print.BsProb {BsMD} R Documentation

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


### 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.

### 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.

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.

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)