## Plotting arc strengths with Rgraphviz and lattice

Measuring the strength of the relationship represented by each arc in a network is a fundamental tool to learn and investigate the structure of Bayesian network. Arc strengths are mostly used programmatically for both purposes; nevertheless it is sometimes useful to explore them visually.

We will consider all the different measures of arc strengths implemented in bnlearn:

1. p-values from the conditional independence tests that would remove individual arcs present in a network;
2. ```> dag = model2network("[A][C][F|C:A][B|A][D|A:C][E|B:F]")
> pvalues = arc.strength(dag, data = learning.test, criterion = "x2")
```
3. differences in network score from removing individual arcs present in a network;
4. ```> score.deltas = arc.strength(dag, data = learning.test, criterion = "bic")
```
5. probabilities of inclusion of all possible arcs, and their directions;
6. ```> bagging = boot.strength(learning.test, algorithm = "tabu", R = 200)
```
7. probabilities of inclusion of individual arcs present in a network, and their directions.
8. ```> bayes.factors = bf.strength(dag, data = learning.test)
```
```Loading required namespace: Rmpfr
```

### Plotting the distribution of arc strengths

```> plot(bagging)
``` ### Plotting the network structure together with the arc strengths

```> strength.plot(dag, strength = pvalues)
``` Last updated on `Sun Jun 21 22:32:02 2020` with bnlearn `4.6-20200410` and `R version 4.0.1 (2020-06-06)`.