summary.OBsProb function

Summary of Posterior Probabilities from Objective Bayesian Design

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

## 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. ## Returns

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 Meyer R. D. (1986) An Analysis of Unreplicated Fractional Factorials., Technometrics 28 (1), 11--18. 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. 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. tools:::Rd_expr_doi("10.1007/s11749-015-0461-3") .

Author(s)

Marta Nai Ruscone.

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)