pcalg: idaFast – R documentation

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idaFast

Multiset of Possible Total Causal Effects for Several Target Var.s

Description

This function estimates the multiset of possible total causal effects of one variable (x) on a several (i.e., a vector of) target variables (y) from observational data.

idaFast() is more efficient than looping over ida. Only method="local" (see ida) is available.

Usage

idaFast(x.pos, y.pos.set, mcov, graphEst)

Arguments

`x.pos`	(integer) position of variable `x` in the covariance matrix.
`y.pos.set`	integer vector of positions of the target variables `y` in the covariance matrix.
`mcov`	covariance matrix that was used to estimate `graphEst`
`graphEst`	estimated CPDAG from the function `pc`. If the output of `pc` is `pc.fit`, then the estimated CPDAG can be obtained by `pc.fit@graph`.

Details

This function performs ida(x.pos, y.pos, mcov, graphEst, method="local", y.notparent=FALSE, verbose=FALSE) for all values of y.pos in y.pos.set simultaneously, in an efficient way. See (the help about) ida for more details. Note that the option y.notparent = TRUE is not implemented, since it is not clear how to do that efficiently without orienting all edges away from y.pos.set at the same time, which seems not to be desirable. Suggestions are welcome.

Value

Matrix with length(y.pos.set) rows. Row i contains the multiset of estimated possible total causal effects of x on y.pos.set[i]. Note that all multisets in the matrix have the same length, since the parents of x are the same for all elements of y.pos.set.

Author(s)

Markus Kalisch (kalisch@stat.math.ethz.ch)

References

see the list in ida.

Examples

## Simulate the true DAG
set.seed(123)
p <- 7
myDAG <- randomDAG(p, prob = 0.2) ## true DAG
myCPDAG <- dag2cpdag(myDAG) ## true CPDAG
covTrue <- trueCov(myDAG) ## true covariance matrix

## simulate data from the true DAG
n <- 10000
dat <- rmvDAG(n, myDAG)
cov.d <- cov(dat)

## estimate CPDAG (see help on the function "pc")
suffStat <- list(C = cor(dat), n = n)
pc.fit <- pc(suffStat, indepTest = gaussCItest, alpha = 0.01, p=p)

if(require(Rgraphviz)) {
  op <- par(mfrow=c(1,3))
  plot(myDAG,        main="true DAG")
  plot(myCPDAG,      main="true CPDAG")
  plot(pc.fit@graph, main="pc()-estimated CPDAG")
  par(op)
}

(eff.est1 <- ida(2,5, cov.d, pc.fit@graph))## method = "local" is default
(eff.est2 <- ida(2,6, cov.d, pc.fit@graph))
(eff.est3 <- ida(2,7, cov.d, pc.fit@graph))
## These three computations can be combinded in an efficient way
## by using idaFast :
(eff.estF <- idaFast(2, c(5,6,7), cov.d, pc.fit@graph))

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