simulateInterventions {backShift} | R Documentation |
Simulate data of a causal cyclic model under shift interventions.
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