Description Usage Arguments Value References See Also Examples

Tolerance intervals for each factor level in a balanced (or nearly-balanced) ANOVA.

1 2 3 | ```
anovatol.int(lm.out, data, alpha = 0.05, P = 0.99, side = 1,
method = c("HE", "HE2", "WBE", "ELL", "KM",
"EXACT", "OCT"), m = 50)
``` |

`lm.out` |
An object of class |

`data` |
A data frame consisting of the data fitted in |

`alpha` |
The level chosen such that |

`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 |

`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. |

`m` |
The maximum number of subintervals to be used in the |

`anovatol.int`

returns a list where each element is a data frame corresponding to each main effect (i.e.,
factor) tested in the ANOVA and the rows of each data frame are the levels of that factor. The columns of each data
frame report the following:

`mean` |
The mean for that factor level. |

`n` |
The effective sample size for that factor level. |

`k` |
The k-factor for constructing the respective factor level's tolerance interval. |

`1-sided.lower` |
The 1-sided lower tolerance bound. This is given only if |

`1-sided.upper` |
The 1-sided upper tolerance bound. This is given only if |

`2-sided.lower` |
The 2-sided lower tolerance bound. This is given only if |

`2-sided.upper` |
The 2-sided upper tolerance bound. This is given only if |

Howe, W. G. (1969), Two-Sided Tolerance Limits for Normal Populations - Some Improvements, *Journal of the
American Statistical Association*, **64**, 610–620.

Weissberg, A. and Beatty, G. (1969), Tables of Tolerance Limit Factors for Normal Distributions, *Technometrics*,
**2**, 483–500.

`K.factor`

, `normtol.int`

, `lm`

, `anova`

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## 90%/95% 2-sided tolerance intervals for a 2-way ANOVA
## using the "warpbreaks" data.
attach(warpbreaks)
lm.out <- lm(breaks ~ wool + tension)
out <- anovatol.int(lm.out, data = warpbreaks, alpha = 0.10,
P = 0.95, side = 2, method = "HE")
out
plottol(out, x = warpbreaks)
``` |

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