Description
Usage
Arguments
Value
References
See Also
Examples
Provides OCtype curves to illustrate how values of the kfactors for normal tolerance intervals, confidence levels, and content levels change as a function of the sample size.
 (k = , alpha = , P = , n, side = 1,
method = ("HE", "HE2", "WBE", "ELL", "KM", "EXACT",
"OCT"), m = 50)

k 
If wanting OC curves where the confidence level or content level is on the yaxis, then a single positive value of k must be specified. This would be the target kfactor for the desired tolerance interval. If k = NULL , then OC curves will be constructed where the kfactor value is found for given levels of alpha , P , and n .

alpha 
The set of levels chosen such that 1alpha are confidence levels. If wanting OC curves where the content level is being calculated, then each curve will correspond to a level in the set of alpha . If a set of P values is specified, then OC curves will be constructed where the kfactor is found and each curve will correspond to each combination of alpha and P . If alpha = NULL , then OC curves will be constructed to find the confidence level for given levels of k , P , and n .

P 
The set of content levels to be considered. If wanting OC curves where the confidence level is being calculated, then each curve will correspond to a level in the set of P . If a set of alpha values is specified, then OC curves will be constructed where the kfactor is found and each curve will correspond to each combination of alpha and P . If P = NULL , then OC curves will be constructed to find the content level for given levels of k , alpha , and n .

n 
A sequence of sample sizes to consider. This must be a vector of at least length 2 since all OC curves are constructed as functions of n .

side 
Whether a 1sided or 2sided tolerance interval is required (determined by side = 1 or side = 2 , respectively).

method 
The method for calculating the kfactors. The kfactor for the 1sided 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 WeissbergBeatty method, but is computationally simpler. "WBE" is the
WeissbergBeatty method (also called the WaldWolfowitz method), which performs similarly to the first Howe method for larger sample sizes. "ELL" is
the Ellison correction to the WeissbergBeatty 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 KrishnamoorthyMathew approximation to the exact solution, which works well for larger sample sizes. "EXACT" computes the
kfactor 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 kfactor when controlling the tails so that there is not more than (1P)/2 of the data in each tail of the distribution.

m 
The maximum number of subintervals to be used in the integrate function, which is used for the underlying exact method for calculating the normal tolerance intervals.

norm.OC
returns a figure with the OC curves constructed using the specifications in the arguments.
Young, D. S. (2016), Normal Tolerance Interval Procedures in the tolerance Package, The R Journal, 8, 200–212.
K.factor
, normtol.int
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## The three types of OCcurves that can be constructed
## with the norm.OC function.
(k = 4, alpha = , P = (0.90, 0.95, 0.99),
n = 10:20, side = 1)
(k = 4, alpha = (0.01, 0.05, 0.10), P = ,
n = 10:20, side = 1)
(k = , P = (0.90, 0.95, 0.99),
alpha=(0.01,0.05,0.10), n = 10:20, side = 1)

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