Description Format Details Author(s) References See Also
A list, representing FROC data consisting of hits, false alarms, number of lesions, number of images, to which we fit a FROC model.
A list consists of two integer vectors f, h
and three integers NL, NI, C
.
f
Non-negative integer vector specifying number of false alarms associated with each confidence level. The first component corresponding to the highest confidence level.
h
Non-negative 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=259 | confidence level | No. of false alarms | No. of hits |
In R console -> | c | f | h |
----------------------- | ----------------------- | ----------------------------- | ------------- |
definitely present | 4 | 11 | 11 |
probably present | 3 | 1 | 97 |
subtle | 2 | 14 | 32 |
very subtle | 1 | 74 | 31 |
—————————————————————————————————
* 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(
h = c(11, 97, 32, 31),
f = c( 11, 1, 14, 74),
NL = 259,
NI = 57,
C = 4)
This object dat
can be passed to the function fit_Bayesian_FROC()
as the following manner fit_Bayesian_FROC(dat)
.
This data-set is very bad fitting. Even if the MCMC sampling is very good, however, the FPF and TPF are not on the FROC curve.
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
.
Issei Tsunoda tsunoda.issei1111@gmail.com
I love you.
viewdata()
, which shows your data confortably by knitr::kable()
.
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