BsMD: print.BsProb – R documentation

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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 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.

Author(s)

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.

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)

BsMD

Bayes Screening and Model Discrimination

v2020.4.30

GPL (>= 2)

Authors

Ernesto Barrios based on Daniel Meyer's code.

Initial release

2020-04-30