BsMD (version 2023.920)

print.BsProb: Printing Posterior Probabilities from Bayesian Screening

Description

Printing method for lists of class BsProb. Prints the posterior probabilities of factors and models from the Bayesian screening procedure.

Usage

# S3 method for BsProb
print(x, X = TRUE, resp = TRUE, factors = TRUE, models = TRUE,
            nMod = 10, digits = 3, plt = FALSE, verbose = FALSE, ...)

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 the posterior probabilities.

Arguments

x

list. Object of BsProb class, output from the BsProb function.

X

logical. If TRUE, the design matrix is printed.

resp

logical. If TRUE, the response vector is printed.

factors

logical. Marginal posterior probabilities are printed if TRUE.

models

logical. If TRUE models posterior probabilities are printed.

nMod

integer. Number of the top ranked models to print.

digits

integer. Significant digits to use for printing.

plt

logical. Factor marginal probabilities are plotted if TRUE.

verbose

logical. If TRUE, the unclass-ed list x is displayed.

...

additional arguments passed to print function.

Author

Ernesto Barrios.

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.

See Also

BsProb, summary.BsProb, plot.BsProb.

Examples

library(BsMD)
data(BM86.data,package="BsMD")
X <- as.matrix(BM86.data[,1:15])
y <- BM86.data["y1"]
# Using prior probability of p = 0.20, and k = 10 (gamma = 2.49)
drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1,
            p = 0.20, g = 2.49, ng = 1, nMod = 10)
print(drillAdvance.BsProb)
plot(drillAdvance.BsProb)

# Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74)
drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1,
            p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10)
print(drillAdvance.BsProbG, X = FALSE, resp = FALSE)
plot(drillAdvance.BsProbG)