pcalg: rmvnorm.ivent – R documentation

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rmvnorm.ivent

Simulate from a Gaussian Causal Model

Description

Produces one or more samples from the observational or an interventional distribution associated to a Gaussian causal model.

Usage

rmvnorm.ivent(n, object, target = integer(0), target.value = numeric(0))

Arguments

`n`	Number of samples required.
`object`	An instance of `GaussParDAG`
`target`	Intervention target: vector of intervened nodes. If the vector is empty, samples from the observational distribution are generated. Otherwise, samples from an interventional distribution are simulated.
`target.value`	Values of the intervened variables. If `target.value` is a vector of the same length as `target`, the indicated intervention levels are used for all `n` samples. If `target.value` is a matrix of dimension `n` by `length(target)`, the i-th sample is simulated using the i-th row of the matrix as intervention levels.

Value

If n = 1 a vector of length p is returned, where p denotes the number of nodes of object. Otherwise an n by p matrix is returned with one sample per row.

Author(s)

Alain Hauser (alain.hauser@bfh.ch)

Examples

set.seed(307)
myDAG <- r.gauss.pardag(5, 0.5)
var(rmvnorm.ivent(n = 1000, myDAG))
myDAG$cov.mat()
var(rmvnorm.ivent(n = 1000, myDAG, target = 1, target.value = 1))
myDAG$cov.mat(target = 1, ivent.var = 0)

pcalg

Methods for Graphical Models and Causal Inference

v2.7-2

GPL (>= 2)

Authors

Markus Kalisch [aut, cre], Alain Hauser [aut], Martin Maechler [aut], Diego Colombo [ctb], Doris Entner [ctb], Patrik Hoyer [ctb], Antti Hyttinen [ctb], Jonas Peters [ctb], Nicoletta Andri [ctb], Emilija Perkovic [ctb], Preetam Nandy [ctb], Philipp Ruetimann [ctb], Daniel Stekhoven [ctb], Manuel Schuerch [ctb], Marco Eigenmann [ctb], Leonard Henckel [ctb], Joris Mooij [ctb]

Initial release

2021-4-20