print.MD | R Documentation |
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'
print(x, X = FALSE, resp = FALSE, Xcand = TRUE, models = TRUE, nMod = x$nMod,
digits = 3, verbose=FALSE, ...)
x |
list of class |
X |
logical. If |
resp |
logical If |
Xcand |
logical. Prints the candidate runs if |
models |
logical. Competing models are printed if |
nMod |
integer. Top models to print. |
digits |
integer. Significant digits to use in the print out. |
verbose |
logical. If |
... |
additional arguments passed to |
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. |
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.
MD
, BsProb
# 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)
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