# plotFROC: Draw FROC curves by two parameters a and b In BayesianFROC: FROC Analysis by Bayesian Approaches

## Description

Plot FROC curves based on two parameters a and b.

## Usage

 ```1 2 3 4 5 6 7 8``` ```plotFROC( a, b, mesh.for.drawing.curve = 10000, upper_x = 1, upper_y = 1, lower_y = 0 ) ```

## Arguments

 `a` An arbitrary real number. It is no need to require any assumption, but I use such as `a`=μ/σ, where μ is a mean of signal distribution and σ is its standard deviation in the bi-normal assumption. `b` An arbitrary positive real number. I use such as `b`=1/σ, where σ is a standard deviation of signal distribution in the bi-noraml assumption. `mesh.for.drawing.curve` A positive large integer, indicating number of dots drawing the curves, Default =10000. `upper_x` A positive real number, indicating the frame size of drawing picture. `upper_y` A positive real number, indicating the frame size of drawing picture. `lower_y` A positive real number, indicating the frame size of drawing picture.

## Details

FROC curve is the alternative notion of ROC curve in signal detection theory.

The definition of FROC curve is

(x(t),y(t) ) = (t, 1 - Φ( b* Φ^{-1}(exp(-t)) -a ) )

where, Φ() is the cumulative distribution function of the standard Gaussian distribution and Φ^{-1}() is its inverse mapping.

Revised 2019 NOv 27

## Examples

 ```1 2 3``` ```dark_theme() plotFROC(0.1,0.2) ```

BayesianFROC documentation built on Jan. 13, 2021, 5:22 a.m.