# logistolint: Logistic (or Log-Logistic) Tolerance Intervals In tolerance: Statistical Tolerance Intervals and Regions

## Description

Provides 1-sided or 2-sided tolerance intervals for data distributed according to a logistic or log-logistic distribution.

## Usage

 ```1 2``` ```logistol.int(x, alpha = 0.05, P = 0.99, log.log = FALSE, side = 1) ```

## Arguments

 `x` A vector of data which is distributed according to a logistic or log-logistic 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. `log.log` If `TRUE`, then the data is considered to be from a log-logistic distribution, in which case the output gives tolerance intervals for the log-logistic distribution. The default is `FALSE`. `side` Whether a 1-sided or 2-sided tolerance interval is required (determined by `side = 1` or `side = 2`, respectively).

## Details

Recall that if the random variable X is distributed according to a log-logistic distribution, then the random variable Y = ln(X) is distributed according to a logistic distribution.

## Value

`logistol.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

Balakrishnan, N. (1992), Handbook of the Logistic Distribution, Marcel Dekker, Inc.

Hall, I. J. (1975), One-Sided Tolerance Limits for a Logistic Distribution Based on Censored Samples, Biometrics, 31, 873–880.

## See Also

`Logistic`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ``` ## 90%/95% 1-sided logistic tolerance intervals for a sample ## of size 20. set.seed(100) x <- rlogis(20, 5, 1) out <- logistol.int(x = x, alpha = 0.10, P = 0.95, log.log = FALSE, side = 1) out plottol(out, x, plot.type = "control", side = "two", x.lab = "Logistic Data") ```

tolerance documentation built on Feb. 6, 2020, 5:08 p.m.