OBsMD (version 3.0)

summary.OBsProb: Summary of Posterior Probabilities from Objective Bayesian Design

Description

Reduced printing method for class OBsProb lists. Prints posterior probabilities of factors and models from Objective Bayesian procedure.

Usage

# S3 method for OBsProb
summary(object, nTop = 10, digits = 3, ...)

Arguments

object

list. OBsProb class list. Output list of OBsProb function.

nTop

integer. Number of the top ranked models to print.

digits

integer. Significant digits to use.

...

additional arguments passed to summary generic function.

Value

The function prints out the marginal factors and models posterior probabilities. Returns invisible list with the components:

calc

Numeric vector with basic calculation information.

probabilities

Data frame with the marginal posterior probabilities.

models

Data frame with the models posterior probabilities.

References

Box, G. E. P and R. D. Meyer (1986). "An Analysis for Unreplicated Fractional Factorials". Technometrics. Vol. 28. No. 1. pp. 11--18.

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.

Consonni, G. and Deldossi, L. (2015), "Objective Bayesian model discrimination in follow-up experimental designs" DOI 10.1007/s11749-015-0461-3. TEST.

See Also

OBsProb, print.OBsProb, plot.OBsProb.

Examples

# NOT RUN {
library(OBsMD)
data(OBsMD.es5, package="OBsMD")
X <- as.matrix(OBsMD.es5[,1:5])
y <- OBsMD.es5[,6]
# Using for model prior probability a Beta with parameters a=1 b=1
es5.OBsProb <- OBsProb(X=X,y=y, abeta=1, bbeta=1, blk=0,mFac=5,mInt=2,nTop=32)
print(es5.OBsProb)
summary(es5.OBsProb)
# }