The function for plotting the heatmap to evaluate the PPQ plan based on the specification test, given lower and upper specification limits.

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.

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.

Author

Yalin Zhu

Examples

if (FALSE) { ## 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) }