BayesianFROC (version 0.2.1)

dddddd: Multiple reader and one modality data

Description

This is a subset of dd

This dataset is made, as a toy data, which is a subset of data dd

dddddd$M

2 modalities

dddddd$C

3 Confidence levels

dddddd$Q

2 readers

Details

The model did not converge both null model and alternative model in 2019 Jun 21.

Contents of dddddd

NL = 142 (Number of Lesions)

NI = 199 (Number of Images)#'

Contents:

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

---------------------------------------------------------------------------------------------------

ModalityID	ReaderID	Confidence levels	No. of false alarms	No. of hits.
q	m	c	f	h
--------------	-------------	------------------------	-------------------	----------------
1	1	3	20	11
1	1	2	29	5
1	1	1	21	1
1	2	3	6	29
1	2	2	15	1
1	2	1	22	0
2	1	3	21	13
2	1	2	24	4
2	1	1	23	1
2	2	3	5	29
2	2	2	30	1
2	2	1	40	0

---------------------------------------------------------------------------------------------------

References

Example data of Jafroc software

See Also

dataList.Chakra.Web dataList.Chakra.Web.orderd dd

Examples

# NOT RUN {

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



                        viewdata(dddddd)




####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#----------------------------------------------------------------------------------------
#                       make an object dddd from an object dd
#----------------------------------------------------------------------------------------



ddd  <-  data.frame(m=dd$m,q=dd$q,c=dd$c,h=dd$h,f=dd$f)
dddd <- ddd[ddd$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)
)

dddddd <-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 {
#-------------------------------------------------------------------------------
#                       Fit a model to data dddddd
#-------------------------------------------------------------------------------

fit <- fit_Bayesian_FROC( ite  = 1111,
                           cha = 1,
                            summary = F,
                              Null.Hypothesis = F,
                               dataList = dddddd )


#-------------------------------------------------------------------------------
#           Draw a curves and data points to confirm goodness of fit
#-------------------------------------------------------------------------------

             DrawCurves(fit,
                       modalityID = c(1,2),
                       readerID = c(1,2)
                       )


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



# Good Bye, pretty crowd!   2019 July 6
# I always think who read this? My heart empty and empty.

# }
# NOT RUN {
# }