AUC | R Documentation |
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.
## Default S3 method:
AUC(d, se.d, scale = 1, CI.alpha = 0.05, ...)
## S3 method for class 'anota'
AUC(d, CI.alpha = 0.05, ...)
d |
a unit lenght vector with the value of d-prime for which AUC
is to be computed or a |
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 |
CI.alpha |
the type I level of the confidence interval of AUC |
... |
additional arguments passed |
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
.
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 |
lower |
the lower confidence limit |
upper |
the upper confidence limit |
CI.alpha |
echoes the provided |
Rune Haubo B Christensen
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.