plotly version of norm.OC)plotly_normOC is an updated function rooted in norm.OC.
plotly_normOC(k = NULL,
alpha = NULL,
P = NULL,
n,
side = 1,
method = c("HE", "HE2", "WBE", "ELL", "KM", "EXACT", "OCT"),
m = 50,
range.min = NULL,
range.max = NULL,
x.lab.size = NULL,
y.lab.size = NULL,
x.tick.size = NULL,
y.tick.size = NULL,
title = NULL,
title.size = NULL,
title.position.x = NULL,
title.position.y = NULL,
legend.size = NULL,
x.cex = NULL,
line.width = NULL,
line.type = c("solid","dash","dot","dashdot"))norm.OC returns a figure with the OC curves constructed using the specifications in the arguments.
If wanting OC curves where the confidence level or content level is on the y-axis, then a single positive value of k must be specified. This would be the target k-factor for the desired tolerance interval. If k = NULL, then OC curves will be constructed where the k-factor value is found for given levels of alpha, P, and n.
The set of levels chosen such that 1-alpha 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 k-factor 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.
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 k-factor 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.
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.
Whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2, respectively).
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. "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 Weissberg-Beatty method, but is computationally simpler. "WBE" is the
Weissberg-Beatty method (also called the Wald-Wolfowitz method), which performs similarly to the first Howe method for larger sample sizes. "ELL" is
the Ellison correction to the Weissberg-Beatty 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 Krishnamoorthy-Mathew approximation to the exact solution, which works well for larger sample sizes. "EXACT" computes the
k-factor 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 k-factor when controlling the tails so that there is not more than (1-P)/2 of the data in each tail of the distribution.
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.
The minimum value of the y-axis.
The maximum value of the y-axis.
Size of label of the x-axis.
Size of label of the y-axis.
Size of tick marks on the x-axis.
Sze of tick marks on the y-axis.
The main title on top of the plot.
Size of the title.
Horizontal position of the title.
Vertical position of the title.
Size of the legend.
Size of data points.
Width of lines connecting data points.
The type of lines connection data points.
Young, D. S. (2016), Normal Tolerance Interval Procedures in the tolerance Package, The R Journal, 8, 200--212.
K.factor, normtol.int, norm.OC
## The three types of OC-curves that can be constructed
## with the ggnorm.OC function.
plotly_normOC(k = 4, alpha = NULL, P = c(0.90, 0.95, 0.99),
n = 10:20, side = 1)
plotly_normOC(k = 4, alpha = c(0.01, 0.05, 0.10), P = NULL,
n = 10:20, side = 1)
plotly_normOC(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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