plot.OBsProb: Plotting of Posterior Probabilities from Objective Bayesian... In OBsMD: Objective Bayesian Model Discrimination in Follow-Up Designs

Description

Method Function for plotting marginal factor posterior probabilities from Objective Bayesian Design.

Usage

 ```1 2``` ``` ## S3 method for class 'OBsProb' plot(x, code = TRUE, prt = FALSE, cex.axis=par("cex.axis"), ...) ```

Arguments

 `x` list. List of class `OBsProb` output from the `OBsProb` function. `code` logical. If `TRUE` coded factor names are used. `prt` logical. If `TRUE`, summary of the posterior probabilities calculation is printed. `cex.axis` Magnification used for the axis annotation. See `par`. `...` additional graphical parameters passed to `plot`.

Details

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.

Value

The function is called for its side effects. It returns an invisible `NULL`.

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.

`OBsProb`, `print.OBsProb`, `summary.OBsProb`.
 ```1 2 3 4 5 6 7 8 9``` ```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) ```