idaFast: Multiset of Possible Total Causal Effects for Several Target...

In pcalg: Methods for Graphical Models and Causal Inference

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 ([e-mail])

References

see the list in ida.

See Also

pc for estimating a CPDAG, and ida for estimating the multiset of possible total causal effects from observational data on only one target variable but with many more options (than here in idaFast).

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 documentation built on May 29, 2024, 5:24 a.m.