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

PPQ_ggplot(attr.name, attr.unit, Llim, Ulim, mu, sigma, n, n.batch, k,
test.point, dynamic)

Arguments

attr.name

(optional) user-defined attribute name for PPQ assessment

attr.unit

(optional) 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

k

general multiplier for constructing the specific interval

test.point

(optional) actual process data points for testing whether the processes pass PPQ

dynamic

logical; if TRUE, then convert the heatmap ggplot to dynamic graph using plotly.

Value

Dynamic 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

PPQ_pp and PPQ_occurve.

Author

Yalin Zhu

Examples

if (FALSE) { mu <- seq(95, 105, 0.1) sigma <- seq(0.1,1.7,0.1) PPQ_ggplot(attr.name = "Sterile Concentration Assay", attr.unit = "%", Llim=95, Ulim=105, mu = mu, sigma = sigma, k=2.373, dynamic = FALSE) test <- data.frame(mu=c(97,98.3,102.5), sd=c(0.55, 1.5, 0.2)) PPQ_ggplot(attr.name = "Sterile Concentration Assay", attr.unit = "%", Llim=95, Ulim=105, mu = mu, sigma = sigma, k=2.373, test.point = test) }