plot.OBsProb {OBsMD} | R Documentation |
Method Function for plotting marginal factor posterior probabilities from Objective Bayesian Design.
## S3 method for class 'OBsProb' 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.OBsProb
function is called
and the marginal posterior probabilities are displayed.
The function is called for its side effects. It returns an invisible
NULL
.
Marta Nai Ruscone.
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.
Consonni, G. and Deldossi, L. (2015), "Objective Bayesian model discrimination in follow-up experimental designs" DOI 10.1007/s11749-015-0461-3. TEST.
OBsProb
, print.OBsProb
, summary.OBsProb
.
library(OBsMD) data(OBsMD.es5, package="OBsMD") X <- as.matrix(OBsMD.es5[,1:5]) y <- OBsMD.es5[,6] # Using for model prior probability a Beta with parameters a=1 b=1 es5.OBsProb <- OBsProb(X=X,y=y, abeta=1, bbeta=1, blk=0,mFac=5,mInt=2,nTop=32) print(es5.OBsProb) summary(es5.OBsProb) plot(es5.OBsProb)