AUC.default {sensR} | 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).
## Default S3 method: AUC(d, scale = 1, se.d, CI.alpha = 0.05, ...) ## S3 method for class 'discrim': 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 discrim 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 |
The function calls integrate
to obtain the area under the ROC
curve implied by d
and scale
.
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 does not take the uncertainty in estimating the scale
nor that of estimating the standard error of d
into account.
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 |
Rune Haubo B Christensen
(odor <- matrix(c(112, 112, 72, 53, 22, 4, 7, 38, 50, 117, 101, 62), 2, byrow = TRUE)) (d.primes <- SDT(odor)[,3]) for(i in 1:5) print(AUC(d.primes[i])) ## Provide standard error of d-prime and compute CI: fm1 <- AnotA(8, 25, 1, 25) AUC(fm1$coef, , fm1$se) AUC(fm1)