plot.BsProb {BsMD} | R Documentation |
Method function for plotting marginal factor posterior probabilities for Bayesian screening.
## S3 method for class 'BsProb'
plot(x, code = TRUE, prt = FALSE, cex.axis=par("cex.axis"), ...)
x |
list. List of class |
code |
logical. If |
prt |
logical. If |
cex.axis |
Magnification used for the axis annotation.
See |
... |
additional graphical parameters passed to |
A spike plot, similar to barplots, is produced with a spike for each factor.
Marginal posterior probabilities are used for the vertical axis.
If code=TRUE
, X1
, X2
, ... are used to label the factors
otherwise the original factor names are used.
If prt=TRUE
, the print.BsProb
function is called
and the posterior probabilities are displayed.
When BsProb
is called for more than one value of gamma (g
),
the spikes for each factor probability are overlapped to show the
resulting range of each marginal probability.
The function is called for its side effects. It returns an invisible
NULL
.
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
, print.BsProb
, summary.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)
plot(drillAdvance.BsProb)
summary(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)
plot(drillAdvance.BsProbG, code = FALSE, prt = TRUE)