Description Usage Arguments Format Details Value References See Also Examples
View source: R/convertFromJafroc.R
Convert an FROC dataset
.xlsx
file of Jafroc
R object
1  convertFromJafroc(No.of.Modalities, No.of.readers, No.of.confidence.levels)

No.of.Modalities 
A positive integer, indicating the number of modalities for FROC dataset in 
No.of.readers 
A positive integer, indicating the number of readers for FROC dataset in an 
No.of.confidence.levels 
A positive integer, indicating the number of confidence levels for FROC dataset in 
The .xlsx
file of Jafroc
consists of three sheets named TP, FP, Truth, precisely! Correctly!
(other names never be permitted !!)
———————————– TP ——————————————
A sheet named TP consists of five columns precisely named from the right hand side:
ReaderID, ModalityID, CaseID, LesionID, TP_Rating.
NOTE.
Note that the above word CaseID means the Image ID vectors indicating the ID of radiographs. That is "case = image = radiograph".
Note that the first row of each sheat of .xlsx
file is constructed by the names of column as follows:
An Example of the sheet named TP in a .xlsx
file for the Jafroc software
Interpretation of table
Throughout this explanation, we follow the convention that readers are male.
For example, the first row means the first reader (ReaderID=1) correctly find the first lesion (LesionID = 1) in the first image (CaseID = 1) taken by the first modality (ModalityID = 1) with his rating 5 (TP_Rating =5).
Similarily the second row means the first reader (ReaderID=1) correctly find the 4th lesion (LesionID = 4) in the second image (CaseID = 2) taken by the 2nd modality (ModalityID = 2) with his rating 4 (TP_Rating = 4).
ReaderID  ModalityID  CaseID  LesionID  TP_Rating. 
         
1  1  1  1  5 
1  2  2  1  4 
1  3  4  1  5 
1  1  8  1  3 
1  2  8  2  4 
1  3  9  1  4 
1  1  9  2  3 
1  2  9  3  5 
1  3  11  1  3 
2  1  1  1  4 
2  2  4  1  4 
2  3  5  1  4 
2  1  8  1  1 
2  2  8  2  2 
2  3  8  3  2 
2  1  10  1  3 
2  2  10  2  2 
2  3  11  1  2 
:  :  :  :  : 
:  :  :  :  : 
———————————– FP ——————————————
A sheet named FP consists of four columns precisely
named from the right hand side: ReaderID, ModalityID, CaseID, FP_Rating
An Example of a sheet named FP in a .xlsx
file for the Jafroc software
Interpretation of table
For example, the first row means the first reader (ReaderID=1) makes a false alarm location in the first image (CaseID = 1) taken by the first modality (ModalityID = 1) with his rating 2 (TP_Rating =2).
Similarily the second row means the first reader (ReaderID=1) makes a false alarm location in the second image (CaseID = 2) taken by the 2nd modality (ModalityID = 2) with his rating 1 (TP_Rating = 1).
Similarily the 6th and 7th rows mean that the first reader (ReaderID=1) makes two false alarm location in the second patient (CaseID = 2). The first false alarm is in the image taken by the 1st modality (ModalityID = 1) with his rating 1 (TP_Rating = 1). The second false alarm is in the image taken by the 3rd modality (ModalityID = 3) with his rating 2 (TP_Rating = 2).
ReaderID  ModalityID  CaseID  FP_Rating. 
       
1  1  1  2 
1  2  2  1 
1  3  3  1 
1  1  5  2 
1  2  7  1 
1  3  7  2 
1  1  9  3 
1  2  9  4 
1  3  10  1 
2  1  1  2 
2  2  2  3 
2  3  3  4 
2  1  8  1 
2  2  9  1 
2  3  11  1 
2  1  14  1 
2  2  15  1 
2  3  21  2 
:  :  :  : 
:  :  :  : 
———————————– Truth ——————————————
A sheet named Truth consists of three columns precisely named from the right hand side:CaseID, LesionID, Weight .
An Example of a sheet named Truth in a .xlsx
file for the Jafroc software
Interpretation of table
For example, the first image (CaseID = 1) contains three lesions each of which is named 1,2,3, namely LesionID = 1,2,3. For example, the second image (CaseID = 2) contains two lesions each of which is named 1,2, namely LesionID = 1,2. For example, the third image (CaseID = 3) contains a sinle lesion named 1, namely LesionID = 1.
CaseID  LesionID  Weight 
     
1  1  0.3333... 
1  2  0.3333... 
1  3  0.3333... 
2  1  0.5 
2  2  0.5 
3  1  1 
4  1  0.25 
4  2  0.25 
4  3  0.25 
4  4  0.25 
5  1  0.5 
5  2  0.5 
6  1  0.3333... 
6  2  0.3333... 
6  3  0.3333... 
7  1  0.3333... 
7  2  0.3333... 
7  3  0.3333... 
8  1  0.25 
8  2  0.25 
8  3  0.25 
8  4  0.25 
:  :  : 
:  :  : 
Note that the weght are used such that each image influences a same effect on the esimates. Without weight, the images including many targets (lesions) will have very strong effect on the estimates. To avoid such bias, Jafroc uses weight. In another context, weight would be used to specify more important lesions in each image.
Revised 2019 Dec 13; 2020 May 27
However, in this package, we do not use the information of weight. Since the theory of the author of this package did not consider such weight. In the future I have to include the notion of weight. Jafroc use the notion fo figure of metric as non parametric manner. So, it seems difficult to include it in the Bayesian model, since generally speaking, Bayesian methodology is parametric.
Create a dataset to be passed into the
function fit_Bayesian_FROC
.
Convert an Excel file whose extension
is .xlsx
of Jafroc format to
an R object representing FROC data
to which we will apply functions in
this package such as fit_Bayesian_FROC()
.
Revised 2019 Jun 19 Revised 2019 Dec 13
The return values include a
list which can be passed to the function fit_Bayesian_FROC
.
For data of Jafroc, running this function,
we immediately can fit the author's Bayesian
FROC model to this return value.
The Jafroc software's format consists of suspicious locations marked by readers and true locations. Such data is redundant for our Bayesian statistical models. So, we reduce the information of data to the number of false positives and number of hits for each confidence levels by this function.
Data can be calculated from the following Jafroc data, in which there are more information than TP and FP. In fact, in the Jafroc data, the FP and TP are counted for each images, each lesions etc. So, it has more information.
It causes limitation of our model. So, our model start to fit a model to the reduced data from Jafroc. So, the redunction will cause the non accuracy evaluation of the observer performance. The future research I should start the Jafroc formulation.
A list, representing FROC data.
Bayesian Models for Freeresponse Receiver Operating Characteristic Analysis,preprint
Rjafroc, which is unfortunately not on CRAN, now 2019 Jun 19. Or JAFROC software in the Chakarboty's Web page. Unfortunately, this software is no longer supported.
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## Only run examples in interactive R sessions
if (interactive()) {
#========================================================================================
# Example for convert the Jafroc data to the BayesianFROC
#========================================================================================
# Work Flow of this example
# step 0) Prepare Jafroc .xlsx file contained in this package
# step 1) Convert the .xlxs file obtained in step 0)
# step 2) Fit a model to data object obtained in step 1)
#========================================================================================
# step 0) Make a Jafroc data as an example dataset
#========================================================================================
# If you can search the xlsx file named JAFROC_data.xlsx
# in the director "inst/extdata" of this package,
# Then this step 0) is redundant. The author prepare this example for the people who
# cannot search the xlsx file in the "inst/extdata" of this package.
# By an xlsx file named JAFROC_data.xlsx in the director "inst/extdata" of this package,
# we can reconstruct it as follows:(If someone can obtain the Excel file
# from the path BayesianFROC/inst/extdata/JAFROC_data.xlsx, then the following code
# is not required to run. If searching bother you, then run the R script to obtain the
# Excel file.)
# I do not know how to users refer the JAFROC_data.xlsx in this package,
# so I provide it by making the same xlsx file as the JAFROC_data.xlsx.
# Note that JAFROC_data.xlsx cannot remove,
# if it is removed, then devtools::run_examples() make an error.
Truth < readxl::read_excel(system.file("extdata",
"JAFROC_data.xlsx",
package="BayesianFROC"),
sheet = "Truth")
##### utils::View(Truth)
TP < readxl::read_excel( system.file("extdata",
"JAFROC_data.xlsx",
package="BayesianFROC"),
sheet = "TP")
#### utils::View(TP)
FP < readxl::read_excel( system.file("extdata",
"JAFROC_data.xlsx",
package="BayesianFROC"),
sheet = "FP")
#### utils::View(FP)
sample < list(TP=TP,FP=FP,Truth=Truth)
openxlsx::write.xlsx(sample,"JafrocDatasetExample.xlsx")
tcltk::tkmessageBox(
message="A file named
JafrocDatasetExample.xlsx
is created in the working directory")
# Now, we obtain an excel file named "JafrocDatasetExample.xlsx", which is same as
# the JAFROC_data.xlsx.
# whose format is available in the Jafroc software developed by Chakraborty.
# If you use your data, your data must has same format of "JafrocDatasetExample.xlsx".
# Note that other excel data must comply with the above format.
# Note that if you have an excel file
# which is formulated correctolly for our package,
# this process does not need.
# (0) From the above, we obtain "JafrocDatasetExample.xlsx"
# which is the multiple reader and multiple modality dataset
# for Jfroc analysis which is NOT implemented in our package,
# but Chakraborty's software called Jafroc or the R package Rjafroc.
#========================================================================================
# step 1) Convert a Jafroc data
#========================================================================================
# (1) Using "JafrocDatasetExample.xlsx" as an example excel file,
# we run the function to convert the excel file from Jafroc format
# to our format:
dataList < convertFromJafroc(
No.of.Modalities =5,
No.of.readers =4,
No.of.confidence.levels = 5
)
# In the variable, there is no xlsx file, since it is selected by interactive manner.
# So, please select the xlsx file obtained in step 0) or your own Jafroc
# .xlsx file.
#========================================================================================
# step 2) Fitting a model to data converted from Jafroc
#========================================================================================
# (2) Now, we obtain a list of an FROC dataset as an R object named "dataList".
# Using this, we can fit a model to the dataset by the following code.
fit < fit_Bayesian_FROC(dataList )
}### Only run examples in interactive R sessions
## End(Not run)
# Revised 2019. Jun 19
# Revised 2019. Dec 13
# Revised 2020 Feb
# Revised 2020 April

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