print.OBsProb function

Printing Objective Posterior Probabilities from Bayesian Design

Printing method for lists of class OBsProb. It prints the posterior probabilities of factors and models from the Objective Bayesian procedure.

## S3 method for class 'OBsProb'
print(x, X = TRUE, resp = TRUE, factors = TRUE, models = TRUE,
            nTop, digits = 3, plt = FALSE, verbose = FALSE, Sh= TRUE, CV=TRUE,...)

Arguments

• x: list. Object of OBsProb class, output from the OBsProb function.
• X: logical. If TRUE, the design matrix is printed.
• resp: logical. If TRUE, the response vector is printed.
• factors: logical. If TRUE, marginal posterior probabilities are printed .
• models: logical. If TRUE, models posterior probabilities are printed.
• nTop: integer. Number of the top ranked models to print.
• digits: integer. Significant digits to use for printing.
• plt: logical. If TRUE, factor marginal probabilities are plotted.
• verbose: logical. If TRUE, the unclass-ed list x is displayed.
• Sh: logical. If TRUE, the Shannon index is printed.
• CV: logical. If TRUE, the coefficient of variation is printed.
• ...: additional arguments passed to print function.

Returns

The function prints out marginal factors and models posterior probabilities. Returns invisible list with the components: - calc: numeric vector with general calculation information.

• probabilities: Data frame with the marginal posterior factor probabilities.

• models: Data frame with model posterior probabilities.

• Sh: Normalized Shannon heterogeneity index on the posterior probabilities of models

• CV: Coefficient of variation of factor 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") .

Author(s)

Marta Nai Ruscone.

See Also

OBsProb, summary.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)
plot(es5.OBsProb)