print.OBsProb: Printing Objective Posterior Probabilities from Bayesian...

Description Usage Arguments Value Author(s) References See Also Examples

Description

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

Usage

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    ## 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.

Value

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.

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. 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. doi: 10.1080/00224065.1993.11979432.

See Also

OBsProb, summary.OBsProb, plot.OBsProb.

Examples

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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)

OBsMD documentation built on July 1, 2020, 10:22 p.m.