R Under development (unstable) (2023-08-20 r84995 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > require(DoE.wrapper) Loading required package: DoE.wrapper Loading required package: FrF2 Loading required package: DoE.base Loading required package: grid Loading required package: conf.design Attaching package: 'DoE.base' The following objects are masked from 'package:stats': aov, lm The following object is masked from 'package:graphics': plot.design The following object is masked from 'package:base': lengths Loading required package: rsm > options(warn=-1) > > ccd.augment(FrF2(8,4,randomize=FALSE),randomize=FALSE) Block.ccd A B C D C1.1 1 -1 -1 -1 -1 C1.2 1 1 -1 -1 1 C1.3 1 -1 1 -1 1 C1.4 1 1 1 -1 -1 C1.5 1 -1 -1 1 1 C1.6 1 1 -1 1 -1 C1.7 1 -1 1 1 -1 C1.8 1 1 1 1 1 C1.9 1 0 0 0 0 C1.10 1 0 0 0 0 C1.11 1 0 0 0 0 C1.12 1 0 0 0 0 S2.1 2 -2 0 0 0 S2.2 2 2 0 0 0 S2.3 2 0 -2 0 0 S2.4 2 0 2 0 0 S2.5 2 0 0 -2 0 S2.6 2 0 0 2 0 S2.7 2 0 0 0 -2 S2.8 2 0 0 0 2 S2.9 2 0 0 0 0 S2.10 2 0 0 0 0 S2.11 2 0 0 0 0 S2.12 2 0 0 0 0 class=design, type= ccd > ccd.augment(FrF2(8,3,randomize=FALSE),randomize=FALSE) creating full factorial with 8 runs ... Block.ccd A B C C1.1 1 -1.000000 -1.000000 -1.000000 C1.2 1 1.000000 -1.000000 -1.000000 C1.3 1 -1.000000 1.000000 -1.000000 C1.4 1 1.000000 1.000000 -1.000000 C1.5 1 -1.000000 -1.000000 1.000000 C1.6 1 1.000000 -1.000000 1.000000 C1.7 1 -1.000000 1.000000 1.000000 C1.8 1 1.000000 1.000000 1.000000 C1.9 1 0.000000 0.000000 0.000000 C1.10 1 0.000000 0.000000 0.000000 C1.11 1 0.000000 0.000000 0.000000 C1.12 1 0.000000 0.000000 0.000000 S2.1 2 -1.825742 0.000000 0.000000 S2.2 2 1.825742 0.000000 0.000000 S2.3 2 0.000000 -1.825742 0.000000 S2.4 2 0.000000 1.825742 0.000000 S2.5 2 0.000000 0.000000 -1.825742 S2.6 2 0.000000 0.000000 1.825742 S2.7 2 0.000000 0.000000 0.000000 S2.8 2 0.000000 0.000000 0.000000 S2.9 2 0.000000 0.000000 0.000000 S2.10 2 0.000000 0.000000 0.000000 class=design, type= ccd > > > ## resolution V design as basis > plan <- FrF2(16,5,ncenter=4,randomize=FALSE) > set.seed(2424) > y <- round(rexp(20), 4) > r.plan <- add.response(plan,y) > ccd.augment(plan,4,randomize=FALSE) Block.ccd A B C D E C1.1 1 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 C1.2 1 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 C1.3 1 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 C1.4 1 1.000000 1.000000 -1.000000 -1.000000 1.000000 C1.5 1 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 C1.6 1 1.000000 -1.000000 1.000000 -1.000000 1.000000 C1.7 1 -1.000000 1.000000 1.000000 -1.000000 1.000000 C1.8 1 1.000000 1.000000 1.000000 -1.000000 -1.000000 C1.9 1 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 C1.10 1 1.000000 -1.000000 -1.000000 1.000000 1.000000 C1.11 1 -1.000000 1.000000 -1.000000 1.000000 1.000000 C1.12 1 1.000000 1.000000 -1.000000 1.000000 -1.000000 C1.13 1 -1.000000 -1.000000 1.000000 1.000000 1.000000 C1.14 1 1.000000 -1.000000 1.000000 1.000000 -1.000000 C1.15 1 -1.000000 1.000000 1.000000 1.000000 -1.000000 C1.16 1 1.000000 1.000000 1.000000 1.000000 1.000000 C1.17 1 0.000000 0.000000 0.000000 0.000000 0.000000 C1.18 1 0.000000 0.000000 0.000000 0.000000 0.000000 C1.19 1 0.000000 0.000000 0.000000 0.000000 0.000000 C1.20 1 0.000000 0.000000 0.000000 0.000000 0.000000 S2.1 2 -2.366432 0.000000 0.000000 0.000000 0.000000 S2.2 2 2.366432 0.000000 0.000000 0.000000 0.000000 S2.3 2 0.000000 -2.366432 0.000000 0.000000 0.000000 S2.4 2 0.000000 2.366432 0.000000 0.000000 0.000000 S2.5 2 0.000000 0.000000 -2.366432 0.000000 0.000000 S2.6 2 0.000000 0.000000 2.366432 0.000000 0.000000 S2.7 2 0.000000 0.000000 0.000000 -2.366432 0.000000 S2.8 2 0.000000 0.000000 0.000000 2.366432 0.000000 S2.9 2 0.000000 0.000000 0.000000 0.000000 -2.366432 S2.10 2 0.000000 0.000000 0.000000 0.000000 2.366432 S2.11 2 0.000000 0.000000 0.000000 0.000000 0.000000 S2.12 2 0.000000 0.000000 0.000000 0.000000 0.000000 S2.13 2 0.000000 0.000000 0.000000 0.000000 0.000000 S2.14 2 0.000000 0.000000 0.000000 0.000000 0.000000 class=design, type= ccd > > ## augmenting design with response > ccd.augment(r.plan,4,randomize=FALSE) Block.ccd A B C D E y C1.1 1 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 0.8382 C1.2 1 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 1.0072 C1.3 1 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 0.7076 C1.4 1 1.000000 1.000000 -1.000000 -1.000000 1.000000 0.0774 C1.5 1 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 0.9655 C1.6 1 1.000000 -1.000000 1.000000 -1.000000 1.000000 0.0927 C1.7 1 -1.000000 1.000000 1.000000 -1.000000 1.000000 1.3219 C1.8 1 1.000000 1.000000 1.000000 -1.000000 -1.000000 1.5610 C1.9 1 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 0.7780 C1.10 1 1.000000 -1.000000 -1.000000 1.000000 1.000000 0.3445 C1.11 1 -1.000000 1.000000 -1.000000 1.000000 1.000000 1.9190 C1.12 1 1.000000 1.000000 -1.000000 1.000000 -1.000000 1.9129 C1.13 1 -1.000000 -1.000000 1.000000 1.000000 1.000000 0.8488 C1.14 1 1.000000 -1.000000 1.000000 1.000000 -1.000000 0.6966 C1.15 1 -1.000000 1.000000 1.000000 1.000000 -1.000000 0.6354 C1.16 1 1.000000 1.000000 1.000000 1.000000 1.000000 3.4653 C1.17 1 0.000000 0.000000 0.000000 0.000000 0.000000 3.5178 C1.18 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.0772 C1.19 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.6282 C1.20 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.2083 S2.1 2 -2.366432 0.000000 0.000000 0.000000 0.000000 NA S2.2 2 2.366432 0.000000 0.000000 0.000000 0.000000 NA S2.3 2 0.000000 -2.366432 0.000000 0.000000 0.000000 NA S2.4 2 0.000000 2.366432 0.000000 0.000000 0.000000 NA S2.5 2 0.000000 0.000000 -2.366432 0.000000 0.000000 NA S2.6 2 0.000000 0.000000 2.366432 0.000000 0.000000 NA S2.7 2 0.000000 0.000000 0.000000 -2.366432 0.000000 NA S2.8 2 0.000000 0.000000 0.000000 2.366432 0.000000 NA S2.9 2 0.000000 0.000000 0.000000 0.000000 -2.366432 NA S2.10 2 0.000000 0.000000 0.000000 0.000000 2.366432 NA S2.11 2 0.000000 0.000000 0.000000 0.000000 0.000000 NA S2.12 2 0.000000 0.000000 0.000000 0.000000 0.000000 NA S2.13 2 0.000000 0.000000 0.000000 0.000000 0.000000 NA S2.14 2 0.000000 0.000000 0.000000 0.000000 0.000000 NA class=design, type= ccd > > ## estimable design > ## basic order > basic <- FrF2(8,4, estimable=c("CD"),res3=TRUE,randomize=FALSE) > ccd.augment(basic,4,randomize=FALSE) Block.ccd A B C D C1.1 1 -1 -1 -1 1 C1.2 1 1 -1 -1 -1 C1.3 1 -1 1 -1 -1 C1.4 1 1 1 -1 1 C1.5 1 -1 -1 1 1 C1.6 1 1 -1 1 -1 C1.7 1 -1 1 1 -1 C1.8 1 1 1 1 1 C1.9 1 0 0 0 0 C1.10 1 0 0 0 0 C1.11 1 0 0 0 0 C1.12 1 0 0 0 0 S2.1 2 -2 0 0 0 S2.2 2 2 0 0 0 S2.3 2 0 -2 0 0 S2.4 2 0 2 0 0 S2.5 2 0 0 -2 0 S2.6 2 0 0 2 0 S2.7 2 0 0 0 -2 S2.8 2 0 0 0 2 S2.9 2 0 0 0 0 S2.10 2 0 0 0 0 S2.11 2 0 0 0 0 S2.12 2 0 0 0 0 class=design, type= ccd > > ndig <- 4 > ## reshuffled > ## only one swap > reshuffled <- FrF2(8,4, estimable=c("AD"),res3=TRUE,randomize=FALSE) > ccd.augment(reshuffled,6,randomize=FALSE) Block.ccd A B C D C1.1 1 -1 -1 1 -1 C1.2 1 1 -1 -1 -1 C1.3 1 -1 1 -1 -1 C1.4 1 1 1 1 -1 C1.5 1 -1 -1 1 1 C1.6 1 1 -1 -1 1 C1.7 1 -1 1 -1 1 C1.8 1 1 1 1 1 C1.9 1 0 0 0 0 C1.10 1 0 0 0 0 C1.11 1 0 0 0 0 C1.12 1 0 0 0 0 C1.13 1 0 0 0 0 C1.14 1 0 0 0 0 S2.1 2 -2 0 0 0 S2.2 2 2 0 0 0 S2.3 2 0 -2 0 0 S2.4 2 0 2 0 0 S2.5 2 0 0 0 -2 S2.6 2 0 0 0 2 S2.7 2 0 0 -2 0 S2.8 2 0 0 2 0 S2.9 2 0 0 0 0 S2.10 2 0 0 0 0 S2.11 2 0 0 0 0 S2.12 2 0 0 0 0 S2.13 2 0 0 0 0 S2.14 2 0 0 0 0 class=design, type= ccd > reshuffled <- FrF2(8,4, estimable=c("AD"),res3=TRUE, default.levels=c(0,100), + factor.names=Letters[22:25],randomize=FALSE) > reshuffled <- FrF2(8,4, estimable=c("AD"),res3=TRUE, default.levels=c(0,100), + factor.names=list(T=c(30,50),U=c(24,26),V=c(100,400),W=c(30,75)),randomize=FALSE) > ccd.augment(reshuffled,6,randomize=FALSE) Block.ccd T U V W C1.1 1 30 24 400 30.0 C1.2 1 50 24 100 30.0 C1.3 1 30 26 100 30.0 C1.4 1 50 26 400 30.0 C1.5 1 30 24 400 75.0 C1.6 1 50 24 100 75.0 C1.7 1 30 26 100 75.0 C1.8 1 50 26 400 75.0 C1.9 1 40 25 250 52.5 C1.10 1 40 25 250 52.5 C1.11 1 40 25 250 52.5 C1.12 1 40 25 250 52.5 C1.13 1 40 25 250 52.5 C1.14 1 40 25 250 52.5 S2.1 2 20 25 250 52.5 S2.2 2 60 25 250 52.5 S2.3 2 40 23 250 52.5 S2.4 2 40 27 250 52.5 S2.5 2 40 25 250 7.5 S2.6 2 40 25 250 97.5 S2.7 2 40 25 -50 52.5 S2.8 2 40 25 550 52.5 S2.9 2 40 25 250 52.5 S2.10 2 40 25 250 52.5 S2.11 2 40 25 250 52.5 S2.12 2 40 25 250 52.5 S2.13 2 40 25 250 52.5 S2.14 2 40 25 250 52.5 class=design, type= ccd > > round(desnum(ccd.augment(reshuffled,6,randomize=FALSE)), 6) Block.ccd T U V W C1.1 0 -1 -1 1 -1 C1.2 0 1 -1 -1 -1 C1.3 0 -1 1 -1 -1 C1.4 0 1 1 1 -1 C1.5 0 -1 -1 1 1 C1.6 0 1 -1 -1 1 C1.7 0 -1 1 -1 1 C1.8 0 1 1 1 1 C1.9 0 0 0 0 0 C1.10 0 0 0 0 0 C1.11 0 0 0 0 0 C1.12 0 0 0 0 0 C1.13 0 0 0 0 0 C1.14 0 0 0 0 0 S2.1 1 -2 0 0 0 S2.2 1 2 0 0 0 S2.3 1 0 -2 0 0 S2.4 1 0 2 0 0 S2.5 1 0 0 0 -2 S2.6 1 0 0 0 2 S2.7 1 0 0 -2 0 S2.8 1 0 0 2 0 S2.9 1 0 0 0 0 S2.10 1 0 0 0 0 S2.11 1 0 0 0 0 S2.12 1 0 0 0 0 S2.13 1 0 0 0 0 S2.14 1 0 0 0 0 > > ## more reshuffling > reshuffled.big <- FrF2(32,7, estimable=c("AC","BC","AB"),randomize=FALSE, + factor.names=Letters[19:25],default.levels=c(10,30)) > ccd.reshuffled.big <- ccd.augment(reshuffled.big,6,randomize=FALSE) > reshuffled.big <- FrF2(32,7, estimable=c("AC","BC","AB"),randomize=FALSE, + factor.names=list(T=c(30,50),U=c(24,26),V=c(100,400),W=c(30,75), X=c(0.1,0.7),Y="",Z=""), + default.levels=c(10,30),repl=2) > ccd.reshuffled.big <- ccd.augment(reshuffled.big,6,randomize=FALSE) > ccd.reshuffled.big run.no run.no.std.rp Block.ccd T U V W C0.1 1 C0.1 0 30.00000 24.00000 100.0000 30.00000 C0.2 2 C0.2 0 50.00000 24.00000 100.0000 30.00000 C0.3 3 C0.3 0 30.00000 24.00000 100.0000 75.00000 C0.4 4 C0.4 0 50.00000 24.00000 100.0000 75.00000 C0.5 5 C0.5 0 30.00000 24.00000 100.0000 30.00000 C0.6 6 C0.6 0 50.00000 24.00000 100.0000 30.00000 C0.7 7 C0.7 0 30.00000 24.00000 100.0000 75.00000 C0.8 8 C0.8 0 50.00000 24.00000 100.0000 75.00000 C0.9 9 C0.9 0 30.00000 26.00000 100.0000 30.00000 C0.10 10 C0.10 0 50.00000 26.00000 100.0000 30.00000 C0.11 11 C0.11 0 30.00000 26.00000 100.0000 75.00000 C0.12 12 C0.12 0 50.00000 26.00000 100.0000 75.00000 C0.13 13 C0.13 0 30.00000 26.00000 100.0000 30.00000 C0.14 14 C0.14 0 50.00000 26.00000 100.0000 30.00000 C0.15 15 C0.15 0 30.00000 26.00000 100.0000 75.00000 C0.16 16 C0.16 0 50.00000 26.00000 100.0000 75.00000 C0.17 17 C0.17 0 30.00000 24.00000 400.0000 30.00000 C0.18 18 C0.18 0 50.00000 24.00000 400.0000 30.00000 C0.19 19 C0.19 0 30.00000 24.00000 400.0000 75.00000 C0.20 20 C0.20 0 50.00000 24.00000 400.0000 75.00000 C0.21 21 C0.21 0 30.00000 24.00000 400.0000 30.00000 C0.22 22 C0.22 0 50.00000 24.00000 400.0000 30.00000 C0.23 23 C0.23 0 30.00000 24.00000 400.0000 75.00000 C0.24 24 C0.24 0 50.00000 24.00000 400.0000 75.00000 C0.25 25 C0.25 0 30.00000 26.00000 400.0000 30.00000 C0.26 26 C0.26 0 50.00000 26.00000 400.0000 30.00000 C0.27 27 C0.27 0 30.00000 26.00000 400.0000 75.00000 C0.28 28 C0.28 0 50.00000 26.00000 400.0000 75.00000 C0.29 29 C0.29 0 30.00000 26.00000 400.0000 30.00000 C0.30 30 C0.30 0 50.00000 26.00000 400.0000 30.00000 C0.31 31 C0.31 0 30.00000 26.00000 400.0000 75.00000 C0.32 32 C0.32 0 50.00000 26.00000 400.0000 75.00000 C0.33 33 C0.33 0 40.00000 25.00000 250.0000 52.50000 C0.34 34 C0.34 0 40.00000 25.00000 250.0000 52.50000 C0.35 35 C0.35 0 40.00000 25.00000 250.0000 52.50000 C0.36 36 C0.36 0 40.00000 25.00000 250.0000 52.50000 C0.37 37 C0.37 0 40.00000 25.00000 250.0000 52.50000 C0.38 38 C0.38 0 40.00000 25.00000 250.0000 52.50000 C1.1 39 C1.1 1 30.00000 24.00000 100.0000 30.00000 C1.2 40 C1.2 1 50.00000 24.00000 100.0000 30.00000 C1.3 41 C1.3 1 30.00000 24.00000 100.0000 75.00000 C1.4 42 C1.4 1 50.00000 24.00000 100.0000 75.00000 C1.5 43 C1.5 1 30.00000 24.00000 100.0000 30.00000 C1.6 44 C1.6 1 50.00000 24.00000 100.0000 30.00000 C1.7 45 C1.7 1 30.00000 24.00000 100.0000 75.00000 C1.8 46 C1.8 1 50.00000 24.00000 100.0000 75.00000 C1.9 47 C1.9 1 30.00000 26.00000 100.0000 30.00000 C1.10 48 C1.10 1 50.00000 26.00000 100.0000 30.00000 C1.11 49 C1.11 1 30.00000 26.00000 100.0000 75.00000 C1.12 50 C1.12 1 50.00000 26.00000 100.0000 75.00000 C1.13 51 C1.13 1 30.00000 26.00000 100.0000 30.00000 C1.14 52 C1.14 1 50.00000 26.00000 100.0000 30.00000 C1.15 53 C1.15 1 30.00000 26.00000 100.0000 75.00000 C1.16 54 C1.16 1 50.00000 26.00000 100.0000 75.00000 C1.17 55 C1.17 1 30.00000 24.00000 400.0000 30.00000 C1.18 56 C1.18 1 50.00000 24.00000 400.0000 30.00000 C1.19 57 C1.19 1 30.00000 24.00000 400.0000 75.00000 C1.20 58 C1.20 1 50.00000 24.00000 400.0000 75.00000 C1.21 59 C1.21 1 30.00000 24.00000 400.0000 30.00000 C1.22 60 C1.22 1 50.00000 24.00000 400.0000 30.00000 C1.23 61 C1.23 1 30.00000 24.00000 400.0000 75.00000 C1.24 62 C1.24 1 50.00000 24.00000 400.0000 75.00000 C1.25 63 C1.25 1 30.00000 26.00000 400.0000 30.00000 C1.26 64 C1.26 1 50.00000 26.00000 400.0000 30.00000 C1.27 65 C1.27 1 30.00000 26.00000 400.0000 75.00000 C1.28 66 C1.28 1 50.00000 26.00000 400.0000 75.00000 C1.29 67 C1.29 1 30.00000 26.00000 400.0000 30.00000 C1.30 68 C1.30 1 50.00000 26.00000 400.0000 30.00000 C1.31 69 C1.31 1 30.00000 26.00000 400.0000 75.00000 C1.32 70 C1.32 1 50.00000 26.00000 400.0000 75.00000 C1.33 71 C1.33 1 40.00000 25.00000 250.0000 52.50000 C1.34 72 C1.34 1 40.00000 25.00000 250.0000 52.50000 C1.35 73 C1.35 1 40.00000 25.00000 250.0000 52.50000 C1.36 74 C1.36 1 40.00000 25.00000 250.0000 52.50000 C1.37 75 C1.37 1 40.00000 25.00000 250.0000 52.50000 C1.38 76 C1.38 1 40.00000 25.00000 250.0000 52.50000 S2.1 77 S2.1 2 10.98095 25.00000 250.0000 52.50000 S2.2 78 S2.2 2 69.01905 25.00000 250.0000 52.50000 S2.3 79 S2.3 2 40.00000 25.00000 250.0000 -12.79286 S2.4 80 S2.4 2 40.00000 25.00000 250.0000 117.79286 S2.5 81 S2.5 2 40.00000 25.00000 250.0000 52.50000 S2.6 82 S2.6 2 40.00000 25.00000 250.0000 52.50000 S2.7 83 S2.7 2 40.00000 22.09809 250.0000 52.50000 S2.8 84 S2.8 2 40.00000 27.90191 250.0000 52.50000 S2.9 85 S2.9 2 40.00000 25.00000 -185.2858 52.50000 S2.10 86 S2.10 2 40.00000 25.00000 685.2858 52.50000 S2.11 87 S2.11 2 40.00000 25.00000 250.0000 52.50000 S2.12 88 S2.12 2 40.00000 25.00000 250.0000 52.50000 S2.13 89 S2.13 2 40.00000 25.00000 250.0000 52.50000 S2.14 90 S2.14 2 40.00000 25.00000 250.0000 52.50000 S2.15 91 S2.15 2 40.00000 25.00000 250.0000 52.50000 S2.16 92 S2.16 2 40.00000 25.00000 250.0000 52.50000 S2.17 93 S2.17 2 40.00000 25.00000 250.0000 52.50000 S2.18 94 S2.18 2 40.00000 25.00000 250.0000 52.50000 S2.19 95 S2.19 2 40.00000 25.00000 250.0000 52.50000 S2.20 96 S2.20 2 40.00000 25.00000 250.0000 52.50000 S3.1 97 S3.1 3 10.98095 25.00000 250.0000 52.50000 S3.2 98 S3.2 3 69.01905 25.00000 250.0000 52.50000 S3.3 99 S3.3 3 40.00000 25.00000 250.0000 -12.79286 S3.4 100 S3.4 3 40.00000 25.00000 250.0000 117.79286 S3.5 101 S3.5 3 40.00000 25.00000 250.0000 52.50000 S3.6 102 S3.6 3 40.00000 25.00000 250.0000 52.50000 S3.7 103 S3.7 3 40.00000 22.09809 250.0000 52.50000 S3.8 104 S3.8 3 40.00000 27.90191 250.0000 52.50000 S3.9 105 S3.9 3 40.00000 25.00000 -185.2858 52.50000 S3.10 106 S3.10 3 40.00000 25.00000 685.2858 52.50000 S3.11 107 S3.11 3 40.00000 25.00000 250.0000 52.50000 S3.12 108 S3.12 3 40.00000 25.00000 250.0000 52.50000 S3.13 109 S3.13 3 40.00000 25.00000 250.0000 52.50000 S3.14 110 S3.14 3 40.00000 25.00000 250.0000 52.50000 S3.15 111 S3.15 3 40.00000 25.00000 250.0000 52.50000 S3.16 112 S3.16 3 40.00000 25.00000 250.0000 52.50000 S3.17 113 S3.17 3 40.00000 25.00000 250.0000 52.50000 S3.18 114 S3.18 3 40.00000 25.00000 250.0000 52.50000 S3.19 115 S3.19 3 40.00000 25.00000 250.0000 52.50000 S3.20 116 S3.20 3 40.00000 25.00000 250.0000 52.50000 X Y Z C0.1 0.1000000 10.00000 30.00000 C0.2 0.1000000 30.00000 10.00000 C0.3 0.1000000 30.00000 10.00000 C0.4 0.1000000 10.00000 30.00000 C0.5 0.7000000 30.00000 30.00000 C0.6 0.7000000 10.00000 10.00000 C0.7 0.7000000 10.00000 10.00000 C0.8 0.7000000 30.00000 30.00000 C0.9 0.1000000 10.00000 10.00000 C0.10 0.1000000 30.00000 30.00000 C0.11 0.1000000 30.00000 30.00000 C0.12 0.1000000 10.00000 10.00000 C0.13 0.7000000 30.00000 10.00000 C0.14 0.7000000 10.00000 30.00000 C0.15 0.7000000 10.00000 30.00000 C0.16 0.7000000 30.00000 10.00000 C0.17 0.1000000 10.00000 10.00000 C0.18 0.1000000 30.00000 30.00000 C0.19 0.1000000 30.00000 30.00000 C0.20 0.1000000 10.00000 10.00000 C0.21 0.7000000 30.00000 10.00000 C0.22 0.7000000 10.00000 30.00000 C0.23 0.7000000 10.00000 30.00000 C0.24 0.7000000 30.00000 10.00000 C0.25 0.1000000 10.00000 30.00000 C0.26 0.1000000 30.00000 10.00000 C0.27 0.1000000 30.00000 10.00000 C0.28 0.1000000 10.00000 30.00000 C0.29 0.7000000 30.00000 30.00000 C0.30 0.7000000 10.00000 10.00000 C0.31 0.7000000 10.00000 10.00000 C0.32 0.7000000 30.00000 30.00000 C0.33 0.4000000 20.00000 20.00000 C0.34 0.4000000 20.00000 20.00000 C0.35 0.4000000 20.00000 20.00000 C0.36 0.4000000 20.00000 20.00000 C0.37 0.4000000 20.00000 20.00000 C0.38 0.4000000 20.00000 20.00000 C1.1 0.1000000 10.00000 30.00000 C1.2 0.1000000 30.00000 10.00000 C1.3 0.1000000 30.00000 10.00000 C1.4 0.1000000 10.00000 30.00000 C1.5 0.7000000 30.00000 30.00000 C1.6 0.7000000 10.00000 10.00000 C1.7 0.7000000 10.00000 10.00000 C1.8 0.7000000 30.00000 30.00000 C1.9 0.1000000 10.00000 10.00000 C1.10 0.1000000 30.00000 30.00000 C1.11 0.1000000 30.00000 30.00000 C1.12 0.1000000 10.00000 10.00000 C1.13 0.7000000 30.00000 10.00000 C1.14 0.7000000 10.00000 30.00000 C1.15 0.7000000 10.00000 30.00000 C1.16 0.7000000 30.00000 10.00000 C1.17 0.1000000 10.00000 10.00000 C1.18 0.1000000 30.00000 30.00000 C1.19 0.1000000 30.00000 30.00000 C1.20 0.1000000 10.00000 10.00000 C1.21 0.7000000 30.00000 10.00000 C1.22 0.7000000 10.00000 30.00000 C1.23 0.7000000 10.00000 30.00000 C1.24 0.7000000 30.00000 10.00000 C1.25 0.1000000 10.00000 30.00000 C1.26 0.1000000 30.00000 10.00000 C1.27 0.1000000 30.00000 10.00000 C1.28 0.1000000 10.00000 30.00000 C1.29 0.7000000 30.00000 30.00000 C1.30 0.7000000 10.00000 10.00000 C1.31 0.7000000 10.00000 10.00000 C1.32 0.7000000 30.00000 30.00000 C1.33 0.4000000 20.00000 20.00000 C1.34 0.4000000 20.00000 20.00000 C1.35 0.4000000 20.00000 20.00000 C1.36 0.4000000 20.00000 20.00000 C1.37 0.4000000 20.00000 20.00000 C1.38 0.4000000 20.00000 20.00000 S2.1 0.4000000 20.00000 20.00000 S2.2 0.4000000 20.00000 20.00000 S2.3 0.4000000 20.00000 20.00000 S2.4 0.4000000 20.00000 20.00000 S2.5 -0.4705715 20.00000 20.00000 S2.6 1.2705715 20.00000 20.00000 S2.7 0.4000000 20.00000 20.00000 S2.8 0.4000000 20.00000 20.00000 S2.9 0.4000000 20.00000 20.00000 S2.10 0.4000000 20.00000 20.00000 S2.11 0.4000000 -9.01905 20.00000 S2.12 0.4000000 49.01905 20.00000 S2.13 0.4000000 20.00000 -9.01905 S2.14 0.4000000 20.00000 49.01905 S2.15 0.4000000 20.00000 20.00000 S2.16 0.4000000 20.00000 20.00000 S2.17 0.4000000 20.00000 20.00000 S2.18 0.4000000 20.00000 20.00000 S2.19 0.4000000 20.00000 20.00000 S2.20 0.4000000 20.00000 20.00000 S3.1 0.4000000 20.00000 20.00000 S3.2 0.4000000 20.00000 20.00000 S3.3 0.4000000 20.00000 20.00000 S3.4 0.4000000 20.00000 20.00000 S3.5 -0.4705715 20.00000 20.00000 S3.6 1.2705715 20.00000 20.00000 S3.7 0.4000000 20.00000 20.00000 S3.8 0.4000000 20.00000 20.00000 S3.9 0.4000000 20.00000 20.00000 S3.10 0.4000000 20.00000 20.00000 S3.11 0.4000000 -9.01905 20.00000 S3.12 0.4000000 49.01905 20.00000 S3.13 0.4000000 20.00000 -9.01905 S3.14 0.4000000 20.00000 49.01905 S3.15 0.4000000 20.00000 20.00000 S3.16 0.4000000 20.00000 20.00000 S3.17 0.4000000 20.00000 20.00000 S3.18 0.4000000 20.00000 20.00000 S3.19 0.4000000 20.00000 20.00000 S3.20 0.4000000 20.00000 20.00000 class=design, type= ccd NOTE: columns run.no and run.no.std.rp are annotation, not part of the data frame > > round(desnum(ccd.reshuffled.big), 6) Block.ccd T U V W X Y Z x6 C0.1 0 0 0 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 C0.2 0 0 0 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 C0.3 0 0 0 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 1.000000 C0.4 0 0 0 1.000000 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 C0.5 0 0 0 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 1.000000 C0.6 0 0 0 1.000000 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 C0.7 0 0 0 -1.000000 -1.000000 -1.000000 1.000000 1.000000 -1.000000 C0.8 0 0 0 1.000000 -1.000000 -1.000000 1.000000 1.000000 1.000000 C0.9 0 0 0 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 C0.10 0 0 0 1.000000 1.000000 -1.000000 -1.000000 -1.000000 1.000000 C0.11 0 0 0 -1.000000 1.000000 -1.000000 1.000000 -1.000000 1.000000 C0.12 0 0 0 1.000000 1.000000 -1.000000 1.000000 -1.000000 -1.000000 C0.13 0 0 0 -1.000000 1.000000 -1.000000 -1.000000 1.000000 1.000000 C0.14 0 0 0 1.000000 1.000000 -1.000000 -1.000000 1.000000 -1.000000 C0.15 0 0 0 -1.000000 1.000000 -1.000000 1.000000 1.000000 -1.000000 C0.16 0 0 0 1.000000 1.000000 -1.000000 1.000000 1.000000 1.000000 C0.17 0 0 0 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 C0.18 0 0 0 1.000000 -1.000000 1.000000 -1.000000 -1.000000 1.000000 C0.19 0 0 0 -1.000000 -1.000000 1.000000 1.000000 -1.000000 1.000000 C0.20 0 0 0 1.000000 -1.000000 1.000000 1.000000 -1.000000 -1.000000 C0.21 0 0 0 -1.000000 -1.000000 1.000000 -1.000000 1.000000 1.000000 C0.22 0 0 0 1.000000 -1.000000 1.000000 -1.000000 1.000000 -1.000000 C0.23 0 0 0 -1.000000 -1.000000 1.000000 1.000000 1.000000 -1.000000 C0.24 0 0 0 1.000000 -1.000000 1.000000 1.000000 1.000000 1.000000 C0.25 0 0 0 -1.000000 1.000000 1.000000 -1.000000 -1.000000 -1.000000 C0.26 0 0 0 1.000000 1.000000 1.000000 -1.000000 -1.000000 1.000000 C0.27 0 0 0 -1.000000 1.000000 1.000000 1.000000 -1.000000 1.000000 C0.28 0 0 0 1.000000 1.000000 1.000000 1.000000 -1.000000 -1.000000 C0.29 0 0 0 -1.000000 1.000000 1.000000 -1.000000 1.000000 1.000000 C0.30 0 0 0 1.000000 1.000000 1.000000 -1.000000 1.000000 -1.000000 C0.31 0 0 0 -1.000000 1.000000 1.000000 1.000000 1.000000 -1.000000 C0.32 0 0 0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 C0.33 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C0.34 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C0.35 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C0.36 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C0.37 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C0.38 0 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.1 1 0 0 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 -1.000000 C1.2 1 0 0 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 C1.3 1 0 0 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 1.000000 C1.4 1 0 0 1.000000 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 C1.5 1 0 0 -1.000000 -1.000000 -1.000000 -1.000000 1.000000 1.000000 C1.6 1 0 0 1.000000 -1.000000 -1.000000 -1.000000 1.000000 -1.000000 C1.7 1 0 0 -1.000000 -1.000000 -1.000000 1.000000 1.000000 -1.000000 C1.8 1 0 0 1.000000 -1.000000 -1.000000 1.000000 1.000000 1.000000 C1.9 1 0 0 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 -1.000000 C1.10 1 0 0 1.000000 1.000000 -1.000000 -1.000000 -1.000000 1.000000 C1.11 1 0 0 -1.000000 1.000000 -1.000000 1.000000 -1.000000 1.000000 C1.12 1 0 0 1.000000 1.000000 -1.000000 1.000000 -1.000000 -1.000000 C1.13 1 0 0 -1.000000 1.000000 -1.000000 -1.000000 1.000000 1.000000 C1.14 1 0 0 1.000000 1.000000 -1.000000 -1.000000 1.000000 -1.000000 C1.15 1 0 0 -1.000000 1.000000 -1.000000 1.000000 1.000000 -1.000000 C1.16 1 0 0 1.000000 1.000000 -1.000000 1.000000 1.000000 1.000000 C1.17 1 0 0 -1.000000 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 C1.18 1 0 0 1.000000 -1.000000 1.000000 -1.000000 -1.000000 1.000000 C1.19 1 0 0 -1.000000 -1.000000 1.000000 1.000000 -1.000000 1.000000 C1.20 1 0 0 1.000000 -1.000000 1.000000 1.000000 -1.000000 -1.000000 C1.21 1 0 0 -1.000000 -1.000000 1.000000 -1.000000 1.000000 1.000000 C1.22 1 0 0 1.000000 -1.000000 1.000000 -1.000000 1.000000 -1.000000 C1.23 1 0 0 -1.000000 -1.000000 1.000000 1.000000 1.000000 -1.000000 C1.24 1 0 0 1.000000 -1.000000 1.000000 1.000000 1.000000 1.000000 C1.25 1 0 0 -1.000000 1.000000 1.000000 -1.000000 -1.000000 -1.000000 C1.26 1 0 0 1.000000 1.000000 1.000000 -1.000000 -1.000000 1.000000 C1.27 1 0 0 -1.000000 1.000000 1.000000 1.000000 -1.000000 1.000000 C1.28 1 0 0 1.000000 1.000000 1.000000 1.000000 -1.000000 -1.000000 C1.29 1 0 0 -1.000000 1.000000 1.000000 -1.000000 1.000000 1.000000 C1.30 1 0 0 1.000000 1.000000 1.000000 -1.000000 1.000000 -1.000000 C1.31 1 0 0 -1.000000 1.000000 1.000000 1.000000 1.000000 -1.000000 C1.32 1 0 0 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 C1.33 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.34 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.35 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.36 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.37 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 C1.38 1 0 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.1 0 1 0 -2.901905 0.000000 0.000000 0.000000 0.000000 0.000000 S2.2 0 1 0 2.901905 0.000000 0.000000 0.000000 0.000000 0.000000 S2.3 0 1 0 0.000000 0.000000 0.000000 -2.901905 0.000000 0.000000 S2.4 0 1 0 0.000000 0.000000 0.000000 2.901905 0.000000 0.000000 S2.5 0 1 0 0.000000 0.000000 0.000000 0.000000 -2.901905 0.000000 S2.6 0 1 0 0.000000 0.000000 0.000000 0.000000 2.901905 0.000000 S2.7 0 1 0 0.000000 -2.901905 0.000000 0.000000 0.000000 0.000000 S2.8 0 1 0 0.000000 2.901905 0.000000 0.000000 0.000000 0.000000 S2.9 0 1 0 0.000000 0.000000 -2.901905 0.000000 0.000000 0.000000 S2.10 0 1 0 0.000000 0.000000 2.901905 0.000000 0.000000 0.000000 S2.11 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 -2.901905 S2.12 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 2.901905 S2.13 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.14 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.15 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.16 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.17 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.18 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.19 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S2.20 0 1 0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.1 0 0 1 -2.901905 0.000000 0.000000 0.000000 0.000000 0.000000 S3.2 0 0 1 2.901905 0.000000 0.000000 0.000000 0.000000 0.000000 S3.3 0 0 1 0.000000 0.000000 0.000000 -2.901905 0.000000 0.000000 S3.4 0 0 1 0.000000 0.000000 0.000000 2.901905 0.000000 0.000000 S3.5 0 0 1 0.000000 0.000000 0.000000 0.000000 -2.901905 0.000000 S3.6 0 0 1 0.000000 0.000000 0.000000 0.000000 2.901905 0.000000 S3.7 0 0 1 0.000000 -2.901905 0.000000 0.000000 0.000000 0.000000 S3.8 0 0 1 0.000000 2.901905 0.000000 0.000000 0.000000 0.000000 S3.9 0 0 1 0.000000 0.000000 -2.901905 0.000000 0.000000 0.000000 S3.10 0 0 1 0.000000 0.000000 2.901905 0.000000 0.000000 0.000000 S3.11 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 -2.901905 S3.12 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 2.901905 S3.13 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.14 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.15 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.16 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.17 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.18 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.19 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 S3.20 0 0 1 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 x7 C0.1 1.000000 C0.2 -1.000000 C0.3 -1.000000 C0.4 1.000000 C0.5 1.000000 C0.6 -1.000000 C0.7 -1.000000 C0.8 1.000000 C0.9 -1.000000 C0.10 1.000000 C0.11 1.000000 C0.12 -1.000000 C0.13 -1.000000 C0.14 1.000000 C0.15 1.000000 C0.16 -1.000000 C0.17 -1.000000 C0.18 1.000000 C0.19 1.000000 C0.20 -1.000000 C0.21 -1.000000 C0.22 1.000000 C0.23 1.000000 C0.24 -1.000000 C0.25 1.000000 C0.26 -1.000000 C0.27 -1.000000 C0.28 1.000000 C0.29 1.000000 C0.30 -1.000000 C0.31 -1.000000 C0.32 1.000000 C0.33 0.000000 C0.34 0.000000 C0.35 0.000000 C0.36 0.000000 C0.37 0.000000 C0.38 0.000000 C1.1 1.000000 C1.2 -1.000000 C1.3 -1.000000 C1.4 1.000000 C1.5 1.000000 C1.6 -1.000000 C1.7 -1.000000 C1.8 1.000000 C1.9 -1.000000 C1.10 1.000000 C1.11 1.000000 C1.12 -1.000000 C1.13 -1.000000 C1.14 1.000000 C1.15 1.000000 C1.16 -1.000000 C1.17 -1.000000 C1.18 1.000000 C1.19 1.000000 C1.20 -1.000000 C1.21 -1.000000 C1.22 1.000000 C1.23 1.000000 C1.24 -1.000000 C1.25 1.000000 C1.26 -1.000000 C1.27 -1.000000 C1.28 1.000000 C1.29 1.000000 C1.30 -1.000000 C1.31 -1.000000 C1.32 1.000000 C1.33 0.000000 C1.34 0.000000 C1.35 0.000000 C1.36 0.000000 C1.37 0.000000 C1.38 0.000000 S2.1 0.000000 S2.2 0.000000 S2.3 0.000000 S2.4 0.000000 S2.5 0.000000 S2.6 0.000000 S2.7 0.000000 S2.8 0.000000 S2.9 0.000000 S2.10 0.000000 S2.11 0.000000 S2.12 0.000000 S2.13 -2.901905 S2.14 2.901905 S2.15 0.000000 S2.16 0.000000 S2.17 0.000000 S2.18 0.000000 S2.19 0.000000 S2.20 0.000000 S3.1 0.000000 S3.2 0.000000 S3.3 0.000000 S3.4 0.000000 S3.5 0.000000 S3.6 0.000000 S3.7 0.000000 S3.8 0.000000 S3.9 0.000000 S3.10 0.000000 S3.11 0.000000 S3.12 0.000000 S3.13 -2.901905 S3.14 2.901905 S3.15 0.000000 S3.16 0.000000 S3.17 0.000000 S3.18 0.000000 S3.19 0.000000 S3.20 0.000000 > > ## a properly designed plan > plan <- ccd.augment(FrF2(16,5,randomize=FALSE),6,randomize=FALSE) > set.seed(23232) > y <- round(rexp(38),4) > r.plan <- add.response(plan, y) > rsm(y~SO(A,B,C,D,E), r.plan) Call: rsm(formula = y ~ SO(A, B, C, D, E), data = r.plan) Coefficients: (Intercept) FO(A, B, C, D, E)A FO(A, B, C, D, E)B 1.02357 -0.15880 -0.03804 FO(A, B, C, D, E)C FO(A, B, C, D, E)D FO(A, B, C, D, E)E 0.30066 -0.00379 0.24679 TWI(A, B, C, D, E)A:B TWI(A, B, C, D, E)A:C TWI(A, B, C, D, E)A:D -0.34728 0.04085 0.14038 TWI(A, B, C, D, E)A:E TWI(A, B, C, D, E)B:C TWI(A, B, C, D, E)B:D -0.23841 0.12311 -0.36014 TWI(A, B, C, D, E)B:E TWI(A, B, C, D, E)C:D TWI(A, B, C, D, E)C:E 0.06652 -0.33824 0.37897 TWI(A, B, C, D, E)D:E PQ(A, B, C, D, E)A^2 PQ(A, B, C, D, E)B^2 -0.31088 -0.15769 -0.04922 PQ(A, B, C, D, E)C^2 PQ(A, B, C, D, E)D^2 PQ(A, B, C, D, E)E^2 -0.01524 0.03363 0.12226 > > ## replicated designs > plan <- ccd.augment(FrF2(16,5,repl=2,randomize=FALSE),6,randomize=FALSE) > > ## blocked design > plan <- ccd.augment(FrF2(32,5,blocks=4,randomize=FALSE),6,randomize=FALSE) > plan <- ccd.augment(FrF2(16,5,blocks=2,randomize=FALSE),6, bbreps=c(1,2),randomize=FALSE) > > ## two different versions of big designs because of different content of base.design > planblockpickbig <- FrF2(64,gen=c(7,11,14),blocks=16,alias.block.2fis=TRUE,randomize=FALSE) > set.seed(2323) > y <- round(rnorm(64),4) > planblockpickbig <- add.response(planblockpickbig,y) > plan <- ccd.augment(planblockpickbig,n0=1,randomize=FALSE) > planblockpickbig <- FrF2(64,nfactors=9,blocks=16,alias.block.2fis=TRUE,randomize=FALSE) > plan <- ccd.augment(planblockpickbig,randomize=FALSE) > > proc.time() user system elapsed 2.87 0.48 3.34