norm.ss | R Documentation |
Provides minimum sample sizes for a future sample size when constructing normal tolerance intervals. Various strategies are available for determining the sample size, including strategies that incorporate known specification limits.
norm.ss(x = NULL, alpha = 0.05, P = 0.99, delta = NULL,
P.prime = NULL, side = 1, m = 50, spec = c(NA, NA),
hyper.par = list(mu.0 = NULL, sig2.0 = NULL,
m.0 = NULL, n.0 = NULL), method = c("DIR",
"FW", "YGZO"))
x |
A vector of current data that is distributed according to a normal distribution. This is only required for |
alpha |
The level chosen such that |
P |
The proportion of the population to be covered by this tolerance interval. |
delta |
The precision measure for the future tolerance interval as specified under the Faulkenberry-Weeks method. |
P.prime |
The proportion of the population (greater than |
side |
Whether a 1-sided or 2-sided tolerance interval is required (determined by |
m |
The maximum number of subintervals to be used in the |
spec |
A vector of length 2 given known specification limits. These are required when |
hyper.par |
Necessary parameter values for the different methods. If |
method |
The method for performing the sample size determination. |
norm.ss
returns a data frame with items:
alpha |
The specified significance level. |
P |
The proportion of the population covered by this tolerance interval. |
delta |
The user-specified or calculated precision measure. Not returned if |
P.prime |
The user-specified or calculated closeness measure. Not returned if |
n |
The minimum sample size determined using the conditions specified for this function. |
Faulkenberry, G. D. and Weeks, D. L. (1968), Sample Size Determination for Tolerance Limits, Technometrics, 10, 343–348.
Young, D. S., Gordon, C. M., Zhu, S., and Olin, B. D. (2016), Sample Size Determination Strategies for Normal Tolerance Intervals Using Historical Data, Quality Engineering, 28, 337–351.
bayesnormtol.int
, Normal
, normtol.int
## Sample size determination for 95%/95% 2-sided normal
## tolerance intervals using the direct method.
set.seed(100)
norm.ss(alpha = 0.05, P = 0.95, side = 2, spec = c(-3, 3),
method = "DIR", hyper.par = list(mu.0 = 0,
sig2.0 = 1))
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