# AUC: AUC computation In perbrock/sensR: Thurstonian Models for Sensory Discrimination

 AUC R Documentation

## AUC computation

### Description

This is the default AUC function for scalar d-primes, which will compute Area Under the ROC curve (ROC is an acronym for receiver operating characteristic) assuming a normal distribution for the underlying percepts.

### Usage

``````
## Default S3 method:
AUC(d, se.d, scale = 1, CI.alpha = 0.05, ...)

## S3 method for class 'anota'
AUC(d, CI.alpha = 0.05, ...)

``````

### Arguments

 `d` a unit lenght vector with the value of d-prime for which AUC is to be computed or a `anota` object from the fitting of a A-not A test with `AnotA` `scale` a unit length vector giving the ratio of scale (ie. standard deviation) of the latent distribution for the no-class items relative to that of the yes-class items `se.d` standard error of `d` (d-prime). If provided, the function will compute confidence limits of value of AUC—cf. in section value. `CI.alpha` the type I level of the confidence interval of AUC `...` additional arguments passed `integrate`

### Details

The AUC is computed using the standard normal distribution function `pnorm`.

Confidence limits are based on a normal approximation of `d` and not of AUC. The limits are computed, if an estimate of the standard error of `d` is provided. Note that the limits do not take the uncertainty in estimating the scale nor that of estimating the standard error of `d` into account.

A print method is implemented for objects of class `AUC`.

### Value

A list with components. If `se.d` is supplied to the default method or if a discrim object is supplied, the object contains the latter three additional elements.

 `value` the estimated value of AUC `res.int` the result from the call to `integrate` `lower` the lower confidence limit `upper` the upper confidence limit `CI.alpha` echoes the provided `CI.alpha`

### Author(s)

Rune Haubo B Christensen

### Examples

``````
## Compute AUC from d-prime and confindence interval for the AUC:
fm1 <- AnotA(8, 25, 1, 25)
AUC(d=fm1\$coef, se.d=fm1\$se)
## The AUC-method for AnotA-objects can be used for convenience:
AUC(fm1)

``````

perbrock/sensR documentation built on Nov. 5, 2023, 10:41 a.m.