plot.BsProb {BsMD} | R Documentation |

## Plotting of Posterior Probabilities from Bayesian Screening

### Description

Method function for plotting marginal factor posterior probabilities for Bayesian screening.

### Usage

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

### Arguments

`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 |

### 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.BsProb`

function is called
and the posterior probabilities are displayed.
When `BsProb`

is called for more than one value of gamma (`g`

),
the spikes for each factor probability are overlapped to show the
resulting range of each marginal probability.

### Value

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

.

### Author(s)

Ernesto Barrios.

### References

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.

### See Also

`BsProb`

, `print.BsProb`

, `summary.BsProb`

.

### Examples

```
library(BsMD)
data(BM86.data,package="BsMD")
X <- as.matrix(BM86.data[,1:15])
y <- BM86.data["y1"]
# Using prior probability of p = 0.20, and k = 10 (gamma = 2.49)
drillAdvance.BsProb <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1,
p = 0.20, g = 2.49, ng = 1, nMod = 10)
plot(drillAdvance.BsProb)
summary(drillAdvance.BsProb)
# Using prior probability of p = 0.20, and a 5 <= k <= 15 (1.22 <= gamma <= 3.74)
drillAdvance.BsProbG <- BsProb(X = X, y = y, blk = 0, mFac = 15, mInt = 1,
p = 0.25, g = c(1.22, 3.74), ng = 3, nMod = 10)
plot(drillAdvance.BsProbG, code = FALSE, prt = TRUE)
```

*BsMD*version 2023.920 Index]