K.factor {tolerance} | R Documentation |
Estimates k-factors for tolerance intervals based on normality by using either the Howe method or the Weissberg-Beatty method.
K.factor(n, alpha = 0.05, P = 0.99, side = 1, method = c("HE", "WBE"))
n |
The sample size. |
alpha |
The level chosen such that 1-alpha is the confidence level. |
P |
The proportion of the population to be covered by this tolerance interval. |
side |
Whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2 ,
respectively). |
method |
The method for calculating the k-factors. The k-factor for the 1-sided tolerance intervals
is performed exactly and thus the same for either method chosen. "HE" is the
Howe method and is often viewed as being extremely accurate, even for small sample sizes. "WBE" is the
Weissberg-Beatty method, which performs similarly to the Howe method for larger sample sizes. |
K.factor
returns the k-factor for tolerance intervals based on normality with the arguments specified 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.
## Showing the effect of the two estimation methods as the ## sample size increases. K.factor(10, P = 0.95, side = 2, method = "HE") K.factor(10, P = 0.95, side = 2, method = "WBE") K.factor(100, P = 0.95, side = 2, method = "HE") K.factor(100, P = 0.95, side = 2, method = "WBE") K.factor(1000, P = 0.95, side = 2, method = "HE") K.factor(1000, P = 0.95, side = 2, method = "WBE")