print.MD: Print Best MD Follow-Up Experiments

In BsMD: Bayes Screening and Model Discrimination

Print Best MD Follow-Up Experiments

Description

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'
print(x, X = FALSE, resp = FALSE, Xcand = TRUE, models = TRUE, nMod = x$nMod,
            digits = 3, verbose=FALSE, ...)

Arguments

x	list of class MD. Output list of the MD function.
X	logical. If TRUE, the initial design matrix is printed.
resp	logical If TRUE, the response vector of initial design is printed.
Xcand	logical. Prints the candidate runs if TRUE.
models	logical. Competing models are printed if TRUE.
nMod	integer. Top models to print.
digits	integer. Significant digits to use in the print out.
verbose	logical. If TRUE, the unclass-ed x is displayed.
...	additional arguments passed to print generic function.

Value

The function is mainly called for its side effects. Prints out the selected components of the class MD objects, output of the MD function. For example the marginal factors and models posterior probabilities and the top MD follow-up experiments with their corresponding MD statistic. It returns invisible list with the components:

calc	Numeric vector with basic calculation information.
models	Data frame with the competing models posterior probabilities.
follow-up	Data frame with the runs for follow-up experiments and 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

MD, BsProb

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)
summary(injectionMolding.MD)

BsMD documentation built on Sept. 19, 2023, 5:07 p.m.