orderd
#----------------------------------------------------------------------------------------


ddd  <-  data.frame(m=dataList.Chakra.Web.orderd$m,
                    q=dataList.Chakra.Web.orderd$q,
                    c=dataList.Chakra.Web.orderd$c,
                    h=dataList.Chakra.Web.orderd$h,
                    f=dataList.Chakra.Web.orderd$f
)

dddd <- ddd[ddd$q < 3,]

# The following code extract the first and the second modality from dd
dddd <- dddd[dddd$m < 3,]  #  Reduce the dataset ddd, i.e., dd
dddd <- dddd[dddd$c <4,]
ddd <- list(
  m=dddd$m,
  q=dddd$q,
  c=dddd$c,
  h=dddd$h,
  f=dddd$f,
  NL=142,
  C=max(dddd$c),
  M=max(dddd$m),
  Q=max(dddd$q)
)

ddddddd <-ddd


# This dataset is made in 2019 July 6, for the aim of easy exihibition
# This dataset is very minimum, and it is easy to view


# }
# NOT RUN {
#-------------------------------------------------------------------------------
#                       Test of Hypothesis based on Bayes factor
#-------------------------------------------------------------------------------

          dataList <- ddddddd
          ite <- 2222
          cha <- 1
          summary <-F


    fitH0 <- fit_Bayesian_FROC( ite  = ite,
          summary = summary,
            cha = cha,
             dataList = dataList ,
              Null.Hypothesis = TRUE
              )


      fitH1 <- fit_Bayesian_FROC( ite  = ite,
              summary = summary,
               cha = cha,
               dataList = dataList ,
                Null.Hypothesis = FALSE)


                 H0 <- bridgesampling::bridge_sampler(fitH0,
                                                     method = "normal",
                                                     silent = TRUE)

             H1 <- bridgesampling::bridge_sampler(fitH1,
                                    method = "normal",
                                     silent = TRUE)



                      BF10 <- bridgesampling::bf( H0,H1)

                      print(BF10)


 message("\n* If the number is greater, then we reject H0 with more confidence.")






#-------------------------------------------------------------------------------
#   When I saw the plots, the author became happy, because it was well fitted
#-------------------------------------------------------------------------------

                                                # 2019 July 12

# }
# NOT RUN {
# }