ti_occurve {PPQplan} | R Documentation |
Operating Characteristic (OC) Curves for the PPQ Plan using Tolerance Interval.
Description
The function for plotting the OC curve to show the PPQ plan based on the specification test, given lower and upper specification limits.
Usage
ti_occurve(attr.name, attr.unit, Llim, Ulim, mu, sigma, n, n.batch, alpha,
coverprob, side, add.reference, NV)
Arguments
attr.name |
user-defined attribute name |
attr.unit |
user-defined attribute unit |
Llim |
lower specification limit |
Ulim |
upper specification limit |
mu |
hypothetical mean of the attribute |
sigma |
hypothetical standard deviation of the attribute |
n |
sample size (number of locations) per batch |
n.batch |
number of batches for passing PPQ during validation |
alpha |
significant level for constructing the tolerance interval. |
coverprob |
coverage probability for constructing the tolerance interval |
side |
whether a 1-sided or 2-sided tolerance interval is required (determined by side = 1 or side = 2, respectively). |
add.reference |
logical; if |
NV |
nominal volume for the specification test. |
Value
OC curves for specification test and PPQ plan.
Author(s)
Yalin Zhu
References
Burdick, R. K., LeBlond, D. J., Pfahler, L. B., Quiroz, J., Sidor, L., Vukovinsky, K., & Zhang, L. (2017). Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry. Springer.
See Also
ti_pp
and rl_pp
.
Examples
## Not run:
ti_occurve(attr.name = "Sterile Concentration Assay", attr.unit="%",
mu=97, sigma=seq(0.1, 10, 0.1), Llim=95, Ulim=105, n=10, add.reference=TRUE)
ti_occurve(attr.name = "Sterile Concentration Assay", attr.unit="%",
mu=100, sigma=seq(0.1, 10, 0.1), Llim=95, Ulim=105, n=10, add.reference=TRUE)
ti_occurve(attr.name = "Extractable Volume", attr.unit = "% of NV=3mL",
Llim = 100, Ulim = Inf, mu=102.5, sigma=seq(0.2, 6 ,0.05), n=40,
alpha = 0.05, coverprob = 0.97, side=1, NV=3)
ti_occurve(attr.name = "Extractable Volume", attr.unit = "% of NV=3mL",
Llim = 100, Ulim = Inf, mu=102.5, sigma=seq(0.2, 6 ,0.05), n=40,
alpha = 0.05, coverprob = 0.992, side=1, NV=3)
## End(Not run)