BsMD (version 2023.920)

summary.MD: 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 MD
summary(object, digits = 3, verbose=FALSE, ...)

Value

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

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.

Author

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)