print.OMD: Print Optimal OMD Follow-Up Experiments
In OBsMD: Objective Bayesian Model Discrimination in Follow-Up Designs

print.OMD

R Documentation

Print Optimal OMD Follow-Up Experiments

Description

Printing method for lists of class OMD. It displays the best extra-runs according to the OMD criterion together with the correspondent OMD values.

Usage

    ## S3 method for class 'OMD'
print(x, X = FALSE, resp = FALSE, Xcand = TRUE, models = TRUE, nMod = x$nMod,
            digits = 3, verbose=FALSE, ...)

Arguments

`x`	list of class `OMD`. Output list of the `OMD` 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. If `TRUE`, prints the candidate runs.
`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 OMD objects, output of the OMD function. For example the marginal factors and models posterior probabilities and the top OMD follow-up experiments with their corresponding OMD 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 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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00224065.1993.11979432")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1271297")}.

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)
print(p_omd)

OBsMD documentation built on Sept. 11, 2024, 6:57 p.m.