Prob | R Documentation |
Compute confidence intervals and point estimates for the probability R, under parametric model assumptions for Y and X. Y and X are two independent continuous random variable from two different populations.
Prob(ydat, xdat, distr = "exp", method = "RPstar", level = 0.05)
ydat |
data vector of the sample measurements from Y. |
xdat |
data vector of the sample measurements from X. |
distr |
character string specifying the type of distribution assumed for Y and X. Possible choices for |
method |
character string specifying the methodological approach used for inference (confidence intervals and point estimates) on the AUC.
The argument |
level |
it is the α that supplies the nominal level (1-α) chosen for the confidence interval. |
PROB |
Point estimate of R = P(Y<X). This value corresponds to the maximum likelihoos estimate if method "Wald" or "RP" is chosen; otherwise, when method "RPstar" is selected, estimate is obtained from the estimating equaltion r_p^* = 0. |
C.Interval |
Confidence interval of R at confidence level (1-α). |
Giuliana Cortese
Cortese G., Ventura L. (2013). Accurate higher-order likelihood inference on R=P(Y<X). Computational Statistics, 28:1035-1059.
wald
, rp
, rpstar
# data from the first population Y <- rnorm(15, mean=5, sd=1) # data from the second population X <- rnorm(10, mean=7, sd=1.5) level <- 0.01 ## \eqn{\alpha} level # estimate and confidence interval under the assumption of two # normal variables with different variances. Prob(Y, X, "norm_DV", "RPstar", level) # method has to be set equal to "RPstar".
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