design_ccd: Defines a CCD for k quantitative factors

In rsurface: Design of Rotatable Central Composite Experiments and Response Surface Analysis

Description

Defines a rotatable central composite design (CCD) for experimentation with k quantitative factors and j replicates of the central point (which corresponds to the ‘average treatment combination’).

Usage

design_ccd(j, k)

Arguments

j	the number of replicates of the central point
k	the number of quantitative factors used or studied in the experimentation.

Value

Three output tables containing the level of replication (number of replicates) and the experimental uncoded values of the quantitative factors to be used for experimentation and one plot showing the corresponding variance of the predicted response.

“Factorial.Points”, the first table which contains the treatment combinations for a 2^k factorial design (which, in coded form, corresponds to the vertices of a square, a cube, or a hyper-cube when k = 2, 3 and more).

“Axial.Points”, the second table which contains 2k axial or “star” points.

“Central.Point”, the third table which contains the number of replicates for the central point, coded (0, 0, 0).

References

Mead, R., Gilmour, S. G., and Mead, A. 2012. Statistical Principles for the Design of Experiments: Applications to Real Experiments. Cambridge University Press, Cambridge.

Panneton, B., Philion, H., Dutilleul, P., Theriault, R., and Khelifi, M. 1999. Full factorial design versus central composite design: Statistical comparison and experimental implications for spray droplet deposition. Transactions of the American Society of Agricultural Engineers 42:877-883.

Examples

#Enter the function as shown below. The user will be prompted to input smallest
#and greatest values for each factor that will be used for experimentation.

if(interactive()){
 design_ccd(5, 3)
 }

rsurface documentation built on May 1, 2019, 6:27 p.m.