Type: | Package |
Title: | Computation and Estimation of Reliability of Stress-Strength Models |
Version: | 1.0.2 |
Date: | 2016-04-29 |
Author: | Alessandro Barbiero <alessandro.barbiero@unimi.it>, Riccardo Inchingolo <dott.inchingolo_r@libero.it> |
Maintainer: | Alessandro Barbiero <alessandro.barbiero@unimi.it> |
Description: | Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures. |
License: | GPL-2 | GPL-3 [expanded from: GPL] |
LazyLoad: | yes |
Packaged: | 2016-04-30 07:57:22 UTC; Barbiero |
Repository: | CRAN |
Date/Publication: | 2016-05-01 00:44:38 |
NeedsCompilation: | no |
Computation and Sample Estimation of Reliability of Stress-Strength Models
Description
Reliability of (normal) stress-strength models and for building two-side or one-side confidence intervals according to different approximate procedures.
Details
Package: | StressStrength |
Type: | Package |
Version: | 1.0.2 |
Date: | 2016-04-29 |
License: | GPL |
LazyLoad: | yes |
Author(s)
Alessandro Barbiero, Riccardo Inchingolo
Maintainer: Alessandro Barbiero <alessandro.barbiero@unimi.it>
References
Kotz S, Lumelskii Y, Pensky M (2003) The stress-strength model and its generalizations: theory and applications. World Scientific, Singapore
Guo H, Krishnamoorthy K (2004) New approximate inferential methods for the reliability parameter in a stress-strength model: The normal case. Commun Stat Theory Methods 33:1715-1731
Computation of reliability of stress-strength models
Description
For a stress-strength model, with independent r.v. X and Y representing the strength and the stress respectively, the function computes the reliability R=P(X>Y)
Usage
SSR(parx, pary, family = "normal")
Arguments
parx |
parameters of X distribution (for the normal distribution, mean |
pary |
parameters of Y distribution (for the normal distribution, mean |
family |
family distribution for both X and Y (now, only "normal" available) |
Details
The function computes R=P(X>Y)
where X and Y are independent r.v. following the family
distribution with distributional parameters parx
and pary
.
Value
R=P(X>Y)
. For normal distributions, R=\Phi(d)
with d=(\mu_x-\mu_y)/\sqrt{\sigma_x^2+\sigma_y^2}
.
Author(s)
Alessandro Barbiero, Riccardo Inchingolo
References
Kotz S, Lumelskii Y, Pensky M (2003) The stress-strength model and its generalizations: theory and applications. World Scientific, Singapore
See Also
Examples
# let X be a normal r.v. with mean 1 and sd 1;
# and Y a normal r.v. with mean 0 and sd 2
# X and Y independent
parx<-c(1, 1)
pary<-c(0, 2)
# reliability of the stress-strength model (X=strength, Y=stress)
SSR(parx,pary)
# changing the parameters of Y
pary<-c(1.5, 2)
# reliability of the stress-strength model (X=strength, Y=stress)
SSR(parx,pary)
Sample estimation of reliability of stress-strength models
Description
The function provides sample estimates of reliability of stress-strength models, where stress and strength are modeled as independent r.v., whose distribution form is known except for the values of its parameters, assumed all unknown
Usage
estSSR(x, y, family="normal", twoside=TRUE, type="RG", alpha=0.05, B=2000)
Arguments
x |
a random sample from r.v. X modeling strength |
y |
a random sample from r.v. Y modeling stress |
family |
the distribution of both X and Y |
twoside |
if TRUE, the function computes two-side confidence intervals; otherwise, one-side (a lower bound) |
type |
type of confidence interval (CI) to be built. For the normal family, "RG" stands for Reiser-Guttman, "AN" for large sample (asymptotically normal), "LOGIT" or "ARCSIN" for logit or arcsin variance stabilizing tranformations, "B" for percentile bootstrap, "GK" for Guo-Krishnamoorthy (one-sided only). |
alpha |
the complement to one of the nominal confidence level |
B |
number of bootstrap replicates (for type "B") |
Details
For more details, please have a look at the references listed below
Value
A list comprising
ML_est |
the sample value of the maximum likelihood estimator; for normal r.v. |
Downton_est |
(for normal r.v.) the sample value of one of the approximated UMVU estimators proposed by Downton |
CI |
the confidence interval |
confidence_level |
the nominal confidence level |
Author(s)
Alessandro Barbiero, Riccardo Inchingolo
References
Barbiero A (2011) Confidence Intervals for Reliability of Stress-Strength Models in the Normal Case, Comm Stat Sim Comp 40(6):907-925
Downton F. (1973) The Estimation of Pr (Y < X) in the Normal Case, Technometrics , 15(3):551-558
Kotz S, Lumelskii Y, Pensky M (2003) The stress-strength model and its generalizations: theory and applications. World Scientific, Singapore
Guo H, Krishnamoorthy K (2004) New approximate inferential methods for the reliability parameter in a stress-strength model: The normal case. Commun Stat Theory Methods 33:1715-1731
Mukherjee SP, Maiti SS (1998) Stress-strength reliability in the Weibull case. Frontiers In Reliability 4:231-248. WorldScientific, Singapore
Reiser BJ, Guttman I (1986) Statistical inference for P(Y<X): The normal case. Technometrics 28:253-257
See Also
Examples
# distributional parameters of X and Y
parx<-c(1, 1)
pary<-c(0, 2)
# sample sizes
n<-10
m<-20
# true value of R
SSR(parx,pary)
# draw independent random samples from X and Y
x<-rnorm(n, parx[1], parx[2])
y<-rnorm(m, pary[1], pary[2])
# build two-sided confidence intervals
estSSR(x, y, type="RG")
estSSR(x, y, type="AN")
estSSR(x, y, type="LOGIT")
estSSR(x, y, type="ARCSIN")
estSSR(x, y, type="B")
estSSR(x, y, type="B",B=1000) # change number of bootstrap replicates
# and one-sided
estSSR(x, y, type="RG", twoside=FALSE)
estSSR(x, y, type="AN", twoside=FALSE)
estSSR(x, y, type="LOGIT", twoside=FALSE)
estSSR(x, y, type="ARCSIN", twoside=FALSE)
estSSR(x, y, type="B", twoside=FALSE)
estSSR(x, y, type="GK", twoside=FALSE)
# changing sample sizes
n<-20
m<-30
x<-rnorm(n, parx[1], parx[2])
y<-rnorm(m, pary[1], pary[2])
# build tow-sided confidence intervals
estSSR(x, y, type="RG")
estSSR(x, y, type="AN")
estSSR(x, y, type="LOGIT")
estSSR(x, y, type="ARCSIN")
estSSR(x, y, type="B")
Numerical solution for an equation involving noncentral T cdf
Description
It provides the solution of the equation F_t(q;df,x)=p
, where F_t
is the cdf (calculated in q
) of a non-central Student r.v. with df
degrees of freedom and unkwon noncentrality parameter x. In R code, gkf provides the solution of pt(q,df,x)=p
.
Usage
gkf(p, q, df, eps = 1e-05)
Arguments
p |
a probability |
q |
a real value |
df |
degrees of freedom of noncentral T |
eps |
tolerance |
Details
This function is used for building Guo-Krishnamoorthy confidence intervals for R
Value
the noncentrality parameter x
satisfying the equation F_t(q;df,x)=p
Author(s)
Alessandro Barbiero, Riccardo Inchingolo
References
Guo H, Krishnamoorthy K (2004) New approximate inferential methods for the reliability parameter in a stress-strength model: The normal case. Commun Stat Theory Methods 33:1715-1731
See Also
Examples
p<-0.95
q<-5
df<-12
ncp<-gkf(p, q, df)
ncp
# check if the result is correct
pt(q, df, ncp)
# OK
# changing the tolerance
ncp<-gkf(p, q, df, eps=1e-10)
ncp
pt(q, df, ncp)