dd.orderd: Multiple Reader and Multiple Modality Data

In BayesianFROC: FROC Analysis by Bayesian Approaches

Description

A list, representing FROC data of MRMC. This is same as dataList.Chakra.Web .

Details

This data is based on in Chakraborty's JAFROC software in which example data exists. The author have calculated hits and false alarms from this Jafroc example data. Moreover the author ordered it such that the modality ID also means its observer performance, namely Modality ID = 1 means it has the most high AUC.

contents

ModalityID	ReaderID	Confidence levels	No. of hits	No. of false alarms
m	q	c	h	f
--------------	-------------	------------------------	-------------------	----------------
1	1	5	61	1
1	1	4	19	4
1	1	3	12	18
1	1	2	9	21
1	1	1	3	23
1	2	5	16	1
1	2	4	29	1
1	2	3	34	0
1	2	2	1	11
1	2	1	0	35
1	3	5	52	6
1	3	4	29	14
1	3	3	10	37
1	3	2	4	36
1	3	1	3	18
1	4	5	10	0
1	4	4	16	2
1	4	3	23	4
1	4	2	43	18
1	4	1	15	25
2	1	5	52	1
2	1	4	25	1
2	1	3	13	21
2	1	2	4	24
2	1	1	1	23
2	2	5	27	1
2	2	4	28	1
2	2	3	29	5
2	2	2	1	30
2	2	1	0	40
2	3	5	53	2
2	3	4	29	19
2	3	3	13	31
2	3	2	2	56
2	3	1	4	42
2	4	5	9	2
2	4	4	16	0
2	4	3	22	2
2	4	2	43	30
2	4	1	14	32
3	1	5	50	0
3	1	4	30	4
3	1	3	11	20
3	1	2	5	29
3	1	1	1	21
3	2	5	15	0
3	2	4	29	0
3	2	3	29	6
3	2	2	1	15
3	2	1	0	22
3	3	5	39	1
3	3	4	31	15
3	3	3	8	18
3	3	2	10	31
3	3	1	3	19
3	4	5	10	1
3	4	4	8	2
3	4	3	25	4
3	4	2	45	16
3	4	1	14	17
4	1	5	35	0
4	1	4	29	2
4	1	3	18	19
4	1	2	9	23
4	1	1	0	18
4	2	5	17	0
4	2	4	27	2
4	2	3	24	6
4	2	2	0	10
4	2	1	0	30
4	3	5	34	2
4	3	4	33	25
4	3	3	7	40
4	3	2	13	29
4	3	1	2	24
4	4	5	12	1
4	4	4	16	1
4	4	3	21	4
4	4	2	35	24
4	4	1	15	32
5	1	5	43	1
5	1	4	29	7
5	1	3	11	13
5	1	2	6	28
5	1	1	0	19
5	2	5	18	0
5	2	4	29	1
5	2	3	21	7
5	2	2	0	7
5	2	1	0	31
5	3	5	43	7
5	3	4	29	15
5	3	3	6	28
5	3	2	7	41
5	3	1	1	9
5	4	5	10	0
5	4	4	14	2
5	4	3	19	5
5	4	2	32	24
5	4	1	23	31

Author(s)

Issei Tsunoda [e-mail]

References

Example data of Jafroc software

Examples

    viewdata(BayesianFROC::dd.orderd)








#========================================================================================
#                           Code to make the dataset dd
#========================================================================================


h<-c(
  61,19,12,9,3,16,29,34,1,0,52,29,10,4 ,3,10,16,23,43,15, # modality 4 of dataset dd
  52,25,13,4,1,27,28,29,1,0,53,29,13,2 ,4,9 ,16,22,43,14, # modality 2 of dataset dd
  50,30,11,5,1,15,29,29,1,0,39,31,8 ,10,3,10,8 ,25,45,14, # modality 1 of dataset dd
  35,29,18,9,0,17,27,24,0,0,34,33,7 ,13,2,12,16,21,35,15,  # modality 5 of dataset dd
  43,29,11,6,0,18,29,21,0,0,43,29,6 ,7 ,1,10,14,19,32,23 # modality 3   of dataset dd

)

f <-c(
  1, 4,18,21,23,1,1,0,11,35, 6,14,37,36,18,0,2,4,18,25,# modality 4  of dataset dd
  1 ,1,21,24,23,1,1,5,30,40,2 ,19,31,56,42,2,0,2,30,32,# modality 2 of dataset dd
  0 ,4,20,29,21,0,0,6,15,22,1 ,15,18,31,19,1,2,4,16,17,# modality 1 of dataset dd
  0, 2,19,23,18,0,2,6,10,30, 2,25,40,29,24,1,1,4,24,32,# modality 5 of dataset dd
  1, 7,13,28,19,0,1,7, 7,31, 7,15,28,41,9 ,0,2,5,24,31# modality 3 of dataset dd

)

a   <- m_q_c_vector_from_M_Q_C(5,4,5)

m <- a$m
c <- a$c
q <- a$q

NI<-199
NL <-142
C<-5
M<-5
Q<-4

dd.orderd <- list(
  h=h,
  f=f,
  m=m,
  c=c,
  q=q,
  NI=NI,
  NL=NL,
  M=M,
  Q=Q,
  C=C
)

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