| K.table {tolerance} | R Documentation |
Tabulated summary of k-factors for tolerance intervals based on normality. The user can specify multiple values for each of the three inputs.
K.table(n, alpha, P, side = 1, by.arg = c("n", "alpha", "P"))
n |
A vector of sample sizes. |
alpha |
The level chosen such that 1-alpha is the confidence level. Can be a vector. |
P |
The proportion of the population to be covered by this tolerance interval. Can be a vector |
side |
Whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2,
respectively). |
by.arg |
How you would like the output organized. If by.arg = "n", then the output provides a list of matrices
sorted by the values specified in n. The matrices have rows corresponding to the values specified by 1-alpha and columns
corresponding ot the values specified by P. If by.arg = "alpha", then the output provides a list of matrices
sorted by the values specified in 1-alpha. The matrices have rows corresponding to the values specified by n and columns
corresponding ot the values specified by P. If by.arg = "P", then the output provides a list of matrices
sorted by the values specified in P. The matrices have rows corresponding to the values specified by 1-alpha and columns
corresponding ot the values specified by n. |
The method used for estimating the k-factors is that due to Howe as it is generally viewed as more accurate than the Weissberg-Beatty method.
K.table returns a list with a structure determined by the argument by.arg described above.
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.
## Tables generated for each value of the sample size.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "n")
## Tables generated for each value of the confidence level.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "alpha")
## Tables generated for each value of the coverage proportion.
K.table(n = seq(50, 100, 10), alpha = c(0.01, 0.05, 0.10),
P = c(0.90, 0.95, 0.99), by.arg = "P")