summary.MD {BsMD}

Summary of Best MD Follow-Up Experiments

Description

Reduced printing method for lists of class MD. Displays the best MD criterion set of runs and their MD for follow-up experiments.

Usage

    ## S3 method for class 'MD'
summary(object, digits = 3, verbose=FALSE, ...)

Arguments

object

list of MD class. Output list of MD function.

digits

integer. Significant digits to use in the print out.

verbose

logical. If TRUE, the unclass-ed object is displayed.

...

additional arguments passed to summary generic function.

Value

It prints out the marginal factors and models posterior probabilities and the top MD follow-up experiments with their corresponding MD statistic.

Author(s)

Ernesto Barrios.

References

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.

See Also

print.MD and MD

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)
summary(reactor.MD)