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

## 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

 ```1 2 3 4 5``` ```## 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

 ```1 2 3 4 5``` ```## 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) ```

sensR documentation built on May 2, 2019, 9:43 a.m.