sensR (version 1.5-2)

AUC: 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

# S3 method for default
AUC(d, se.d, scale = 1, CI.alpha = 0.05, ...)
# S3 method for 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

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

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.

Examples

# NOT RUN {
## 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)

# }
# NOT RUN {
# }