A list, representing an FROC dataset with divergent transitions .
Note that the maximal number of confidence level,
denoted by C
, are included,
however, confidence level vector c
should not be specified.
If specified, will be ignored ,
since it is created by c <c(rep(C:1))
in the program
and it does not refer from user input data,
where C
is the highest number of confidence levels.
Should write down your hits and
false alarms vector so that it is compatible with this automatically created vector c
.
A list consists of the following
integer vectors f, h
and integers NL, NI, C
.
f
Nonnegative integer vector specifying number of false alarms associated with each confidence level. The first component corresponding to the highest confidence level.
h
Nonnegative integer vector specifying number of Hits associated with each confidence level. The first component corresponding to the highest confidence level.
NL
A positive integer, representing Number of Lesions.
NI
A positive integer, representing Number of Images.
C
A positive integer, representing Number of Confidence level.
Contents:
A single reader and single modality case
——————————————————————————————————
NI=57,NL=269  confidence level  No. of false alarms  No. of hits 
In R console >  c  f  h 
       
definitely present  3  0  21 
probably present  2  7  4 
questionable  1  36  3 
—————————————————————————————————
* false alarms = False Positives = FP
* hits = True Positives = TP
Note that in FROC data, the confidence level means present (deseased, positive) case only. Since each reader marks their suspicous location only and it generate the hits and false alarms for his confidenc level representing that lesion is present. In the absent case, reader does not mark any locations and hence, the absent cofidence level does not relate this dataset.
Note that the first column of confidence level vector c
should not be specified. If specified, will be ignored , since it is created by c <c(rep(C:1))
automatically in the program and it does not refer from user input data even if it is specified explicitly, where C
is the highest number of confidence levels.
So you should check the compatibility of your data and the program's generating new confidence level vector by
a table which can be displayed by the function viewdata()
.
Note that The format for the above example data must be made by the following forms:
dat < list(
c=c(3,2,1), #Confidence level
h=c(21,4,3), #Number of hits for each confidence level
f=c(0,7,36), #Number of false alarms for each confidence level
NL=60, #Number of lesions
NI=30, #Number of images
C=3) #Number of confidence level
This R object dat
can be passed to the function fit_Bayesian_FROC()
as the following manner fit_Bayesian_FROC(dat)
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Not run:
#========================================================================================
# Change the zero cell to 1,
# then The number of divergent transitions are significantly decrease
# Thus, the divergent transtions is not rigid.
#========================================================================================
data < dataList.divergent.transition.in.case.of.srsc
data$f < c(1,7,36)
f < fit_Bayesian_FROC( ite = 1111, cha = 1, summary = TRUE, dataList = data )
## End(Not run)#dontrun

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