hyperarcs.vdg: Scaled prediction variance in nested cubes

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

Description

Create a graph of scaled prediction variances for points in nested cubes (hyperarcs)

Usage

hyperarcs.vdg(design.matrix, design.matrix.2 = NULL, design.matrix.3 = NULL, 
           des.names = c("Design 1","Design 2","Design 3"))

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 minimum and maximum number of factors are 3 and 6, respectively.
des.names	A vector of descriptive names for designs in character strings.

Value

hyperarcs.vdg is called to generate a plot of scaled prediction variances on hyperarcs.

Examples

CCD1<- gen.CCD(n.vars = 3, n.center = 2, alpha = 1)
CCD2<- gen.CCD(n.vars = 3, n.center = 5, alpha = 1)
hyperarcs.vdg(CCD1, CCD2)

Example output

$design.1
      Hypercube Radius     max.V    min.V  average.V
 [1,]       0.00000000  3.586207 3.586207   3.586207
 [2,]       0.07142857  3.580435 3.569478   3.576465
 [3,]       0.14285714  3.565010 3.522950   3.549459
 [4,]       0.21428571  3.545615 3.457595   3.511818
 [5,]       0.28571429  3.531720 3.391699   3.474606
 [6,]       0.35714286  3.536581 3.350495   3.453303
 [7,]       0.42857143  3.577244 3.359007   3.467814
 [8,]       0.50000000  3.674540 3.419262   3.542470
 [9,]       0.57142857  3.921439 3.551247   3.706025
[10,]       0.64285714  4.376912 3.782270   3.991654
[11,]       0.71428571  5.067986 4.142223   4.436960
[12,]       0.78571429  6.055334 4.666386   5.083953
[13,]       0.85714286  7.443513 5.390398   5.979100
[14,]       0.92857143  9.398427 6.356985   7.173264
[15,]       1.00000000 11.936830 7.610453   8.721739

$design.2
      Hypercube Radius     max.V    min.V  average.V
 [1,]       0.00000000  2.546392 2.546392   2.546392
 [2,]       0.07142857  2.546272 2.545835   2.546088
 [3,]       0.14285714  2.548653 2.546926   2.547565
 [4,]       0.21428571  2.564587 2.555347   2.557992
 [5,]       0.28571429  2.611359 2.580914   2.589316
 [6,]       0.35714286  2.716789 2.637350   2.658266
 [7,]       0.42857143  2.913411 2.742439   2.786340
 [8,]       0.50000000  3.241626 2.917712   2.999832
 [9,]       0.57142857  3.749356 3.188678   3.329803
[10,]       0.64285714  4.491896 3.584907   3.812094
[11,]       0.71428571  5.531911 4.139826   4.487328
[12,]       0.78571429  6.930780 4.890792   5.400892
[13,]       0.85714286  8.779611 5.879096   6.602987
[14,]       0.92857143 11.157122 7.149960   8.148564
[15,]       1.00000000 14.155290 8.752540  10.097360

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