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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("BsMD-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('BsMD') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "BM86.data" > > ### * BM86.data > > flush(stderr()); flush(stdout()) > > ### Name: BM86.data > ### Title: Data sets in Box and Meyer (1986) > ### Aliases: BM86.data > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(BM86.data,package="BsMD") > print(BM86.data) X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 y1 y2 y3 y4 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 0.23 43.7 14.0 0.08 2 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 0.30 40.2 16.8 0.04 3 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 0.52 42.4 15.0 0.53 4 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 0.54 44.7 15.4 0.43 5 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 0.70 42.4 27.6 0.31 6 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 0.76 45.9 24.0 0.09 7 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1.00 42.2 27.4 0.12 8 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 0.96 40.6 22.6 0.36 9 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 0.32 42.4 22.3 0.79 10 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 0.39 45.5 17.1 0.68 11 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 0.61 43.6 21.5 0.73 12 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 0.66 40.6 17.5 0.08 13 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 0.89 44.0 15.9 0.77 14 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 0.97 40.2 21.9 0.38 15 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1.07 42.5 16.7 0.49 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.21 46.5 20.3 0.23 > > > > cleanEx(); ..nameEx <- "BM93.e1.data" > > ### * BM93.e1.data > > flush(stderr()); flush(stdout()) > > ### Name: BM93.e1.data > ### Title: Example 1 data in Box and Meyer (1993) > ### Aliases: BM93.e1.data > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(BM93.e1.data,package="BsMD") > print(BM93.e1.data) Run A B C D E y 1 6 1 -1 1 -1 -1 56 2 12 1 1 -1 1 -1 93 3 23 -1 1 1 -1 1 67 4 14 1 -1 1 1 -1 60 5 28 1 1 -1 1 1 77 6 24 1 1 1 -1 1 65 7 15 -1 1 1 1 -1 95 8 29 -1 -1 1 1 1 49 9 25 -1 -1 -1 1 1 44 10 18 1 -1 -1 -1 1 63 11 3 -1 1 -1 -1 -1 63 12 1 -1 -1 -1 -1 -1 61 > > > > cleanEx(); ..nameEx <- "BM93.e2.data" > > ### * BM93.e2.data > > flush(stderr()); flush(stdout()) > > ### Name: BM93.e2.data > ### Title: Example 2 data in Box and Meyer (1993) > ### Aliases: BM93.e2.data > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(BM93.e2.data,package="BsMD") > print(BM93.e2.data) A B C D E F G y 1 1 1 -1 1 1 1 -1 6.058 2 1 -1 1 1 1 -1 -1 4.733 3 -1 1 1 1 -1 -1 -1 4.625 4 1 1 1 -1 -1 -1 1 5.899 5 1 1 -1 -1 -1 1 -1 7.000 6 1 -1 -1 -1 1 -1 1 5.752 7 -1 -1 -1 1 -1 1 1 5.682 8 -1 -1 1 -1 1 1 -1 6.607 9 -1 1 -1 1 1 -1 1 5.818 10 1 -1 1 1 -1 1 1 5.917 11 -1 1 1 -1 1 1 1 5.863 12 -1 -1 -1 -1 -1 -1 -1 4.809 > > > > cleanEx(); ..nameEx <- "BM93.e3.data" > > ### * BM93.e3.data > > flush(stderr()); flush(stdout()) > > ### Name: BM93.e3.data > ### Title: Example 3 data in Box and Meyer (1993) > ### Aliases: BM93.e3.data > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(BM93.e3.data,package="BsMD") > print(BM93.e3.data) blk A B C D E F G H y 1 -1 -1 -1 -1 1 1 1 -1 1 14.0 2 -1 1 -1 -1 -1 -1 1 1 1 16.8 3 -1 -1 1 -1 -1 1 -1 1 1 15.0 4 -1 1 1 -1 1 -1 -1 -1 1 15.4 5 -1 -1 -1 1 1 -1 -1 1 1 27.6 6 -1 1 -1 1 -1 1 -1 -1 1 24.0 7 -1 -1 1 1 -1 -1 1 -1 1 27.4 8 -1 1 1 1 1 1 1 1 1 22.6 9 -1 1 1 1 -1 -1 -1 1 -1 22.3 10 -1 -1 1 1 1 1 -1 -1 -1 17.1 11 -1 1 -1 1 1 -1 1 -1 -1 21.5 12 -1 -1 -1 1 -1 1 1 1 -1 17.5 13 -1 1 1 -1 -1 1 1 -1 -1 15.9 14 -1 -1 1 -1 1 -1 1 1 -1 21.9 15 -1 1 -1 -1 1 1 -1 1 -1 16.7 16 -1 -1 -1 -1 -1 -1 -1 -1 -1 20.3 17 1 -1 1 1 1 -1 -1 -1 1 29.4 18 1 -1 1 -1 -1 -1 1 1 1 19.7 19 1 1 1 -1 -1 1 -1 -1 1 13.6 20 1 1 1 1 1 1 1 1 1 24.7 > > > > cleanEx(); ..nameEx <- "BsProb" > > ### * BsProb > > flush(stderr()); flush(stdout()) > > ### Name: BsProb > ### Title: Posterior Probabilities from Bayesian Screening Experiments > ### Aliases: BsProb > ### Keywords: design > > ### ** Examples > > library(BsMD) > data(BM86.data,package="BsMD") > X <- as.matrix(BM86.data[,1:15]) > y <- BM86.data["y1"] > # Using prior probability of p = 0.20, and k = 10 (gamma = 2.49) > drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.20, g = 2.49, ng = 1, nMod = 10) > plot(drillAdvance.BsProb) > summary(drillAdvance.BsProb) Calculations: nRun nFac nBlk mFac mInt p g totMod 16.00 15.00 0.00 15.00 1.00 0.20 2.49 32768.00 Factor probabilities: Factor Code Prob 1 none none 0.000 2 X1 x1 0.240 3 X2 x2 1.000 4 X3 x3 0.028 5 X4 x4 1.000 6 X5 x5 0.025 7 X6 x6 0.034 8 X7 x7 0.025 9 X8 x8 0.983 10 X9 x9 0.046 11 X10 x10 0.025 12 X11 x11 0.037 13 X12 x12 0.091 14 X13 x13 0.034 15 X14 x14 0.028 16 X15 x15 0.030 Model probabilities: Prob Sigma2 NumFac Factors M1 0.504 0.003 3 2,4,8 M2 0.148 0.002 4 1,2,4,8 M3 0.043 0.003 4 2,4,8,12 M4 0.022 0.003 4 2,4,8,9 M5 0.022 0.002 5 1,2,4,8,12 M6 0.018 0.003 4 2,4,8,11 M7 0.017 0.003 4 2,4,8,13 M8 0.017 0.003 4 2,4,6,8 M9 0.015 0.003 4 2,4,8,15 M10 0.014 0.003 4 2,4,8,14 > > # Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74) > drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10) > plot(drillAdvance.BsProbG, code = FALSE, prt = TRUE) Calculations: nRun nFac nBlk mFac mInt p g[1] g[3] 16.00 15.00 0.00 15.00 1.00 0.25 1.22 3.74 totMod 32768.00 Posterior probabilities for each gamma value: 1 2 3 gamma 1.220 2.480 3.740 none 0.000 0.000 0.000 x1 0.172 0.303 0.368 x2 0.998 1.000 1.000 x3 0.067 0.037 0.026 x4 1.000 1.000 1.000 x5 0.063 0.033 0.022 x6 0.072 0.046 0.035 x7 0.063 0.033 0.022 x8 0.893 0.988 0.994 x9 0.082 0.061 0.053 x10 0.064 0.033 0.023 x11 0.075 0.050 0.039 x12 0.108 0.123 0.135 x13 0.072 0.046 0.035 x14 0.067 0.037 0.026 x15 0.068 0.039 0.028 > > > > cleanEx(); ..nameEx <- "DanielPlot" > > ### * DanielPlot > > flush(stderr()); flush(stdout()) > > ### Name: DanielPlot > ### Title: Normal Plot of Effects > ### Aliases: DanielPlot > ### Keywords: design > > ### ** Examples > > ### Injection Molding Experiment. Box et al. 1978. > library(BsMD) > # Data > data(BM86.data,package="BsMD") # Design matrix and response > print(BM86.data) # from Box and Meyer (1986) X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 y1 y2 y3 y4 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 0.23 43.7 14.0 0.08 2 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 0.30 40.2 16.8 0.04 3 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 0.52 42.4 15.0 0.53 4 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 0.54 44.7 15.4 0.43 5 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 0.70 42.4 27.6 0.31 6 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 0.76 45.9 24.0 0.09 7 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1.00 42.2 27.4 0.12 8 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 0.96 40.6 22.6 0.36 9 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 0.32 42.4 22.3 0.79 10 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 0.39 45.5 17.1 0.68 11 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 0.61 43.6 21.5 0.73 12 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 0.66 40.6 17.5 0.08 13 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 0.89 44.0 15.9 0.77 14 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 0.97 40.2 21.9 0.38 15 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1.07 42.5 16.7 0.49 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.21 46.5 20.3 0.23 > > # Model Fitting. Box and Meyer (1986) example 3. > injectionMolding.lm <- lm(y3 ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + + X10 + X11 + X12 + X13 + X14 + X15, data = BM86.data) > print(coef(injectionMolding.lm)) # Model coefficients (Intercept) X1 X2 X3 X4 X5 19.75 -0.30 -0.20 -0.30 2.30 0.45 X6 X7 X8 X9 X10 X11 -0.10 -0.15 -0.60 0.35 0.05 0.15 X12 X13 X14 X15 -2.75 1.90 0.05 -0.30 > > # Daniel Plots > par(mfrow=c(1,3),oma=c(0,0,1,0),pty="s") > DanielPlot(injectionMolding.lm, half = TRUE, main = "Half-Normal Plot") > DanielPlot(injectionMolding.lm, main = "Normal Plot of Effects") > DanielPlot(injectionMolding.lm, + faclab = list(idx = c(12,4,13), lab = c(" -H"," VG"," -B")), + main = "Active Contrasts") > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "LenthPlot" > > ### * LenthPlot > > flush(stderr()); flush(stdout()) > > ### Name: LenthPlot > ### Title: Lenth's Plot of Effects > ### Aliases: LenthPlot > ### Keywords: design > > ### ** Examples > > ### Tensile Strength Experiment. Taguchi and Wu. 1980 > library(BsMD) > # Data > data(BM86.data,package="BsMD") # Design matrix and responses > print(BM86.data) # from Box and Meyer (1986) X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 y1 y2 y3 y4 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 0.23 43.7 14.0 0.08 2 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 0.30 40.2 16.8 0.04 3 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 0.52 42.4 15.0 0.53 4 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 0.54 44.7 15.4 0.43 5 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 0.70 42.4 27.6 0.31 6 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 0.76 45.9 24.0 0.09 7 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1.00 42.2 27.4 0.12 8 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 0.96 40.6 22.6 0.36 9 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 0.32 42.4 22.3 0.79 10 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 0.39 45.5 17.1 0.68 11 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 0.61 43.6 21.5 0.73 12 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 0.66 40.6 17.5 0.08 13 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 0.89 44.0 15.9 0.77 14 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 0.97 40.2 21.9 0.38 15 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1.07 42.5 16.7 0.49 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.21 46.5 20.3 0.23 > > # Model Fitting. Box and Meyer (1986) example 2. > tensileStrength.lm <- lm(y2 ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + + X10 + X11 + X12 + X13 + X14 + X15, data = BM86.data) > print(coef(tensileStrength.lm)) # Model coefficients (Intercept) X1 X2 X3 X4 X5 42.9625 0.0625 -0.0750 0.1500 0.0750 0.2000 X6 X7 X8 X9 X10 X11 -0.0125 0.1875 0.2000 -0.0250 0.2125 0.0625 X12 X13 X14 X15 0.0625 -0.1875 1.0750 1.5500 > > par(mfrow=c(1,2),pty="s") > DanielPlot(tensileStrength.lm, main = "Daniel Plot") > LenthPlot(tensileStrength.lm, main = "Lenth's Plot") alpha PSE ME SME 0.0500000 0.2250000 0.5783809 1.1741965 > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "MD" > > ### * MD > > flush(stderr()); flush(stdout()) > > ### Name: MD > ### Title: Best Model Discrimination (MD) Follow-Up Experiments > ### Aliases: MD > ### Keywords: design > > ### ** Examples > > ### Injection Molding Experiment. Meyer et al. 1996, example 2. > library(BsMD) > data(BM93.e3.data,package="BsMD") > X <- as.matrix(BM93.e3.data[1:16,c(1,2,4,6,9)]) > y <- BM93.e3.data[1:16,10] > p <- c(0.2356,0.2356,0.2356,0.2356,0.0566) > s2 <- c(0.5815,0.5815,0.5815,0.5815,0.4412) > nf <- c(3,3,3,3,4) > facs <- matrix(c(2,1,1,1,1,3,3,2,2,2,4,4,3,4,3,0,0,0,0,4),nrow=5, + dimnames=list(1:5,c("f1","f2","f3","f4"))) > nFDes <- 4 > Xcand <- matrix(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, + -1,-1,-1,-1,1,1,1,1,-1,-1,-1,-1,1,1,1,1, + -1,-1,1,1,-1,-1,1,1,-1,-1,1,1,-1,-1,1,1, + -1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1, + -1,1,1,-1,1,-1,-1,1,1,-1,-1,1,-1,1,1,-1), + nrow=16,dimnames=list(1:16,c("blk","f1","f2","f3","f4")) + ) > injectionMolding.MD <- MD(X = X, y = y, nFac = 4, nBlk = 1, mInt = 3, + g = 2, nMod = 5, p = p, s2 = s2, nf = nf, facs = facs, + nFDes = 4, Xcand = Xcand, mIter = 20, nStart = 25, top = 10) > summary(injectionMolding.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 16 4 1 3 2 2 5 Follow up: nCand nRuns maxIter nStart 16 4 20 25 Top 10 runs: D r1 r2 r3 r4 1 85.680 9 9 12 15 2 84.848 9 12 14 15 3 83.639 9 11 12 15 4 77.094 9 11 12 14 5 77.069 9 9 11 12 6 76.744 9 12 13 15 7 76.234 9 10 11 12 8 74.666 9 11 12 13 9 73.855 9 10 12 15 10 72.856 9 12 15 15 > > > ### Reactor Experiment. Meyer et al. 1996, example 3. > par(mfrow=c(1,2),pty="s") > data(Reactor.data,package="BsMD") > > # Posterior probabilities based on first 8 runs > X <- as.matrix(cbind(blk = rep(-1,8), Reactor.data[c(25,2,19,12,13,22,7,32), 1:5])) > y <- Reactor.data[c(25,2,19,12,13,22,7,32), 6] > reactor8.BsProb <- BsProb(X = X, y = y, blk = 1, mFac = 5, mInt = 3, + p =0.25, g =0.40, ng = 1, nMod = 32) > plot(reactor8.BsProb,prt=TRUE,,main="(8 runs)") Calculations: nRun nFac nBlk mFac mInt p g totMod 8.00 5.00 1.00 5.00 3.00 0.25 0.40 32.00 Factor probabilities: Factor Code Prob 1 none none 0.230 2 A x1 0.271 3 B x2 0.375 4 C x3 0.172 5 D x4 0.291 6 E x5 0.170 Model probabilities: Prob Sigma2 NumFac Factors M1 0.231 272 0 none M2 0.134 206 1 2 M3 0.075 244 1 4 M4 0.070 248 1 1 M5 0.055 154 2 1,2 M6 0.055 154 2 2,4 M7 0.055 154 2 1,4 M8 0.052 270 1 5 M9 0.051 272 1 3 M10 0.032 180 2 2,3 > > # MD optimal 4-run design > p <- reactor8.BsProb$ptop > s2 <- reactor8.BsProb$sigtop > nf <- reactor8.BsProb$nftop > facs <- reactor8.BsProb$jtop > nFDes <- 4 > Xcand <- as.matrix(cbind(blk = rep(+1,32), Reactor.data[,1:5])) > reactor.MD <- MD(X = X, y = y, nFac = 5, nBlk = 1, mInt = 3, g =0.40, nMod = 32, + p = p,s2 = s2, nf = nf, facs = facs, nFDes = 4, Xcand = Xcand, + mIter = 20, nStart = 25, top = 5) > summary(reactor.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 8.0 5.0 1.0 3.0 0.4 0.4 32.0 Follow up: nCand nRuns maxIter nStart 32 4 20 25 Top 5 runs: D r1 r2 r3 r4 1 0.615 4 10 11 26 2 0.610 4 10 11 28 3 0.608 4 10 26 27 4 0.606 4 10 12 27 5 0.603 4 11 12 26 > > # Posterior probabilities based on all 12 runs > X <- rbind(X, Xcand[c(4,10,11,26), ]) > y <- c(y, Reactor.data[c(4,10,11,26),6]) > reactor12.BsProb <- BsProb(X = X, y = y, blk = 1, mFac = 5, mInt = 3, + p = 0.25, g =1.20,ng = 1, nMod = 5) > plot(reactor12.BsProb,prt=TRUE,main="(12 runs)") Calculations: nRun nFac nBlk mFac mInt p g totMod 12.00 5.00 1.00 5.00 3.00 0.25 1.20 32.00 Factor probabilities: Factor Code Prob 1 none none 0.041 2 A x1 0.012 3 B x2 0.938 4 C x3 0.199 5 D x4 0.873 6 E x5 0.647 Model probabilities: Prob Sigma2 NumFac Factors M1 0.462 17.11 3 2,4,5 M2 0.209 66.63 2 2,4 M3 0.172 7.51 4 2,3,4,5 M4 0.064 167.76 1 2 M5 0.041 288.79 0 none > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "PB12Des" > > ### * PB12Des > > flush(stderr()); flush(stdout()) > > ### Name: PB12Des > ### Title: 12-run Plackett-Burman Design Matrix > ### Aliases: PB12Des > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(PB12Des,package="BsMD") > str(PB12Des) `data.frame': 12 obs. of 11 variables: $ x1 : int 1 1 -1 1 1 1 -1 -1 -1 1 ... $ x2 : int -1 1 1 -1 1 1 1 -1 -1 -1 ... $ x3 : int 1 -1 1 1 -1 1 1 1 -1 -1 ... $ x4 : int -1 1 -1 1 1 -1 1 1 1 -1 ... $ x5 : int -1 -1 1 -1 1 1 -1 1 1 1 ... $ x6 : int -1 -1 -1 1 -1 1 1 -1 1 1 ... $ x7 : int 1 -1 -1 -1 1 -1 1 1 -1 1 ... $ x8 : int 1 1 -1 -1 -1 1 -1 1 1 -1 ... $ x9 : int 1 1 1 -1 -1 -1 1 -1 1 1 ... $ x10: int -1 1 1 1 -1 -1 -1 1 -1 1 ... $ x11: int 1 -1 1 1 1 -1 -1 -1 1 -1 ... > X <- as.matrix(PB12Des) > print(t(X)%*%X) x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x1 12 0 0 0 0 0 0 0 0 0 0 x2 0 12 0 0 0 0 0 0 0 0 0 x3 0 0 12 0 0 0 0 0 0 0 0 x4 0 0 0 12 0 0 0 0 0 0 0 x5 0 0 0 0 12 0 0 0 0 0 0 x6 0 0 0 0 0 12 0 0 0 0 0 x7 0 0 0 0 0 0 12 0 0 0 0 x8 0 0 0 0 0 0 0 12 0 0 0 x9 0 0 0 0 0 0 0 0 12 0 0 x10 0 0 0 0 0 0 0 0 0 12 0 x11 0 0 0 0 0 0 0 0 0 0 12 > > > > cleanEx(); ..nameEx <- "Reactor.data" > > ### * Reactor.data > > flush(stderr()); flush(stdout()) > > ### Name: Reactor.data > ### Title: Reactor Experiment Data > ### Aliases: Reactor.data > ### Keywords: datasets > > ### ** Examples > > library(BsMD) > data(Reactor.data,package="BsMD") > print(Reactor.data) A B C D E y 1 -1 -1 -1 -1 -1 61 2 1 -1 -1 -1 -1 53 3 -1 1 -1 -1 -1 63 4 1 1 -1 -1 -1 61 5 -1 -1 1 -1 -1 53 6 1 -1 1 -1 -1 56 7 -1 1 1 -1 -1 54 8 1 1 1 -1 -1 61 9 -1 -1 -1 1 -1 69 10 1 -1 -1 1 -1 61 11 -1 1 -1 1 -1 94 12 1 1 -1 1 -1 93 13 -1 -1 1 1 -1 66 14 1 -1 1 1 -1 60 15 -1 1 1 1 -1 95 16 1 1 1 1 -1 98 17 -1 -1 -1 -1 1 56 18 1 -1 -1 -1 1 63 19 -1 1 -1 -1 1 70 20 1 1 -1 -1 1 65 21 -1 -1 1 -1 1 59 22 1 -1 1 -1 1 55 23 -1 1 1 -1 1 67 24 1 1 1 -1 1 65 25 -1 -1 -1 1 1 44 26 1 -1 -1 1 1 45 27 -1 1 -1 1 1 78 28 1 1 -1 1 1 77 29 -1 -1 1 1 1 49 30 1 -1 1 1 1 42 31 -1 1 1 1 1 81 32 1 1 1 1 1 82 > > > > cleanEx(); ..nameEx <- "plot.BsProb" > > ### * plot.BsProb > > flush(stderr()); flush(stdout()) > > ### Name: plot.BsProb > ### Title: Plotting of Posterior Probabilities from Bayesian Screening > ### Aliases: plot.BsProb > ### Keywords: design hplot > > ### ** Examples > > library(BsMD) > data(BM86.data,package="BsMD") > X <- as.matrix(BM86.data[,1:15]) > y <- BM86.data["y1"] > # Using prior probability of p = 0.20, and k = 10 (gamma = 2.49) > drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.20, g = 2.49, ng = 1, nMod = 10) > plot(drillAdvance.BsProb) > summary(drillAdvance.BsProb) Calculations: nRun nFac nBlk mFac mInt p g totMod 16.00 15.00 0.00 15.00 1.00 0.20 2.49 32768.00 Factor probabilities: Factor Code Prob 1 none none 0.000 2 X1 x1 0.240 3 X2 x2 1.000 4 X3 x3 0.028 5 X4 x4 1.000 6 X5 x5 0.025 7 X6 x6 0.034 8 X7 x7 0.025 9 X8 x8 0.983 10 X9 x9 0.046 11 X10 x10 0.025 12 X11 x11 0.037 13 X12 x12 0.091 14 X13 x13 0.034 15 X14 x14 0.028 16 X15 x15 0.030 Model probabilities: Prob Sigma2 NumFac Factors M1 0.504 0.003 3 2,4,8 M2 0.148 0.002 4 1,2,4,8 M3 0.043 0.003 4 2,4,8,12 M4 0.022 0.003 4 2,4,8,9 M5 0.022 0.002 5 1,2,4,8,12 M6 0.018 0.003 4 2,4,8,11 M7 0.017 0.003 4 2,4,8,13 M8 0.017 0.003 4 2,4,6,8 M9 0.015 0.003 4 2,4,8,15 M10 0.014 0.003 4 2,4,8,14 > > # Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74) > drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10) > plot(drillAdvance.BsProbG, code = FALSE, prt = TRUE) Calculations: nRun nFac nBlk mFac mInt p g[1] g[3] 16.00 15.00 0.00 15.00 1.00 0.25 1.22 3.74 totMod 32768.00 Posterior probabilities for each gamma value: 1 2 3 gamma 1.220 2.480 3.740 none 0.000 0.000 0.000 x1 0.172 0.303 0.368 x2 0.998 1.000 1.000 x3 0.067 0.037 0.026 x4 1.000 1.000 1.000 x5 0.063 0.033 0.022 x6 0.072 0.046 0.035 x7 0.063 0.033 0.022 x8 0.893 0.988 0.994 x9 0.082 0.061 0.053 x10 0.064 0.033 0.023 x11 0.075 0.050 0.039 x12 0.108 0.123 0.135 x13 0.072 0.046 0.035 x14 0.067 0.037 0.026 x15 0.068 0.039 0.028 > > > > cleanEx(); ..nameEx <- "print.BsProb" > > ### * print.BsProb > > flush(stderr()); flush(stdout()) > > ### Name: print.BsProb > ### Title: Printing Posterior Probabilities from Bayesian Screening > ### Aliases: print.BsProb > ### Keywords: design > > ### ** Examples > > library(BsMD) > data(BM86.data,package="BsMD") > X <- as.matrix(BM86.data[,1:15]) > y <- BM86.data["y1"] > # Using prior probability of p = 0.20, and k = 10 (gamma = 2.49) > drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.20, g = 2.49, ng = 1, nMod = 10) > print(drillAdvance.BsProb) Design Matrix: X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 2 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 3 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 4 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 5 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 6 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 7 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 8 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 9 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 10 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 11 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 12 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 13 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 14 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 15 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Response vector: 0.23 0.3 0.52 0.54 0.7 0.76 1 0.96 0.32 0.39 0.61 0.66 0.89 0.97 1.07 1.21 Calculations: nRun nFac nBlk mFac mInt p g totMod 16.00 15.00 0.00 15.00 1.00 0.20 2.49 32768.00 Output file: BsPrint.out Factor probabilities: Factor Code Prob 1 none none 0.000 2 X1 x1 0.240 3 X2 x2 1.000 4 X3 x3 0.028 5 X4 x4 1.000 6 X5 x5 0.025 7 X6 x6 0.034 8 X7 x7 0.025 9 X8 x8 0.983 10 X9 x9 0.046 11 X10 x10 0.025 12 X11 x11 0.037 13 X12 x12 0.091 14 X13 x13 0.034 15 X14 x14 0.028 16 X15 x15 0.030 Model probabilities: Prob Sigma2 NumFac Factors M1 0.504 0.003 3 2,4,8 M2 0.148 0.002 4 1,2,4,8 M3 0.043 0.003 4 2,4,8,12 M4 0.022 0.003 4 2,4,8,9 M5 0.022 0.002 5 1,2,4,8,12 M6 0.018 0.003 4 2,4,8,11 M7 0.017 0.003 4 2,4,8,13 M8 0.017 0.003 4 2,4,6,8 M9 0.015 0.003 4 2,4,8,15 M10 0.014 0.003 4 2,4,8,14 > plot(drillAdvance.BsProb) > > # Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74) > drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10) > print(drillAdvance.BsProbG, X = FALSE, resp = FALSE) Calculations: nRun nFac nBlk mFac mInt p g[1] g[3] 16.00 15.00 0.00 15.00 1.00 0.25 1.22 3.74 totMod 32768.00 Output file: BsPrint.out Weighted factor probabilities: Factor Code Prob 1 none none 0.000 2 X1 x1 0.331 3 X2 x2 1.000 4 X3 x3 0.033 5 X4 x4 1.000 6 X5 x5 0.029 7 X6 x6 0.041 8 X7 x7 0.029 9 X8 x8 0.987 10 X9 x9 0.058 11 X10 x10 0.029 12 X11 x11 0.046 13 X12 x12 0.128 14 X13 x13 0.041 15 X14 x14 0.033 16 X15 x15 0.035 Values of posterior density of gamma: gamma pgam 1 1.22 1002159 2 2.48 11697753 3 3.74 12868144 Posterior probabilities for each gamma value: 1 2 3 gamma 1.220 2.480 3.740 none 0.000 0.000 0.000 x1 0.172 0.303 0.368 x2 0.998 1.000 1.000 x3 0.067 0.037 0.026 x4 1.000 1.000 1.000 x5 0.063 0.033 0.022 x6 0.072 0.046 0.035 x7 0.063 0.033 0.022 x8 0.893 0.988 0.994 x9 0.082 0.061 0.053 x10 0.064 0.033 0.023 x11 0.075 0.050 0.039 x12 0.108 0.123 0.135 x13 0.072 0.046 0.035 x14 0.067 0.037 0.026 x15 0.068 0.039 0.028 > plot(drillAdvance.BsProbG) > > > > cleanEx(); ..nameEx <- "print.MD" > > ### * print.MD > > flush(stderr()); flush(stdout()) > > ### Name: print.MD > ### Title: Print Best MD Follow-Up Experiments > ### Aliases: print.MD > ### Keywords: design > > ### ** Examples > > # Injection Molding Experiment. Meyer et al. 1996. Example 2. > # MD for one extra experiment. > library(BsMD) > data(BM93.e3.data,package="BsMD") > X <- as.matrix(BM93.e3.data[1:16,c(1,2,4,6,9)]) > y <- BM93.e3.data[1:16,10] > nBlk <- 1 > nFac <- 4 > mInt <- 3 > g <- 2 > nMod <- 5 > p <- c(0.2356,0.2356,0.2356,0.2356,0.0566) > s2 <- c(0.5815,0.5815,0.5815,0.5815,0.4412) > nf <- c(3,3,3,3,4) > facs <- matrix(c(2,1,1,1,1,3,3,2,2,2,4,4,3,4,3,0,0,0,0,4),nrow=5, + dimnames=list(1:5,c("f1","f2","f3","f4"))) > nFDes <- 1 > Xcand <- matrix(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1, + -1,-1,-1,-1,1,1,1,1,-1,-1,-1,-1,1,1,1,1, + -1,-1,1,1,-1,-1,1,1,-1,-1,1,1,-1,-1,1,1, + -1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1,-1,1, + -1,1,1,-1,1,-1,-1,1,1,-1,-1,1,-1,1,1,-1), + nrow=16,dimnames=list(1:16,c("blk","f1","f2","f3","f4")) + ) > mIter <- 0 > startDes <- matrix(c(9,11,12,15),nrow=4) > top <- 10 > injectionMolding.MD <- MD(X=X,y=y,nFac=nFac,nBlk=nBlk,mInt=mInt,g=g, + nMod=nMod,p=p,s2=s2,nf=nf,facs=facs, + nFDes=nFDes,Xcand=Xcand,mIter=mIter,startDes=startDes,top=top) > > print(injectionMolding.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 16 4 1 3 2 2 5 Follow up: nCand nRuns maxIter nStart 16 1 0 4 Competing Models: Prob Sigma2 NumFac Factors 1 0.236 0.582 3 2,3,4 2 0.236 0.582 3 1,3,4 3 0.236 0.582 3 1,2,3 4 0.236 0.582 3 1,2,4 5 0.057 0.441 4 1,2,3,4 Candidate runs: blk f1 f2 f3 f4 1 1 -1 -1 -1 -1 2 1 -1 -1 1 1 3 1 -1 1 -1 1 4 1 -1 1 1 -1 5 1 1 -1 -1 1 6 1 1 -1 1 -1 7 1 1 1 -1 -1 8 1 1 1 1 1 9 1 -1 -1 -1 1 10 1 -1 -1 1 -1 11 1 -1 1 -1 -1 12 1 -1 1 1 1 13 1 1 -1 -1 -1 14 1 1 -1 1 1 15 1 1 1 -1 1 16 1 1 1 1 -1 Search trace output file: MDPrint.out Top 4 runs: D r1 1 2.349 12 2 2.341 9 3 1.563 15 4 1.261 11 > summary(injectionMolding.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 16 4 1 3 2 2 5 Follow up: nCand nRuns maxIter nStart 16 1 0 4 Top 4 runs: D r1 1 2.349 12 2 2.341 9 3 1.563 15 4 1.261 11 > > > > > cleanEx(); ..nameEx <- "summary.BsProb" > > ### * summary.BsProb > > flush(stderr()); flush(stdout()) > > ### Name: summary.BsProb > ### Title: Summary of Posterior Probabilities from Bayesian Screening > ### Aliases: summary.BsProb > ### Keywords: design > > ### ** Examples > > library(BsMD) > data(BM86.data,package="BsMD") > X <- as.matrix(BM86.data[,1:15]) > y <- BM86.data["y1"] > # Using prior probability of p = 0.20, and k = 10 (gamma = 2.49) > drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.20, g = 2.49, ng = 1, nMod = 10) > plot(drillAdvance.BsProb) > summary(drillAdvance.BsProb) Calculations: nRun nFac nBlk mFac mInt p g totMod 16.00 15.00 0.00 15.00 1.00 0.20 2.49 32768.00 Factor probabilities: Factor Code Prob 1 none none 0.000 2 X1 x1 0.240 3 X2 x2 1.000 4 X3 x3 0.028 5 X4 x4 1.000 6 X5 x5 0.025 7 X6 x6 0.034 8 X7 x7 0.025 9 X8 x8 0.983 10 X9 x9 0.046 11 X10 x10 0.025 12 X11 x11 0.037 13 X12 x12 0.091 14 X13 x13 0.034 15 X14 x14 0.028 16 X15 x15 0.030 Model probabilities: Prob Sigma2 NumFac Factors M1 0.504 0.003 3 2,4,8 M2 0.148 0.002 4 1,2,4,8 M3 0.043 0.003 4 2,4,8,12 M4 0.022 0.003 4 2,4,8,9 M5 0.022 0.002 5 1,2,4,8,12 M6 0.018 0.003 4 2,4,8,11 M7 0.017 0.003 4 2,4,8,13 M8 0.017 0.003 4 2,4,6,8 M9 0.015 0.003 4 2,4,8,15 M10 0.014 0.003 4 2,4,8,14 > > # Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74) > drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1, + p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10) > plot(drillAdvance.BsProbG) > summary(drillAdvance.BsProbG) Calculations: nRun nFac nBlk mFac mInt p g[1] g[3] 16.00 15.00 0.00 15.00 1.00 0.25 1.22 3.74 totMod 32768.00 Posterior probabilities for each gamma value: 1 2 3 gamma 1.220 2.480 3.740 none 0.000 0.000 0.000 x1 0.172 0.303 0.368 x2 0.998 1.000 1.000 x3 0.067 0.037 0.026 x4 1.000 1.000 1.000 x5 0.063 0.033 0.022 x6 0.072 0.046 0.035 x7 0.063 0.033 0.022 x8 0.893 0.988 0.994 x9 0.082 0.061 0.053 x10 0.064 0.033 0.023 x11 0.075 0.050 0.039 x12 0.108 0.123 0.135 x13 0.072 0.046 0.035 x14 0.067 0.037 0.026 x15 0.068 0.039 0.028 > > > > cleanEx(); ..nameEx <- "summary.MD" > > ### * summary.MD > > flush(stderr()); flush(stdout()) > > ### Name: summary.MD > ### Title: Summary of Best MD Follow-Up Experiments > ### Aliases: summary.MD > ### Keywords: design > > ### ** Examples > > ### Reactor Experiment. Meyer et al. 1996, example 3. > library(BsMD) > data(Reactor.data,package="BsMD") > > # Posterior probabilities based on first 8 runs > X <- as.matrix(cbind(blk = rep(-1,8), Reactor.data[c(25,2,19,12,13,22,7,32), 1:5])) > y <- Reactor.data[c(25,2,19,12,13,22,7,32), 6] > reactor.BsProb <- BsProb(X = X, y = y, blk = 1, mFac = 5, mInt = 3, + p =0.25, g =0.40, ng = 1, nMod = 32) > > # MD optimal 4-run design > p <- reactor.BsProb$ptop > s2 <- reactor.BsProb$sigtop > nf <- reactor.BsProb$nftop > facs <- reactor.BsProb$jtop > nFDes <- 4 > Xcand <- as.matrix(cbind(blk = rep(+1,32), Reactor.data[,1:5])) > reactor.MD <- MD(X = X, y = y, nFac = 5, nBlk = 1, mInt = 3, g =0.40, nMod = 32, + p = p,s2 = s2, nf = nf, facs = facs, nFDes = 4, Xcand = Xcand, + mIter = 20, nStart = 25, top = 5) > print(reactor.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 8.0 5.0 1.0 3.0 0.4 0.4 32.0 Follow up: nCand nRuns maxIter nStart 32 4 20 25 Competing Models: Prob Sigma2 NumFac Factors M1 0.231 272.0 0 none M2 0.134 206.2 1 2 M3 0.075 243.8 1 4 M4 0.070 247.9 1 1 M5 0.055 154.0 2 1,2 M6 0.055 154.0 2 2,4 M7 0.055 154.0 2 1,4 M8 0.052 269.7 1 5 M9 0.051 271.9 1 3 M10 0.032 180.0 2 2,3 M11 0.023 197.7 2 2,5 M12 0.022 120.8 3 1,2,4 M13 0.017 215.5 2 4,5 M14 0.012 237.5 2 3,4 M15 0.011 245.6 2 3,5 M16 0.011 245.6 2 1,5 M17 0.011 245.6 2 1,3 M18 0.009 119.3 3 2,3,5 M19 0.009 119.3 3 3,4,5 M20 0.009 119.3 3 2,4,5 M21 0.009 119.3 3 1,4,5 M22 0.009 119.3 3 1,2,3 M23 0.009 119.3 3 1,2,5 M24 0.009 119.3 3 2,3,4 M25 0.009 119.3 3 1,3,4 M26 0.004 76.4 4 1,2,3,4 M27 0.003 77.0 4 1,2,4,5 M28 0.003 76.4 4 2,3,4,5 M29 0.003 84.3 4 1,3,4,5 M30 0.002 238.1 3 1,3,5 M31 0.002 94.9 4 1,2,3,5 M32 0.001 53.7 5 1,2,3,4,5 Candidate runs: blk A B C D E 1 1 -1 -1 -1 -1 -1 2 1 1 -1 -1 -1 -1 3 1 -1 1 -1 -1 -1 4 1 1 1 -1 -1 -1 5 1 -1 -1 1 -1 -1 6 1 1 -1 1 -1 -1 7 1 -1 1 1 -1 -1 8 1 1 1 1 -1 -1 9 1 -1 -1 -1 1 -1 10 1 1 -1 -1 1 -1 11 1 -1 1 -1 1 -1 12 1 1 1 -1 1 -1 13 1 -1 -1 1 1 -1 14 1 1 -1 1 1 -1 15 1 -1 1 1 1 -1 16 1 1 1 1 1 -1 17 1 -1 -1 -1 -1 1 18 1 1 -1 -1 -1 1 19 1 -1 1 -1 -1 1 20 1 1 1 -1 -1 1 21 1 -1 -1 1 -1 1 22 1 1 -1 1 -1 1 23 1 -1 1 1 -1 1 24 1 1 1 1 -1 1 25 1 -1 -1 -1 1 1 26 1 1 -1 -1 1 1 27 1 -1 1 -1 1 1 28 1 1 1 -1 1 1 29 1 -1 -1 1 1 1 30 1 1 -1 1 1 1 31 1 -1 1 1 1 1 32 1 1 1 1 1 1 Search trace output file: MDPrint.out Top 5 runs: D r1 r2 r3 r4 1 0.615 4 10 11 26 2 0.610 4 10 11 28 3 0.608 4 10 26 27 4 0.606 4 10 12 27 5 0.603 4 11 12 26 > summary(reactor.MD) Base: nRuns nFac nBlk maxInt gMain gInter nMod 8.0 5.0 1.0 3.0 0.4 0.4 32.0 Follow up: nCand nRuns maxIter nStart 32 4 20 25 Top 5 runs: D r1 r2 r3 r4 1 0.615 4 10 11 26 2 0.610 4 10 11 28 3 0.608 4 10 26 27 4 0.606 4 10 12 27 5 0.603 4 11 12 26 > > > > ### *