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Simulate data from a caugi DAG.

Source: R/simulation.R
simulate_data.Rd

Simulate data from a caugi object of class DAG using a linear structural equation model (SEM). As standard, the data is simulated from a DAG, where each node is generated as a linear combination of its parents plus Gaussian noise, following the topological order of the graph. Nodes without custom equations are simulated using auto-generated linear Gaussian relationships.

Usage

simulate_data(
  cg,
  n,
  ...,
  standardize = TRUE,
  coef_range = c(0.1, 0.9),
  error_sd = 1,
  seed = NULL
)

Arguments

cg

A caugi object of class DAG.

n

Integer; number of observations to simulate.

...

Named expressions for custom structural equations. Names must match node names in the graph. Expressions can reference parent node names and the variable n (sample size). Nodes without custom equations use auto-generated linear Gaussian relationships.

standardize

Logical; if TRUE, standardize all variables to have mean 0 and standard deviation 1. Default is TRUE.

coef_range

Numeric vector of length 2; range for random edge coefficients that will be sampled uniformly. Default is c(0.1, 0.9).

error_sd

Numeric; standard deviation for error terms in auto-generated equations. Default is 1.

seed

Optional integer; random seed for reproducibility.

Value

A data.frame with n rows and one column per node, ordered according to the node order in the graph.

See also

Other simulation functions: generate_graph()

Examples

cg <- caugi(A %-->% B, B %-->% C, A %-->% C, class = "DAG")

# Fully automatic simulation
df <- simulate_data(cg, n = 100)

# With standardization
df <- simulate_data(cg, n = 100, standardize = TRUE)

# Custom equations for some nodes
df <- simulate_data(cg, n = 100,
  A = rnorm(n, mean = 10, sd = 2),
  B = 0.5 * A + rnorm(n, sd = 0.5)
)

# Reproducible simulation
df <- simulate_data(cg, n = 100, seed = 42)