summary.OMD {OBsMD} | R Documentation |
Summary of Optimal OMD Follow-Up Experiments
Description
Reduced printing method for lists of class OMD
. It displays the
best extra-runs according to the OMD criterion together with the correspondent OMD value.
Usage
## S3 method for class 'OMD'
summary(object, digits = 3, verbose=FALSE, ...)
Arguments
object |
list of |
digits |
integer. Significant digits to use in the print out. |
verbose |
logical. If |
... |
additional arguments passed to |
Value
It prints out the marginal factors and models posterior probabilities and the top OMD follow-up experiments with their corresponding OMD statistic.
Author(s)
Marta Nai Ruscone.
References
Box, G. E. P. and Meyer, R. D. (1993) Finding the Active Factors in Fractionated Screening Experiments., Journal of Quality Technology 25(2), 94–105. doi:10.1080/00224065.1993.11979432.
Consonni, G. and Deldossi, L. (2016) Objective Bayesian Model Discrimination in Follow-up design., Test 25(3), 397–412. doi:10.1007/s11749-015-0461-3.
Meyer, R. D., Steinberg, D. M. and Box, G. E. P. (1996) Follow-Up Designs to Resolve Confounding in Multifactor Experiments (with discussion)., Technometrics 38(4), 303–332. doi:10.2307/1271297.
See Also
Examples
library(OBsMD)
data(OBsMD.es5, package="OBsMD")
X <- as.matrix(OBsMD.es5[,1:5])
y <- OBsMD.es5[,6]
es5.OBsProb <- OBsProb(X=X,y=y,blk=0,mFac=5,mInt=2,nTop=32)
nMod <- 26
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,
-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=32,ncol=5,dimnames=list(1:32,c("A","B","C","D","E")),byrow=TRUE)
p_omd <- OMD(OBsProb=es5.OBsProb,nFac=5,nBlk=0,nMod=26,
nFoll=4,Xcand=Xcand,mIter=20,nStart=25,startDes=NULL,
top=30)
summary(p_omd)