print.BsProb {BsMD} | R Documentation |
Printing method for lists of class BsProb
. Prints the posterior
probabilities of factors and models from the Bayesian screening procedure.
## S3 method for class 'BsProb'
print(x, X = TRUE, resp = TRUE, factors = TRUE, models = TRUE,
nMod = 10, digits = 3, plt = FALSE, verbose = FALSE, ...)
x |
list. Object of |
X |
logical. If |
resp |
logical. If |
factors |
logical. Marginal posterior probabilities are printed if |
models |
logical. If |
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 |
verbose |
logical. If |
... |
additional arguments passed to |
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.
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
.
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)