ddddd: Data of MRMC; Model * _ does _* converge.

In BayesianFROC: FROC Analysis by Bayesian Approaches

Description

This is a subset of dd. In the past, this model did not converge in the Model_MRMC.stan, thus I made a new stan file to get convergence estimates. The stan file named Model_Hiera_OneModalityMultipleReader_TargetFormulation.stan. Thus, even if the number of modalityt is 1, we can pool the AUCs over all readers by using this new model. The author believes this pooling is the most natural, primitive, simple way.

ddddd$M

1 modality <—- ATTENTION!!

ddddd$C

5 Confidence levels

ddddd$Q

4 readers

Details

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

Contents of dddd

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	5	0	50
1	1	4	4	30
1	1	3	20	11
1	1	2	29	5
1	1	1	21	1
1	2	5	0	15
1	2	4	0	29
1	2	3	6	29
1	2	2	15	1
1	2	1	22	0
1	3	5	1	39
1	3	4	15	31
1	3	3	18	8
1	3	2	31	10
1	3	1	19	3
1	4	5	1	10
1	4	4	2	8
1	4	3	4	25
1	4	2	16	45
1	4	1	17	14

—————————————————————————————————

Author(s)

Issei Tsunoda [e-mail]

References

Example data of Jafroc software

See Also

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

Examples

#========================================================================================
#                        Show data by table
#========================================================================================



                        viewdata(BayesianFROC::ddddd)




####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$m < 2,]  #  Reduce the dataset ddd, i.e., dd

ddd <- list(
           m=dddd$m,
           q=dddd$q,
           c=dddd$c,
           h=dddd$h,
           f=dddd$f,
           NL=142,
           NI=199, # 2020 April 6
           C=max(dddd$c),
           M=max(dddd$m),
           Q=max(dddd$q)
        )

          ddddd <-ddd

BayesianFROC documentation built on Jan. 23, 2022, 9:06 a.m.