simDAG (version 0.3.0)

dag2matrix: Obtain a Adjacency Matrix from a DAG object

Description

The sim_from_dag function requires the user to specify the causal relationships inside a DAG object containing node information. This function takes this object as input and outputs the underlying adjacency matrix. This can be useful to plot the theoretical DAG or to check if the nodes have been specified correctly.

Usage

dag2matrix(dag, include_root_nodes=TRUE, include_td_nodes=FALSE)

Value

Returns a numeric square matrix with one row and one column per used node in dag.

Arguments

dag

A DAG object created using the empty_dag function with nodes added to it using the + syntax. See ?empty_dag or ?node for more details. Supports DAGs with time-dependent nodes added using the node_td function. However, including such DAGs may result in cyclic causal structures, because time is not represented in the output matrix.

include_root_nodes

Whether to include root nodes in the output matrix. Should usually be kept at TRUE (default).

include_td_nodes

Whether to include time-dependent nodes added to the dag using the node_td function or not. When including these types of nodes, it is possible for the adjacency matrix to contain cycles, e.g. that it is not a classic DAG anymore, due to the matrix not representing the passage of time.

Author

Robin Denz

Details

An adjacency matrix is simply a square matrix in which each node has one column and one row associated with it. For example, if the node A has a causal effect on node B, the matrix will contain 1 in the spot matrix["A", "B"].

If a time-varying node is also defined as a time-fixed node, the parents of both parts will be pooled when creating the output matrix.

See Also

empty_dag, node, node_td

Examples

library(simDAG)

# some example DAG
dag <- empty_dag() +
  node("death", type="binomial", parents=c("age", "sex"), betas=c(1, 2),
       intercept=-10) +
  node("age", type="rnorm", mean=10, sd=2) +
  node("sex", parents="", type="rbernoulli", p=0.5) +
  node("smoking", parents=c("sex", "age"), type="binomial",
       betas=c(0.6, 0.2), intercept=-2)

# get adjacency matrix
dag2matrix(dag)

# get adjacency matrix using only the child nodes
dag2matrix(dag, include_root_nodes=FALSE)

## adding time-varying nodes
dag <- dag +
  node_td("disease", type="time_to_event", parents=c("age", "smoking"),
          prob_fun=0.01) +
  node_td("cve", type="time_to_event", parents=c("age", "sex", "smoking",
                                                 "disease"),
          prob_fun=0.001, event_duration=Inf)

# get adjacency matrix including all nodes
dag2matrix(dag, include_td_nodes=TRUE)

# get adjacency matrix including only time-constant nodes
dag2matrix(dag, include_td_nodes=FALSE)

# get adjacency matrix using only the child nodes
dag2matrix(dag, include_root_nodes=FALSE)