Description Usage Arguments Value References
Simulate data of a causal cyclic model under shift interventions.
1 2  simulateInterventions(n, p, A, G, intervMultiplier, noiseMult, nonGauss,
hiddenVars, knownInterventions, fracVarInt, simulateObs, seed = 1)

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 
intervMultiplier 
Regulates the strength of the interventions. 
noiseMult 
Regulates the noise variance. 
nonGauss 
Set to 
hiddenVars 
Set to 
knownInterventions 
Set to 
fracVarInt 
If 
simulateObs 
If 
seed 
Random seed. 
A list with the following elements:
X
(nxp)dimensional data matrix
environment
Indicator of the experiment or the intervention type an
observation belongs to. A numeric vector of length n.
interventionVar
(Gxp)dimensional matrix with intervention variances.
interventions
Location of interventions if knownInterventions
was set to TRUE
.
configs
A list with the following elements:
trueA
True connectivity matrix used to generate the data.
G
Number of environments.
indexObservationalData
Index of observational data
intervMultiplier
Multiplier steering the intervention strength
noiseMult
Multiplier steering the noise level
fracVarInt
If knownInterventions
was set to TRUE
,
fraction of variables that were intervened on in each environment.
hiddenVars
If TRUE
, hidden variables exist.
knownInterventions
If TRUE
, location of interventions is known.
simulateObs
If TRUE
, environment 1
contains
observational data.
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
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