print.OBsProb {OBsMD} | R Documentation |
Printing method for lists of class OBsProb
. It prints the posterior
probabilities of factors and models from the Objective Bayesian procedure.
## S3 method for class 'OBsProb' print(x, X = TRUE, resp = TRUE, factors = TRUE, models = TRUE, nTop, digits = 3, plt = FALSE, verbose = FALSE, Sh= TRUE, CV=TRUE,...)
x |
list. Object of |
X |
logical. If |
resp |
logical. If |
factors |
logical. If |
models |
logical. If |
nTop |
integer. Number of the top ranked models to print. |
digits |
integer. Significant digits to use for printing. |
plt |
logical. If |
verbose |
logical. If |
Sh |
logical. If |
CV |
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 posterior probabilities. |
Sh |
Normalized Shannon heterogeneity index on the posterior probabilities of models |
CV |
Coefficient of variation of factor posterior probabilities. |
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.
OBsProb
, summary.OBsProb
, plot.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)