BsMD: print.MD – R documentation

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print.MD

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.

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

Bayes Screening and Model Discrimination

v2020.4.30

GPL (>= 2)

Authors

Ernesto Barrios based on Daniel Meyer's code.

Initial release

2020-04-30