cpv: Cuboidal Prediction Variance

In VdgRsm: Plots of Scaled Prediction Variances for Response Surface Designs

Description

Create a variance dispersion graph for a response surface design in a cuboidal region.

Usage

cpv(design.matrix, design.matrix.2 = NULL, des.names = c("Design 1","Design 2"),
    add.pts = TRUE)

Arguments

design.matrix, design.matrix.2	Data frames of design points to be compared in coded or uncoded units. There should be one column for each factor in the design, and one row for each run in the design. The maximum number of factors is 6. If the number of factor is more than 4, only one design is allowed.
add.pts	Generate scaled prediction variances of random design points in the VDG. By default add.pts = TRUE.
des.names	A vector of descriptive names for designs in character strings.

Value

cpv is called to generate a variance sispersion graph when the number of factors k = 2, 3, or 4 and to generate side-by-side boxplots for k = 5 and 6. In the former case, a table of the minimum, maximum, and average of scaled prediction variances is also produced.

Examples

CCD1<- gen.CCD(n.vars = 3, n.center = 2, alpha = 1)
CCD2<- gen.CCD(n.vars = 3, n.center = 5, alpha = 1)
cpv(CCD1, CCD2, des.names = c("CCD with nc=2", "CCD with nc=5"), add.pts = FALSE)

Example output

$design.1
         Radius     max.V     min.V  average.V
 [1,] 0.0000000  3.586207  3.586207   3.586207
 [2,] 0.1428571  3.565088  3.563146   3.563921
 [3,] 0.2857143  3.532054  3.500971   3.513372
 [4,] 0.4285714  3.578070  3.420709   3.483491
 [5,] 0.5714286  3.854743  3.357406   3.555826
 [6,] 0.7142857  4.574325  3.360123   3.844546
 [7,] 0.8571429  6.009710  3.491940   4.496441
 [8,] 1.0000000  8.494436  3.829957   5.690918
 [9,] 1.0591734  8.215537  4.051355   5.882366
[10,] 1.1183467  8.102125  4.330779   6.132363
[11,] 1.1775201  8.221320  4.675819   6.467564
[12,] 1.2366935  8.581984  5.094483   6.904944
[13,] 1.2958668  9.235444  5.595187   7.405096
[14,] 1.3550402 10.212916  6.186762   7.945339
[15,] 1.4142136 11.529760  6.878451   8.190336
[16,] 1.4596189 10.795938  7.483110   8.426118
[17,] 1.5050242 10.403171  8.156919   8.781135
[18,] 1.5504295 10.379343  8.904549   9.319788
[19,] 1.5958348 10.578669  9.730813   9.979781
[20,] 1.6412402 10.986580 10.640667  10.736882
[21,] 1.6866455 11.724013 11.639211  11.663794
[22,] 1.7320508 12.731034 12.731034  12.731034

$design.2
         Radius     max.V     min.V  average.V
 [1,] 0.0000000  2.546392  2.546392   2.546392
 [2,] 0.1428571  2.548088  2.545781   2.546701
 [3,] 0.2857143  2.587916  2.551004   2.565731
 [4,] 0.4285714  2.770100  2.583235   2.657787
 [5,] 0.5714286  3.268345  2.677757   2.913381
 [6,] 0.7142857  4.325838  2.883973   3.459226
 [7,] 0.8571429  6.255248  3.265396   4.458241
 [8,] 1.0000000  9.438725  3.899656   6.109548
 [9,] 1.0591734  9.202533  4.257567   6.431892
[10,] 1.1183467  9.160134  4.681660   6.821041
[11,] 1.1775201  9.389863  5.179580   7.307278
[12,] 1.2366935  9.900796  5.759389   7.909311
[13,] 1.2958668 10.752367  6.429561   8.578828
[14,] 1.3550402 11.980046  7.198988   9.287298
[15,] 1.4142136 13.600406  8.076976   9.634840
[16,] 1.4596189 12.764341  8.830357   9.950180
[17,] 1.5050242 12.325356  9.657933  10.399189
[18,] 1.5504295 12.315723 10.564405  11.057501
[19,] 1.5958348 12.561449 11.554621  11.850270
[20,] 1.6412402 13.044341 12.633570  12.747825
[21,] 1.6866455 13.907090 13.806387  13.835580
[22,] 1.7320508 15.077577 15.077577  15.077577

VdgRsm documentation built on May 2, 2019, 3:48 p.m.