ProbYX-package | R Documentation |
Compute confidence intervals and point estimates for R, under parametric model assumptions for Y and X. Y and X are two independent continuous random variables from two different populations.
Package: | ProbYX |
Type: | Package |
Version: | 1.1 |
Date: | 2012-03-20 |
License: | GPL-2 |
LazyLoad: | yes |
The package can be used for computing accurate confidence intervals and
point estimates for the stress-strength (reliability) model R = P(Y<X); maximum likelihood estimates, Wald statistic, signed
log-likelihood ratio statistic and its modified version ca be computed.
The main function is Prob
, which evaluates confidence intervals and
point estimates under different approaches and parametric assumptions.
Giuliana Cortese
Maintainer: Giuliana Cortese gcortese@stat.unipd.it
Cortese G., Ventura L. (2013). Accurate higher-order likelihood inference on P(Y<X). Computational Statistics, 28:1035-1059.
Kotz S, Lumelskii Y, Pensky M. (2003). The Stress-Strength Model and its Generalizations. Theory and Applications. World Scientific, Singapore.
# 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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