data_of_36_readers_and_a_single_modality: 36 readers and a sinle modality data
In BayesianFROC: FROC Analysis by Bayesian Approaches
Description
Details
Author(s)
References
See Also
Examples
Description
An example data-set whose sample size is large.
Details
Frequentist methods fails when a sample size is large.
Namely, p value monotonically decreases when the sample size tends to large.
On the other hands, in Bayesian methods, the large samples such as large readers in FROC context
fails the MCMC algorithm. Thus Bayesian methods is also not free from such large sample problem in this sense.
This dataset is made for validation that wheter Bayes factor well work which is a subset of data dataList.Chakra.Web.orderd
- the number of modalities, denoted by
M which is now 1 modality
- the number of Confidences, denoted by
C which is now 5 Confidence levels
- the number of readers, denoted by
Q which is now 36 readers
Contents of data_of_36_readers_and_a_single_modality
NL = 142 (Number of Lesions)
NI = 57 (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.
m q c f h
-------------- ------------- ------------------------ ------------------- ----------------
1 1 5 0 12
1 1 4 3 22
1 1 3 7 18
1 1 2 12 18
1 1 1 8 15
1 2 5 0 14
1 2 4 4 24
1 2 3 9 17
1 2 2 14 15
1 2 1 10 6
1 3 5 0 26
1 3 4 3 39
1 3 3 6 23
1 3 2 11 16
1 3 1 7 6
1 4 5 0 9
1 4 4 1 17
1 4 3 4 15
1 4 2 8 18
1 4 1 5 25
1 5 5 0 9
1 5 4 2 17
1 5 3 5 16
1 5 2 9 19
1 5 1 6 27
1 6 5 0 39
1 6 4 0 46
1 6 3 2 22
1 6 2 15 13
1 6 1 2 3
1 7 5 0 9
1 7 4 1 17
1 7 3 4 14
1 7 2 8 16
1 7 1 5 17
1 8 5 1 11
1 8 4 5 19
1 8 3 10 16
1 8 2 16 17
1 8 1 12 15
1 9 5 0 15
1 9 4 1 26
1 9 3 3 20
1 9 2 6 18
1 9 1 4 12
1 10 5 0 31
1 10 4 4 40
1 10 3 8 22
1 10 2 13 16
1 10 1 9 5
1 11 5 0 13
1 11 4 2 23
1 11 3 5 19
1 11 2 9 19
1 11 1 6 17
1 12 5 0 8
1 12 4 3 16
1 12 3 7 15
1 12 2 11 17
1 12 1 8 22
1 13 5 0 13
1 13 4 1 23
1 13 3 4 19
1 13 2 7 21
1 13 1 4 20
1 14 5 0 36
1 14 4 4 45
1 14 3 9 22
1 14 2 14 13
1 14 1 10 3
1 15 5 0 17
1 15 4 2 27
1 15 3 5 20
1 15 2 9 18
1 15 1 6 10
1 16 5 0 8
1 16 4 4 15
1 16 3 8 13
1 16 2 13 16
1 16 1 9 22
1 17 5 0 9
1 17 4 1 16
1 17 3 4 15
1 17 2 8 17
1 17 1 5 20
1 18 5 0 12
1 18 4 2 21
1 18 3 6 17
1 18 2 10 17
1 18 1 7 12
1 19 5 0 19
1 19 4 3 33
1 19 3 8 21
1 19 2 12 19
1 19 1 9 13
1 20 5 0 8
1 20 4 1 15
1 20 3 3 14
1 20 2 6 16
1 20 1 4 21
1 21 5 0 33
1 21 4 2 41
1 21 3 5 21
1 21 2 9 13
1 21 1 6 3
1 22 5 0 15
1 22 4 3 26
1 22 3 7 20
1 22 2 12 20
1 22 1 8 15
1 23 5 0 9
1 23 4 4 17
1 23 3 8 15
1 23 2 12 18
1 23 1 9 23
1 24 5 0 10
1 24 4 0 19
1 24 3 3 17
1 24 2 6 20
1 24 1 4 23
1 25 5 0 8
1 25 4 1 15
1 25 3 3 14
1 25 2 6 17
1 25 1 4 22
1 26 5 0 12
1 26 4 1 21
1 26 3 4 18
1 26 2 8 19
1 26 1 5 18
1 27 5 0 19
1 27 4 1 32
1 27 3 4 18
1 27 2 7 13
1 27 1 5 4
1 28 5 1 10
1 28 4 5 18
1 28 3 9 16
1 28 2 15 19
1 28 1 11 26
1 29 5 0 16
1 29 4 2 27
1 29 3 6 21
1 29 2 10 20
1 29 1 7 16
1 30 5 1 9
1 30 4 4 18
1 30 3 9 16
1 30 2 14 19
1 30 1 10 25
1 31 5 0 10
1 31 4 3 19
1 31 3 7 16
1 31 2 11 18
1 31 1 8 20
1 32 5 1 12
1 32 4 5 22
1 32 3 10 18
1 32 2 15 19
1 32 1 11 18
1 33 5 1 14
1 33 4 6 24
1 33 3 11 18
1 33 2 16 17
1 33 1 12 10
1 34 5 0 34
1 34 4 3 43
1 34 3 8 22
1 34 2 12 14
1 34 1 9 3
1 35 5 0 9
1 35 4 1 17
1 35 3 4 15
1 35 2 8 18
1 35 1 5 25
1 36 5 1 17
1 36 4 6 31
1 36 3 11 20
1 36 2 16 17
1 36 1 12 9
Author(s)
Issei Tsunoda tsunoda.issei1111@gmail.com
References
Example data of Jafroc software
See Also
Not dataList.Chakra.Web
But dataList.Chakra.Web.orderd
Not dd
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
#========================================================================================
# Show data by table
#========================================================================================
viewdata(data_of_36_readers_and_a_single_modality)
plot_FPF_and_TPF_from_a_dataset(data_of_36_readers_and_a_single_modality)
####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#========================================================================================
# make this data from functions in this package
#========================================================================================
v <- v_truth_creator_for_many_readers_MRMC_data(M=1,Q=36)
m <- mu_truth_creator_for_many_readers_MRMC_data(M=1,Q=36)
d <- create_dataList_MRMC(mu.truth = m,v.truth = v)
# The last object named d is the desired dataset.
BayesianFROC documentation built on Jan. 23, 2022, 9:06 a.m.
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Description Details Author(s) References See Also Examples
Description
An example data-set whose sample size is large.
Details
Frequentist methods fails when a sample size is large. Namely, p value monotonically decreases when the sample size tends to large.
On the other hands, in Bayesian methods, the large samples such as large readers in FROC context fails the MCMC algorithm. Thus Bayesian methods is also not free from such large sample problem in this sense.
This dataset is made for validation that wheter Bayes factor well work which is a subset of data dataList.Chakra.Web.orderd
- the number of modalities, denoted by
Mwhich is now 1 modality
- the number of Confidences, denoted by
Cwhich is now 5 Confidence levels
- the number of readers, denoted by
Qwhich is now 36 readers
Contents of data_of_36_readers_and_a_single_modality
NL = 142 (Number of Lesions)
NI = 57 (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. |
m | q | c | f | h |
| -------------- | ------------- | ------------------------ | ------------------- | ---------------- |
| 1 | 1 | 5 | 0 | 12 |
| 1 | 1 | 4 | 3 | 22 |
| 1 | 1 | 3 | 7 | 18 |
| 1 | 1 | 2 | 12 | 18 |
| 1 | 1 | 1 | 8 | 15 |
| 1 | 2 | 5 | 0 | 14 |
| 1 | 2 | 4 | 4 | 24 |
| 1 | 2 | 3 | 9 | 17 |
| 1 | 2 | 2 | 14 | 15 |
| 1 | 2 | 1 | 10 | 6 |
| 1 | 3 | 5 | 0 | 26 |
| 1 | 3 | 4 | 3 | 39 |
| 1 | 3 | 3 | 6 | 23 |
| 1 | 3 | 2 | 11 | 16 |
| 1 | 3 | 1 | 7 | 6 |
| 1 | 4 | 5 | 0 | 9 |
| 1 | 4 | 4 | 1 | 17 |
| 1 | 4 | 3 | 4 | 15 |
| 1 | 4 | 2 | 8 | 18 |
| 1 | 4 | 1 | 5 | 25 |
| 1 | 5 | 5 | 0 | 9 |
| 1 | 5 | 4 | 2 | 17 |
| 1 | 5 | 3 | 5 | 16 |
| 1 | 5 | 2 | 9 | 19 |
| 1 | 5 | 1 | 6 | 27 |
| 1 | 6 | 5 | 0 | 39 |
| 1 | 6 | 4 | 0 | 46 |
| 1 | 6 | 3 | 2 | 22 |
| 1 | 6 | 2 | 15 | 13 |
| 1 | 6 | 1 | 2 | 3 |
| 1 | 7 | 5 | 0 | 9 |
| 1 | 7 | 4 | 1 | 17 |
| 1 | 7 | 3 | 4 | 14 |
| 1 | 7 | 2 | 8 | 16 |
| 1 | 7 | 1 | 5 | 17 |
| 1 | 8 | 5 | 1 | 11 |
| 1 | 8 | 4 | 5 | 19 |
| 1 | 8 | 3 | 10 | 16 |
| 1 | 8 | 2 | 16 | 17 |
| 1 | 8 | 1 | 12 | 15 |
| 1 | 9 | 5 | 0 | 15 |
| 1 | 9 | 4 | 1 | 26 |
| 1 | 9 | 3 | 3 | 20 |
| 1 | 9 | 2 | 6 | 18 |
| 1 | 9 | 1 | 4 | 12 |
| 1 | 10 | 5 | 0 | 31 |
| 1 | 10 | 4 | 4 | 40 |
| 1 | 10 | 3 | 8 | 22 |
| 1 | 10 | 2 | 13 | 16 |
| 1 | 10 | 1 | 9 | 5 |
| 1 | 11 | 5 | 0 | 13 |
| 1 | 11 | 4 | 2 | 23 |
| 1 | 11 | 3 | 5 | 19 |
| 1 | 11 | 2 | 9 | 19 |
| 1 | 11 | 1 | 6 | 17 |
| 1 | 12 | 5 | 0 | 8 |
| 1 | 12 | 4 | 3 | 16 |
| 1 | 12 | 3 | 7 | 15 |
| 1 | 12 | 2 | 11 | 17 |
| 1 | 12 | 1 | 8 | 22 |
| 1 | 13 | 5 | 0 | 13 |
| 1 | 13 | 4 | 1 | 23 |
| 1 | 13 | 3 | 4 | 19 |
| 1 | 13 | 2 | 7 | 21 |
| 1 | 13 | 1 | 4 | 20 |
| 1 | 14 | 5 | 0 | 36 |
| 1 | 14 | 4 | 4 | 45 |
| 1 | 14 | 3 | 9 | 22 |
| 1 | 14 | 2 | 14 | 13 |
| 1 | 14 | 1 | 10 | 3 |
| 1 | 15 | 5 | 0 | 17 |
| 1 | 15 | 4 | 2 | 27 |
| 1 | 15 | 3 | 5 | 20 |
| 1 | 15 | 2 | 9 | 18 |
| 1 | 15 | 1 | 6 | 10 |
| 1 | 16 | 5 | 0 | 8 |
| 1 | 16 | 4 | 4 | 15 |
| 1 | 16 | 3 | 8 | 13 |
| 1 | 16 | 2 | 13 | 16 |
| 1 | 16 | 1 | 9 | 22 |
| 1 | 17 | 5 | 0 | 9 |
| 1 | 17 | 4 | 1 | 16 |
| 1 | 17 | 3 | 4 | 15 |
| 1 | 17 | 2 | 8 | 17 |
| 1 | 17 | 1 | 5 | 20 |
| 1 | 18 | 5 | 0 | 12 |
| 1 | 18 | 4 | 2 | 21 |
| 1 | 18 | 3 | 6 | 17 |
| 1 | 18 | 2 | 10 | 17 |
| 1 | 18 | 1 | 7 | 12 |
| 1 | 19 | 5 | 0 | 19 |
| 1 | 19 | 4 | 3 | 33 |
| 1 | 19 | 3 | 8 | 21 |
| 1 | 19 | 2 | 12 | 19 |
| 1 | 19 | 1 | 9 | 13 |
| 1 | 20 | 5 | 0 | 8 |
| 1 | 20 | 4 | 1 | 15 |
| 1 | 20 | 3 | 3 | 14 |
| 1 | 20 | 2 | 6 | 16 |
| 1 | 20 | 1 | 4 | 21 |
| 1 | 21 | 5 | 0 | 33 |
| 1 | 21 | 4 | 2 | 41 |
| 1 | 21 | 3 | 5 | 21 |
| 1 | 21 | 2 | 9 | 13 |
| 1 | 21 | 1 | 6 | 3 |
| 1 | 22 | 5 | 0 | 15 |
| 1 | 22 | 4 | 3 | 26 |
| 1 | 22 | 3 | 7 | 20 |
| 1 | 22 | 2 | 12 | 20 |
| 1 | 22 | 1 | 8 | 15 |
| 1 | 23 | 5 | 0 | 9 |
| 1 | 23 | 4 | 4 | 17 |
| 1 | 23 | 3 | 8 | 15 |
| 1 | 23 | 2 | 12 | 18 |
| 1 | 23 | 1 | 9 | 23 |
| 1 | 24 | 5 | 0 | 10 |
| 1 | 24 | 4 | 0 | 19 |
| 1 | 24 | 3 | 3 | 17 |
| 1 | 24 | 2 | 6 | 20 |
| 1 | 24 | 1 | 4 | 23 |
| 1 | 25 | 5 | 0 | 8 |
| 1 | 25 | 4 | 1 | 15 |
| 1 | 25 | 3 | 3 | 14 |
| 1 | 25 | 2 | 6 | 17 |
| 1 | 25 | 1 | 4 | 22 |
| 1 | 26 | 5 | 0 | 12 |
| 1 | 26 | 4 | 1 | 21 |
| 1 | 26 | 3 | 4 | 18 |
| 1 | 26 | 2 | 8 | 19 |
| 1 | 26 | 1 | 5 | 18 |
| 1 | 27 | 5 | 0 | 19 |
| 1 | 27 | 4 | 1 | 32 |
| 1 | 27 | 3 | 4 | 18 |
| 1 | 27 | 2 | 7 | 13 |
| 1 | 27 | 1 | 5 | 4 |
| 1 | 28 | 5 | 1 | 10 |
| 1 | 28 | 4 | 5 | 18 |
| 1 | 28 | 3 | 9 | 16 |
| 1 | 28 | 2 | 15 | 19 |
| 1 | 28 | 1 | 11 | 26 |
| 1 | 29 | 5 | 0 | 16 |
| 1 | 29 | 4 | 2 | 27 |
| 1 | 29 | 3 | 6 | 21 |
| 1 | 29 | 2 | 10 | 20 |
| 1 | 29 | 1 | 7 | 16 |
| 1 | 30 | 5 | 1 | 9 |
| 1 | 30 | 4 | 4 | 18 |
| 1 | 30 | 3 | 9 | 16 |
| 1 | 30 | 2 | 14 | 19 |
| 1 | 30 | 1 | 10 | 25 |
| 1 | 31 | 5 | 0 | 10 |
| 1 | 31 | 4 | 3 | 19 |
| 1 | 31 | 3 | 7 | 16 |
| 1 | 31 | 2 | 11 | 18 |
| 1 | 31 | 1 | 8 | 20 |
| 1 | 32 | 5 | 1 | 12 |
| 1 | 32 | 4 | 5 | 22 |
| 1 | 32 | 3 | 10 | 18 |
| 1 | 32 | 2 | 15 | 19 |
| 1 | 32 | 1 | 11 | 18 |
| 1 | 33 | 5 | 1 | 14 |
| 1 | 33 | 4 | 6 | 24 |
| 1 | 33 | 3 | 11 | 18 |
| 1 | 33 | 2 | 16 | 17 |
| 1 | 33 | 1 | 12 | 10 |
| 1 | 34 | 5 | 0 | 34 |
| 1 | 34 | 4 | 3 | 43 |
| 1 | 34 | 3 | 8 | 22 |
| 1 | 34 | 2 | 12 | 14 |
| 1 | 34 | 1 | 9 | 3 |
| 1 | 35 | 5 | 0 | 9 |
| 1 | 35 | 4 | 1 | 17 |
| 1 | 35 | 3 | 4 | 15 |
| 1 | 35 | 2 | 8 | 18 |
| 1 | 35 | 1 | 5 | 25 |
| 1 | 36 | 5 | 1 | 17 |
| 1 | 36 | 4 | 6 | 31 |
| 1 | 36 | 3 | 11 | 20 |
| 1 | 36 | 2 | 16 | 17 |
| 1 | 36 | 1 | 12 | 9 |
Author(s)
Issei Tsunoda tsunoda.issei1111@gmail.com
References
Example data of Jafroc software
See Also
Not dataList.Chakra.Web
But dataList.Chakra.Web.orderd
Not dd
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | #========================================================================================
# Show data by table
#========================================================================================
viewdata(data_of_36_readers_and_a_single_modality)
plot_FPF_and_TPF_from_a_dataset(data_of_36_readers_and_a_single_modality)
####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#========================================================================================
# make this data from functions in this package
#========================================================================================
v <- v_truth_creator_for_many_readers_MRMC_data(M=1,Q=36)
m <- mu_truth_creator_for_many_readers_MRMC_data(M=1,Q=36)
d <- create_dataList_MRMC(mu.truth = m,v.truth = v)
# The last object named d is the desired dataset.
|
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