simulateInterventions: Simulate data of a causal cyclic model under shift...

Description Usage Arguments Value References

View source: R/simulate.R

Description

Simulate data of a causal cyclic model under shift interventions.

Usage

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simulateInterventions(n, p, A, G, intervMultiplier, noiseMult, nonGauss,
  hiddenVars, knownInterventions, fracVarInt, simulateObs, seed = 1)

Arguments

n

Number of observations.

p

Number of variables.

A

Connectivity matrix A. The entry A_{ij} contains the edge from node i to node j.

G

Number of environments, has to be larger than two for backShift.

intervMultiplier

Regulates the strength of the interventions.

noiseMult

Regulates the noise variance.

nonGauss

Set to TRUE to generate non-Gaussian noise.

hiddenVars

Set to TRUE to include hidden variables.

knownInterventions

Set to TRUE if location of interventions should be known.

fracVarInt

If knownInterventions is TRUE, fraction of variables that are intervened on in each environment.

simulateObs

If TRUE, also generate observational data.

seed

Random seed.

Value

A list with the following elements:

References

Dominik Rothenhaeusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen (2015): backShift: Learning causal cyclic graphs from unknown shift interventions. arXiv preprint: http://arxiv.org/abs/1506.02494


backShift documentation built on May 1, 2019, 9:25 p.m.