pi_ctplot {PPQplan} | R Documentation |
Heatmap/Contour Plot for Assessing Power of the CQA PPQ Plan Using Prediction Interval.
Description
The function for plotting the heatmap to evaluate the PPQ plan based on the specification test, given lower and upper specification limits.
Usage
pi_ctplot(attr.name, attr.unit, Llim, Ulim, mu, sigma, n, n.batch, alpha, test.point)
Arguments
attr.name |
user-defined attribute name for PPQ assessment |
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 prediction interval. |
test.point |
(optional) actual process data points for testing whether the processes pass PPQ |
Value
Heatmap (or Contour Plot) for PPQ Assessment.
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
pi_pp
and pi_occurve
.
Examples
## Not run:
## Example verifying simulation resutls in the textbook page 249
mu <- seq(95, 105, 0.1)
sigma <- seq(0.2, 3.5, 0.1)
pi_ctplot(attr.name = "Composite Assay", attr.unit = "%LC",
mu = mu, sigma = sigma, Llim=95, Ulim=105)
mu <- seq(90, 110, 0.5)
pi_ctplot(attr.name = "Composite Assay", attr.unit = "%LC",
mu = mu, sigma = sigma, Llim=90, Ulim=110)
mu <- seq(95,105,0.1)
sigma <- seq(0.1,2.5,0.1)
pi_ctplot(attr.name = "Sterile Concentration Assay", attr.unit = "%",
mu = mu, sigma = sigma, Llim=95, Ulim=105)
test <- data.frame(mean=c(97,98.3,102.5), sd=c(0.55, 1.5, 1.2))
pi_ctplot(attr.name = "Sterile Concentration Assay", attr.unit = "%", Llim=95, Ulim=105,
mu = mu, sigma = sigma, test.point=test)
## End(Not run)