simGausFromDAG: Simulate Gaussian data according to DAG

In causalDisco: Tools for Causal Discovery on Observational Data

Simulate Gaussian data according to DAG

Description

Simulates a jointly Gaussian dataset given a DAG adjacency matrix ("from-to" encoding, see amat for details). The data is simulated using linear structural equations and the parameters (residual standard deviations and regression coefficients) are sampled from chosen intervals.

Usage

simGausFromDAG(
  amat,
  n,
  regparLim = c(0.5, 2),
  resSDLim = c(0.1, 1),
  pnegRegpar = 0.4,
  standardize = FALSE
)

Arguments

amat	An adjacency matrix.
n	The number of observations that should be simulated.
regparLim	The interval from which regression parameters are sampled.
resSDLim	The interval from which residual standard deviations are sampled.
pnegRegpar	The probability of sampling a negative regression parameter.
standardize	If FALSE (the default), the raw data are returned. If TRUE, the data are first standardized, i.e., each variable will have its mean subtracted and be divided by its standard deviation.

Details

A variable X_{i} is simulated as
X_{i} := \sum_{Z \in pa(X_{i})} \beta_{Z} * Z + e_{i}
where pa(X_{i}) are the parents of X_{i} in the DAG. The residual, e_{i}, is drawn from a normal distribution.

Value

A data.frame of identically distributed simulated observations.

Examples

# Simulate DAG adjacency matrix with 6 nodes
ex_dag <- simDAG(6) 

# Simulate Gaussian data (100 iid observations) 
ex_data <- simGausFromDAG(ex_dag, n = 100)

causalDisco documentation built on Jan. 20, 2026, 5:09 p.m.