norm.OC | R Documentation |
Provides OC-type curves to illustrate how values of the k-factors for normal tolerance intervals, confidence levels, and content levels change as a function of the sample size.
norm.OC(k = NULL, alpha = NULL, P = NULL, n, side = 1,
method = c("HE", "HE2", "WBE", "ELL", "KM", "EXACT",
"OCT"), m = 50)
k |
If wanting OC curves where the confidence level or content level is on the y-axis, then a single positive value of |
alpha |
The set of levels chosen such that |
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 |
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 |
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 |
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
## The three types of OC-curves that can be constructed
## with the norm.OC function.
norm.OC(k = 4, alpha = NULL, P = c(0.90, 0.95, 0.99),
n = 10:20, side = 1)
norm.OC(k = 4, alpha = c(0.01, 0.05, 0.10), P = NULL,
n = 10:20, side = 1)
norm.OC(k = NULL, P = c(0.90, 0.95, 0.99),
alpha=c(0.01,0.05,0.10), n = 10:20, side = 1)
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