summary.OBsProb {OBsMD} | R Documentation |
Summary of Posterior Probabilities from Objective Bayesian Design
Description
Reduced printing method for class OBsProb
lists. Prints
posterior probabilities of factors and models from Objective Bayesian procedure.
Usage
## S3 method for class 'OBsProb'
summary(object, nTop = 10, digits = 3, ...)
Arguments
object |
list. |
nTop |
integer. Number of the top ranked models to print. |
digits |
integer. Significant digits to use. |
... |
additional arguments passed to |
Value
The function prints out the marginal factors and models posterior probabilities. Returns invisible list with the components:
calc |
Numeric vector with basic calculation information. |
probabilities |
Data frame with the marginal posterior probabilities. |
models |
Data frame with the models posterior probabilities. |
Author(s)
Marta Nai Ruscone.
References
Box, G. E. P. and Meyer R. D. (1986) An Analysis of Unreplicated Fractional Factorials., Technometrics 28(1), 11–18. doi:10.1080/00401706.1986.10488093.
Box, G. E. P. and Meyer, R. D. (1993) Finding the Active Factors in Fractionated Screening Experiments., Journal of Quality Technology 25(2), 94–105. doi:10.1080/00224065.1993.11979432.
Consonni, G. and Deldossi, L. (2016) Objective Bayesian Model Discrimination in Follow-up design., Test 25(3), 397–412. doi:10.1007/s11749-015-0461-3.
See Also
OBsProb
, print.OBsProb
, plot.OBsProb
.
Examples
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)