Description Usage Arguments Details Author(s) See Also Examples

Make a plot of the marginal likelihood against the prior strength parameter, highlighting the value used to produce the network.

1 2 |

`output` |
The object returned from the interventionalInference function. |

`xlab` |
A label for the prior strength axis. |

`ylab` |
A label for the marginal likelihood axis. |

`col.max` |
The colour of the line highlighting the maximum. |

`lty.max` |
The line type of the highlight. |

`lwd.max` |
The line width of the highlight. |

`...` |
Other arguments, such as |

It is important to check that the Empirical Bayes calculation is doing something sensible.

Simon Spencer

`interventionalDBN-package`

,`interventionalInference`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
library(interventionalDBN)
data(interventionalData)# loads interventionalData.
# Load your own data spreadsheet using myData<-read.csv("myDataFile.csv").
# Format the data for network inference
d<-formatData(interventionalData)
# EGFRi is active in conditions 2 and 4, AKTi is active in conditions 3 and 4.
# Each condition has 8 timepoints.
Z<-matrix(0,32,15)
Z[9:16,1]<-1 # EGFR (node 1) inhibited in condition 2
Z[25:32,1]<-1 # EGFR inhibited in condition 4
Z[17:24,8]<-1 # AKT (node 8) inhibited in condition 3
Z[25:32,8]<-1 # AKT inhibited in condition 4
# Perform network inference with Hamming Prior that prefers self-edges,
# and use Empirical Bayes to choose the priorStrength.
myNetwork4<-interventionalInference(d$y,d$X0,d$X1,Z,max.indeg=3,
perfectOut=TRUE,fixedEffectOut=TRUE,
priorType="Hamming",priorGraph=diag(rep(1,15)),priorStrength=0:10/2)
# You should always check to see if the Empirical Bayes appears to be working.
plotMaxML(myNetwork4)
``` |

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