make_data_single function

Generate full dataset

make_data_single(
  model,
  n = 1,
  parameters = NULL,
  param_type = NULL,
  given = NULL,
  w = NULL,
  P = NULL,
  A = NULL
)

Arguments

• model: A causal_model. A model object generated by make_model.
• n: An integer. Number of observations.
• parameters: A numeric vector. Values of parameters may be specified. By default, parameters is drawn from priors.
• param_type: A character. String specifying type of parameters to make ("flat", "prior_mean", "posterior_mean", "prior_draw", "posterior_draw", "define). With param_type set to define use arguments to be passed to make_priors; otherwise flat sets equal probabilities on each nodal type in each parameter set; prior_mean, prior_draw, posterior_mean, posterior_draw take parameters as the means or as draws from the prior or posterior.
• given: A string specifying known values on nodes, e.g. "X==1 & Y==1"
• w: Vector of event probabilities can be provided directly. This is useful for speed for repeated data draws.
• P: A matrix. Parameter matrix that can be used to generate w if w is not provided
• A: A matrix. Ambiguity matrix that can be used to generate w if w is not provided

Returns

A data.frame of simulated data.

Examples

model <- make_model("X -> Y")

# Simplest behavior uses by default the parameter vector contained in model
CausalQueries:::make_data_single(model, n = 5)

CausalQueries:::make_data_single(model, n = 5, param_type = "prior_draw")

# Simulate multiple datasets. This is fastest if
# event probabilities (w) are  provided
w <- get_event_probabilities(model)
replicate(5, CausalQueries:::make_data_single(model, n = 5, w = w))

See Also

Other data_generation: data_helpers, get_all_data_types(), observe_data()