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

In OBsMD: Objective Bayesian Model Discrimination in Follow-Up Designs

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 class '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.

Author(s)

Marta Nai Ruscone.

References

Box, G. E. P. and Meyer R. D. (1986) An Analysis of Unreplicated Fractional Factorials., Technometrics 28(1), 11–18. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.1986.10488093")}.

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")}.

Consonni, G. and Deldossi, L. (2016) Objective Bayesian Model Discrimination in Follow-up design., Test 25(3), 397–412. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11749-015-0461-3")}.

See Also

OBsProb, print.OBsProb, plot.OBsProb.

Examples

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)

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