paretotol.int {tolerance}

Pareto (or Power Distribution) Tolerance Intervals

Package:

tolerance

Version:

0.5.2

Description

Provides 1-sided or 2-sided tolerance intervals for data distributed according to either a Pareto distribution or a power distribution (i.e., the inverse Pareto distribution).

Usage

paretotol.int(x, alpha = 0.05, P = 0.99, side = 1,
              method = c("GPU", "DUN"), power.dist = FALSE)

Arguments

x: A vector of data which is distributed according to either a Pareto distribution or a power distribution.
alpha: The level chosen such that 1-alpha is the confidence level.
P: The proportion of the population to be covered by this tolerance interval.
side: Whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2, respectively).
method: The method for how the upper tolerance bound is approximated when transforming to utilize the relationship with the 2-parameter exponential distribution. "GPU" is the Guenther-Patil-Upppuluri method. "DUN" is the Dunsmore method, which was empirically shown to be an improvement for samples greater than or equal to 8. More information on these methods can be found in the "References".
power.dist: If TRUE, then the data is considered to be from a power distribution, in which case the output gives tolerance intervals for the power distribution. The default is FALSE.

Details

Recall that if the random variable X is distributed according to a Pareto distribution, then the random variable is distributed according to a 2-parameter exponential distribution. Moreover, if the random variable W is distributed according to a power distribution, then the random variable X = 1/W is distributed according to a Pareto distribution, which in turn means that the random variable is distributed according to a 2-parameter exponential distribution.

Values

paretotol.int returns a data frame with items:

alpha: The specified significance level.
P: The proportion of the population covered by this tolerance interval.
1-sided.lower: The 1-sided lower tolerance bound. This is given only if side = 1.
1-sided.upper: The 1-sided upper tolerance bound. This is given only if side = 1.
2-sided.lower: The 2-sided lower tolerance bound. This is given only if side = 2.
2-sided.upper: The 2-sided upper tolerance bound. This is given only if side = 2.

References

Dunsmore, I. R. (1978), Some Approximations for Tolerance Factors for the Two Parameter Exponential Distribution, Technometrics, 20, 317--318.

Engelhardt, M. and Bain, L. J. (1978), Tolerance Limits and Confidence Limits on Reliability for the Two-Parameter Exponential Distribution, Technometrics, 20, 37--39. Guenther, W. C., Patil, S. A., and Uppuluri, V. R. R. (1976), One-Sided β-Content Tolerance Factors for the Two Parameter Exponential Distribution, Technometrics, 18, 333--340.

Krishnamoorthy, K., Mathew, T., and Mukherjee, S. (2008), Normal-Based Methods for a Gamma Distribution: Prediction and Tolerance Intervals and Stress-Strength Reliability, Technometrics, 50, 69--78.

Examples

## 95%/99% 2-sided Pareto tolerance intervals 
## for a sample of size 500. 
 
set.seed(100)
x <- exp(r2exp(500, rate = 0.15, shift = 2))
out <- paretotol.int(x = x, alpha = 0.05, P = 0.99, side = 2,
                     method = "DUN", power.dist = FALSE)
out
 
plottol(out, x, plot.type = "both", side = "two", 
        x.lab = "Pareto Data")

Documentation reproduced from package tolerance, version 0.5.2. License: GPL (>= 2)

Comments

by kinneybowles on Jan 9, 2013

solutions are properly delivered so it's just worthy to have let the problem solver rest in the fauteuil relax