spv: Spherical Prediction Variance

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

Description

Create variance dispesion graphs (VDGs) for response surface designs in spherical regions.

Usage

spv(design.matrix, design.matrix.2 = NULL, design.matrix.3 = NULL, 
    des.names = c("Design 1","Design 2","Design 3"),  
    scale = TRUE, add.pts = TRUE, label = "ON")

Arguments

design.matrix, design.matrix.2, design.matrix.3	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 7.
des.names	A vector of descriptive names for designs in character strings.
scale	Design points are scaled by a factor equal to the square root of the number of factors divided by the maximum of radii across the set of design points. This factor makes two or more designs comparable by scaling the maximum design point radius to be the square root of the number of factors.
add.pts	Generate scaled prediction variances of random design points in the VDG. By default add.pts = TRUE.
label	The default is "ON" meaning that all legends will be appeared, and if it is "OFF", legends will be removed.

Value

spv is called to generate the Variance Dispersion Graph(s) and a table of the minimum, maximum, and average of scaled prediction variances.

Examples

CCD1<- gen.CCD(n.vars = 3, n.center = 2, alpha = 1)
CCD2<- gen.CCD(n.vars = 3, n.center = 2, alpha = sqrt(3))
spv(CCD1, CCD2, des.names = c("CCD 1","CCD 2"))

Example output

$design.1
         Radius     max.V     min.V  average.V
 [1,] 0.0000000  3.586207  3.586207   3.586207
 [2,] 0.1237179  3.569893  3.568801   3.569236
 [3,] 0.2474358  3.537993  3.520528   3.527488
 [4,] 0.3711537  3.541630  3.453215   3.488452
 [5,] 0.4948717  3.666010  3.386575   3.497943
 [6,] 0.6185896  4.030423  3.348208   3.620103
 [7,] 0.7423075  4.788240  3.373600   3.937399
 [8,] 0.8660254  6.126915  3.506119   4.550627
 [9,] 0.9897433  8.267984  3.797022   5.578907
[10,] 1.1134612 11.467067  4.305450   7.159685
[11,] 1.2371791 16.013865  5.098429   9.448734
[12,] 1.3608971 22.232161  6.250872  12.620153
[13,] 1.4846150 30.479823  7.845576  16.866369
[14,] 1.6083329 41.148800  9.973224  22.398131
[15,] 1.7320508 54.665124 12.732386  29.444519

$design.2
         Radius     max.V    min.V  average.V
 [1,] 0.0000000  8.000000 8.000000   8.000000
 [2,] 0.1237179  7.936254 7.936237   7.936247
 [3,] 0.2474358  7.749747 7.749470   7.749637
 [4,] 0.3711537  7.454669 7.453266   7.454110
 [5,] 0.4948717  7.074671 7.070236   7.072903
 [6,] 0.6185896  6.642864 6.632035   6.638548
 [7,] 0.7423075  6.201820 6.179365   6.192871
 [8,] 0.8660254  5.803570 5.761970   5.786991
 [9,] 0.9897433  5.509607 5.438639   5.481323
[10,] 1.1134612  5.390883 5.277206   5.345577
[11,] 1.2371791  5.527810 5.354549   5.458758
[12,] 1.3608971  6.010263 5.756592   5.909163
[13,] 1.4846150  6.937574 6.578300   6.794387
[14,] 1.6083329  8.418537 7.923687   8.221317
[15,] 1.7320508 10.571407 9.905808  10.306135

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