Obtain a DAG object from a Adjacency Matrix and a List of Node Types

The sim_from_dag function requires the user to specify the causal relationships inside a DAG object containing node information. This function creates such an object using a adjacency matrix and a list of node types. The resulting DAG will be only partially specified, which may be useful for the dag_from_data function.

Usage

matrix2dag(mat, type)

Arguments

mat: A p x p adjacency matrix where p is the number of variables. The matrix should be filled with zeros. Only places where the variable specified by the row has a direct causal effect on the variable specified by the column should be 1. Both the columns and the rows should be named with the corresponding variable names.
type: A named list with one entry for each variable in mat, specifying the type of the corresponding node. See node for available node types.

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"]. This function uses this kind of matrix and additional information about the node type to create a DAG object. The resulting DAG cannot be used in the sim_from_dag function directly, because it will not contain the necessary parameters such as beta-coefficients or intercepts etc. It may, however, be passed directly to the dag_from_data function. This is pretty much it's only valid use-case. If the goal is to to specify a full DAG manually, the user should use the empty_dag function in conjunction with node calls instead, as described in the respective documentation pages and the vignettes.

The output will never contain time-dependent nodes. If this is necessary, the user needs to manually define the DAG.

Author

Robin Denz

Value

Returns a partially specified DAG object.

Examples

library(simDAG)

# simple example adjacency matrix
mat <- matrix(c(0, 0, 1, 0, 0, 1, 0, 0, 0), ncol=3, byrow=TRUE)
colnames(mat) <- c("age", "sex", "death")
rownames(mat) <- c("age", "sex", "death")

type <- list(age="rnorm", sex="rbernoulli", death="binomial")

matrix2dag(mat=mat, type=type)
#> A DAG object with:
#>   -  3  nodes in total
#>   -  2  of which are root nodes
#>   -  1  of which are child nodes
#>   -  0  of which are time-varying nodes

Obtain a `DAG` object from a Adjacency Matrix and a List of Node Types