Printing method for lists of class BsProb
. Prints the posterior
probabilities of factors and models from the Bayesian screening procedure.
# S3 method for BsProb
print(x, X = TRUE, resp = TRUE, factors = TRUE, models = TRUE,
nMod = 10, digits = 3, plt = FALSE, verbose = FALSE, ...)
list. Object of BsProb
class, output from the BsProb
function.
logical. If TRUE
, the design matrix is printed.
logical. If TRUE
, the response vector is printed.
logical. Marginal posterior probabilities are printed if TRUE
.
logical. If TRUE
models posterior probabilities are printed.
integer. Number of the top ranked models to print.
integer. Significant digits to use for printing.
logical. Factor marginal probabilities are plotted if TRUE
.
logical. If TRUE
, the unclass
-ed list x
is displayed.
additional arguments passed to print
function.
The function prints out marginal factors and models posterior probabilities. Returns invisible list with the components:
numeric vector with general calculation information.
Data frame with the marginal posterior factor probabilities.
Data frame with model the posterior probabilities.
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.
# NOT RUN {
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)
# }
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