dag_network: Directed acyclic graphs (DAGs) via Bayesian networks (BNs).

In DPI: The Directed Prediction Index

Directed acyclic graphs (DAGs) via Bayesian networks (BNs).

Description

Directed acyclic graphs (DAGs) via Bayesian networks (BNs). It uses bnlearn::boot.strength() to estimate the strength of each edge as its empirical frequency over a set of networks learned from bootstrap samples. It computes (1) the probability of each edge (modulo its direction) and (2) the probabilities of each edge's directions conditional on the edge being present in the graph (in either direction). Stability thresholds are usually set as 0.85 for strength (i.e., an edge appearing in more than 85% of BNs bootstrap samples) and 0.50 for direction (i.e., a direction appearing in more than 50% of BNs bootstrap samples) (Briganti et al., 2023). Finally, for each chosen algorithm, it returns the stable Bayesian network as the final DAG.

Usage

dag_network(
  data,
  algorithm = c("pc.stable", "hc", "rsmax2"),
  algorithm.args = list(),
  n.boot = 1000,
  seed = NULL,
  strength = 0.85,
  direction = 0.5,
  node.text.size = 1.2,
  edge.width.max = 1.5,
  edge.label.mrg = 0.01,
  file = NULL,
  width = 6,
  height = 4,
  dpi = 500,
  ...
)

Arguments

data	Data.
algorithm	Structure learning algorithms for building Bayesian networks (BNs). Should be function name(s) from the bnlearn package. Better to perform BNs with all three classes of algorithms to check the robustness of results (Briganti et al., 2023). Defaults to the most common algorithms: "pc.stable" (PC), "hc" (HC), and "rsmax2" (RS), for the three classes, respectively. (1) Constraint-based Algorithms PC: "pc.stable" (the first practical constraint-based causal structure learning algorithm by Peter & Clark) Others: "gs", "iamb", "fast.iamb", "inter.iamb", "iamb.fdr" (2) Score-based Algorithms Hill-Climbing: "hc" (the hill-climbing greedy search algorithm, exploring DAGs by single-edge additions, removals, and reversals, with random restarts to avoid local optima) Others: "tabu" (3) Hybrid Algorithms (combination of constraint-based and score-based algorithms) Restricted Maximization: "rsmax2" (the general 2-phase restricted maximization algorithm, first restricting the search space and then finding the optimal [maximizing the score of] network structure in the restricted space) Others: "mmhc", "h2pc"
algorithm.args	An optional list of extra arguments passed to the algorithm.
n.boot	Number of bootstrap samples (for learning a more "stable" network structure). Defaults to 1000.
seed	Random seed for replicable results. Defaults to NULL.
strength	Stability threshold of edge strength: the minimum proportion (probability) of BNs (among the n.boot bootstrap samples) in which each edge appears. Defaults to 0.85 (85%). Two reverse directions share the same edge strength. Empirical frequency (?~100%) will be mapped onto edge width/thickness in the final integrated DAG, with wider (thicker) edges showing stronger links, though they usually look similar since the default range has been limited to 0.85~1.
direction	Stability threshold of edge direction: the minimum proportion (probability) of BNs (among the n.boot bootstrap samples) in which a direction of each edge appears. Defaults to 0.50 (50%). The proportions of two reverse directions add up to 100%. Empirical frequency (?~100%) will be mapped onto edge greyscale/transparency in the final integrated DAG, with its value shown as edge text label.
node.text.size	Scalar on the font size of node (variable) labels. Defaults to 1.2.
edge.width.max	Maximum value of edge strength to scale all edge widths. Defaults to 1.5 for better display of arrow.
edge.label.mrg	Margin of the background box around the edge label. Defaults to 0.01.
file	File name of saved plot (".png" or ".pdf").
width, height	Width and height (in inches) of saved plot. Defaults to 6 and 4.
dpi	Dots per inch (figure resolution). Defaults to 500.
...	Arguments passed on to qgraph().

Value

Return a list (class dag.net) of Bayesian network results and qgraph object with its grob (Grid Graphical Object).

References

Briganti, G., Scutari, M., & McNally, R. J. (2023). A tutorial on Bayesian networks for psychopathology researchers. Psychological Methods, 28(4), 947–961. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000479")}

Burger, J., Isvoranu, A.-M., Lunansky, G., Haslbeck, J. M. B., Epskamp, S., Hoekstra, R. H. A., Fried, E. I., Borsboom, D., & Blanken, T. F. (2023). Reporting standards for psychological network analyses in cross-sectional data. Psychological Methods, 28(4), 806–824. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000471")}

Scutari, M., & Denis, J.-B. (2021). Bayesian networks: With examples in R (2nd ed.). Chapman and Hall/CRC. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1201/9780429347436")}

https://www.bnlearn.com/

See Also

S3method.network

cor_network()

Examples

bn = dag_network(airquality, seed=1)
bn
# bn$pc.stable
# bn$hc
# bn$rsmax2

## All DAG objects can be directly plotted
## or saved with print(..., file="xxx.png")
# bn$pc.stable$DAG.edge
# bn$pc.stable$DAG.strength
# bn$pc.stable$DAG.direction
# bn$pc.stable$DAG
# ...

## Not run: 

print(bn, file="airquality.png")
# will save three plots with auto-modified file names:
- "airquality_DAG.NET_BNs.01_pc.stable.png"
- "airquality_DAG.NET_BNs.02_hc.png"
- "airquality_DAG.NET_BNs.03_rsmax2.png"

# arrange multiple plots using cowplot::plot_grid()
# but still with unknown issue on incomplete figure
c1 = cor_network(airquality, "cor")
c2 = cor_network(airquality, "pcor")
bn = dag_network(airquality, seed=1)
plot_grid(
  ~print(c1),
  ~print(c2),
  ~print(bn$hc$DAG),
  ~print(bn$rsmax2$DAG),
  labels="AUTO"
)

## End(Not run)

DPI documentation built on Aug. 21, 2025, 5:42 p.m.