summary.MD {BsMD} | R Documentation |
Reduced printing method for lists of class MD
. Displays the
best MD criterion set of runs and their MD for follow-up experiments.
## S3 method for class 'MD' summary(object, digits = 3, verbose=FALSE, ...)
object |
list of |
digits |
integer. Significant digits to use in the print out. |
verbose |
logical. If |
... |
additional arguments passed to |
It prints out the marginal factors and models posterior probabilities and the top MD follow-up experiments with their corresponding MD statistic.
Ernesto Barrios.
Meyer, R. D., Steinberg, D. M. and Box, G. E. P. (1996). "Follow-Up Designs to Resolve Confounding in Multifactor Experiments (with discussion)". Technometrics, Vol. 38, No. 4, pp. 303–332.
Box, G. E. P and R. D. Meyer (1993). "Finding the Active Factors in Fractionated Screening Experiments". Journal of Quality Technology. Vol. 25. No. 2. pp. 94–105.
### 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) summary(reactor.MD)