data_generate_NaN_in_fit_with_iteration1111_seed1234: *NaN in samplings* A Single Reader and A Single Modality

In BayesianFROC: FROC Analysis by Bayesian Approaches

Description

A list, representing FROC data consisting of hits, false alarms, number of lesions, number of images. We fit a FROC model to the data.

Format

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 a single modality case

——————————————————————————————————

NI=57,NL=259	confidence level	No. of false alarms	No. of hits
In R console ->	c	f	h
-----------------------	-----------------------	-----------------------------	-------------
definitely present	3	0	97
probably present	2	12	35
questionable	1	67	25

—————————————————————————————————

* 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( 97, 35, 25 ),

f = c( 0, 12, 67 ),

NL = 259,

NI = 57,

C = 3)

This object dat can be passed to the function fit_Bayesian_FROC() as the following manner fit_Bayesian_FROC(dat).

Details

The fitted model object includes NaN in samplings. f <- fit_Bayesian_FROC( ite = 1111, summary = FALSE, cha = 1, dataList = data_generate_NaN_in_fit_with_iteration1111_seed1234 ,see = 1234 )

References

Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data, Dev P. Chakraborty.

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