bayesnormtolint: Bayesian Normal Tolerance Intervals
In tolerance: Statistical Tolerance Intervals and Regions

Description Usage Arguments Details Value References See Also Examples

Provides 1-sided or 2-sided Bayesian tolerance intervals under the conjugate prior for data distributed according to a normal distribution.

bayesnormtol.int(x = NULL, norm.stats = list(x.bar = NA, 
                 s = NA, n = NA), alpha = 0.05, P = 0.99, 
                 side = 1, method = c("HE", "HE2", "WBE", 
                 "ELL", "KM", "EXACT", "OCT"), m = 50,
                 hyper.par = list(mu.0 = NULL, 
                 sig2.0 = NULL, m.0 = NULL, n.0 = NULL))

`x`	A vector of data which is distributed according to a normal distribution.
`norm.stats`	An optional list of statistics that can be provided in-lieu of the full dataset. If provided, the user must specify all three components: the sample mean (`x.bar`), the sample standard deviation (`s`), and the sample size (`n`).
`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 calculating the k-factors. The k-factor for the 1-sided tolerance intervals is performed exactly and thus is the same for the chosen method. `"HE"` is the Howe method and is often viewed as being extremely accurate, even for small sample sizes. `"HE2"` is a second method due to Howe, which performs similarly to the Weissberg-Beatty method, but is computationally simpler. `"WBE"` is the Weissberg-Beatty method (also called the Wald-Wolfowitz method), which performs similarly to the first Howe method for larger sample sizes. `"ELL"` is the Ellison correction to the Weissberg-Beatty method when `f` is appreciably larger than `n^2`. A warning message is displayed if `f` is not larger than `n^2`. `"KM"` is the Krishnamoorthy-Mathew approximation to the exact solution, which works well for larger sample sizes. `"EXACT"` computes the k-factor exactly by finding the integral solution to the problem via the `integrate` function. Note the computation time of this method is largely determined by `m`. `"OCT"` is the Owen approach to compute the k-factor when controlling the tails so that there is not more than (1-P)/2 of the data in each tail of the distribution.
`m`	The maximum number of subintervals to be used in the `integrate` function. This is necessary only for `method = "EXACT"` and `method = "OCT"`. The larger the number, the more accurate the solution. Too low of a value can result in an error. A large value can also cause the function to be slow for `method = "EXACT"`.
`hyper.par`	A list consisting of the hyperparameters for the conjugate prior: the hyperparameters for the mean (`mu.0` and `n.0`) and the hyperparameters for the variance (`sig2.0` and `m.0`).

Note that if one considers the non-informative prior distribution, then the Bayesian tolerance intervals are the same as the classical solution, which can be obtained by using normtol.int.

bayesnormtol.int returns a data frame with items:

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

Aitchison, J. (1964), Bayesian Tolerance Regions, Journal of the Royal Statistical Society, Series B, 26, 161–175.

Guttman, I. (1970), Statistical Tolerance Regions: Classical and Bayesian, Charles Griffin and Company.

Young, D. S., Gordon, C. M., Zhu, S., and Olin, B. D. (2016), Sample Size Determination Strategies for Normal Tolerance Intervals Using Historical Data, Quality Engineering, 28, 337–351.

Normal, normtol.int, K.factor

 
## 95%/85% 1-sided Bayesian normal tolerance limits for 
## a sample of size 100. 

set.seed(100)
x <- rnorm(100)
out <- bayesnormtol.int(x = x, alpha = 0.05, P = 0.85, 
                        side = 1, method = "EXACT", 
                        hyper.par = list(mu.0 = 0, 
                        sig2.0 = 1, n.0 = 10, m.0 = 10))
out

plottol(out, x, plot.type = "both", side = "upper", 
        x.lab = "Normal Data")