hypertol.int | R Documentation |
Provides 1-sided or 2-sided tolerance intervals for hypergeometric random variables. From a sampling without replacement perspective, these limits use the proportion of units from group A (e.g., "black balls" in an urn) in a sample to bound the number of potential units drawn from group A in a future sample taken from the universe.
hypertol.int(x, n, N, m = NULL, alpha = 0.05, P = 0.99,
side = 1, method = c("EX", "LS", "CC"))
x |
The number of units from group A in the sample. Can be a vector, in which case the sum of |
n |
The size of the random sample of units selected. |
N |
The population size. |
m |
The quantity of units to be sampled from the universe for a future study. If |
alpha |
The level chosen such that |
P |
The proportion of units from group A in future samples of size |
side |
Whether a 1-sided or 2-sided tolerance interval is required (determined by |
method |
The method for calculating the lower and upper confidence bounds, which are used in the calculation
of the tolerance bounds. The default method is |
hypertol.int
returns a data frame with items:
alpha |
The specified significance level. |
P |
The proportion of units from group A in future samples of size |
rate |
The sampling rate determined by |
p.hat |
The proportion of units in the sample from group A, calculated by |
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 |
As this methodology is built using large-sample theory, if the sampling rate is less than 0.05, then a warning is generated stating that the results are not reliable. Also, compare the functionality of this procedure with the acc.samp
procedure, which is to determine a minimal acceptance limit for a particular sampling plan.
Brown, L. D., Cai, T. T., and DasGupta, A. (2001), Interval Estimation for a Binomial Proportion, Statistical Science, 16, 101–133.
Eichenberger, P., Hulliger, B., and Potterat, J. (2011), Two Measures for Sample Size Determination, Survey Research Methods, 5, 27–37.
Young, D. S. (2014), Tolerance Intervals for Hypergeometric and Negative Hypergeometric Variables, Sankhya: The Indian Journal of Statistics, Series B, 77(1), 114–140.
acc.samp
, Hypergeometric
## 90%/95% 1-sided and 2-sided hypergeometric tolerance
## intervals for a future sample of 30 when the universe
## is of size 100.
hypertol.int(x = 15, n = 50, N = 100, m = 30, alpha = 0.10,
P = 0.95, side = 1, method = "LS")
hypertol.int(x = 15, n = 50, N = 100, m = 30, alpha = 0.10,
P = 0.95, side = 1, method = "CC")
hypertol.int(x = 15, n = 50, N = 100, m = 30, alpha = 0.10,
P = 0.95, side = 2, method = "LS")
hypertol.int(x = 15, n = 50, N = 100, m = 30, alpha = 0.10,
P = 0.95, side = 2, method = "CC")
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