summary.OBsProb {OBsMD} | R Documentation |
Reduced printing method for class OBsProb
lists. Prints
posterior probabilities of factors and models from Objective Bayesian procedure.
## S3 method for class 'OBsProb' summary(object, nTop = 10, digits = 3, ...)
object |
list. |
nTop |
integer. Number of the top ranked models to print. |
digits |
integer. Significant digits to use. |
... |
additional arguments passed to |
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. |
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
, 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)