# bonftolint: Approximate 2-Sided Tolerance Intervals that Control the... In tolerance: Statistical Tolerance Intervals and Regions

## Description

This function allows the user to control what proportion of the population is to be in the tails of the given distribution for a 2-sided tolerance interval. The result is a conservative approximation based on Bonferroni's inequality.

## Usage

 `1` ```bonftol.int(fn, P1 = 0.005, P2 = 0.005, alpha = 0.05, ...) ```

## Arguments

 `fn` The function name for the 2-sided tolerance interval to be calculated. `P1` The proportion of the population not covered in the lower tail of the distribution. `P2` The proportion of the population not covered in the upper tail of the distribution. `alpha` The level chosen such that `1-alpha` is the confidence level. `...` Additional arguments passed to `fn`, including the data. All arguments that would be specified in `fn` must also be specified here.

## Value

The results for the 2-sided tolerance interval procedure are reported. See the corresponding help file for `fn` about specific output. Note that the (minimum) proportion of the population to be covered by this interval is `1 - (P1 + P2)`.

## Note

This function can be used with any 2-sided tolerance interval function, including the regression tolerance interval functions.

## References

Jensen, W. A. (2009), Approximations of Tolerance Intervals for Normally Distributed Data, Quality and Reliability Engineering International, 25, 571–580.

Patel, J. K. (1986), Tolerance Intervals - A Review, Communications in Statistics - Theory and Methodology, 15, 2719–2762.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ``` ## 95%/97% tolerance interval for normally distributed ## data controlling 1% of the data is in the lower tail ## and 2% of the data in the upper tail. set.seed(100) x <- rnorm(100, 0, 0.2) bonftol.int(normtol.int, x = x, P1 = 0.01, P2 = 0.02, alpha = 0.05, method = "HE") ```

tolerance documentation built on May 2, 2019, 4:01 a.m.