pcalg: checkTriple – R documentation

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checkTriple

Check Consistency of Conditional Independence for a Triple of Nodes

Description

For each subset of nbrsA and nbrsC where a and c are conditionally independent, it is checked if b is in the conditioning set.

Usage

checkTriple(a, b, c, nbrsA, nbrsC,
            sepsetA, sepsetC,
            suffStat, indepTest, alpha, version.unf = c(NA, NA),
            maj.rule = FALSE, verbose = FALSE)

Arguments

`a, b, c`	(integer) positions in adjacency matrix for nodes a, b, and c, respectively.
`nbrsA, nbrsC`	(integer) position in adjacency matrix for neighbors of a and c, respectively.
`sepsetA`	vector containing Sepset(a,c).
`sepsetC`	vector containing Sepset(c,a).
`suffStat`	a `list` of sufficient statistics for independent tests; see, e.g., `pc`.
`indepTest`	a `function` for the independence test, see, e.g., `pc`.
`alpha`	significance level of test.
`version.unf`	(integer) vector of length two: `version.unf[1]`: 1 - check for all separating subsets of `nbrsA` and `nbrsC` if `b` is in that set, 2 - it also checks if there at all exists any sepset which is a subset of the neighbours (there might be none, although `b` is in the sepset, which indicates an ambiguous situation); `version.unf[2]`: 1 - do not consider the initial sepsets `sepsetA` and `sepsetC` (same as Tetrad), 2 - consider if `b` is in `sepsetA` or `sepsetC`.
`maj.rule`	logical indicating that the following majority rule is applied: if `b` is in less than 50% of the checked sepsets, we say that `b` is in no sepset. If `b` is in more than 50% of the checked sepsets, we say that `b` is in all sepsets. If `b` is in exactly 50% of the checked sepsets, the triple is considered ‘ambiguous’.
`verbose`	Logical asking for detailed output of intermediate steps.

Details

This function is used in the conservative versions of structure learning algorithms.

Value

`decision`	Decision on possibly ambiguous triple, an integer code, 1 `b` is in NO sepset (make v-structure); 2 `b` is in ALL sepsets (make no v-structure); 3 `b` is in SOME but not all sepsets (ambiguous triple)
`vers`	Version (1 or 2) of the ambiguous triple (1=normal ambiguous triple that is `b` is in some sepsets; 2=triple coming from `version.unf[1]==2`, that is, `a` and `c` are indep given the initial sepset but there doesn't exist a subset of the neighbours that d-separates them.)
`sepsetA`	Updated version of `sepsetA`
`sepsetC`	Updated version of `sepsetC`

Author(s)

Markus Kalisch (kalisch@stat.math.ethz.ch) and Diego Colombo.

References

D. Colombo and M.H. Maathuis (2014).Order-independent constraint-based causal structure learning. Journal of Machine Learning Research 15 3741-3782.

Examples

##################################################
## Using Gaussian Data
##################################################
## Load predefined data
data(gmG)
n <- nrow    (gmG8$x)
V <- colnames(gmG8$x)


## define independence test (partial correlations), and test level
indepTest <- gaussCItest
alpha <- 0.01
## define sufficient statistics
suffStat <- list(C = cor(gmG8$x), n = n)

## estimate CPDAG
pc.fit <- pc(suffStat, indepTest, alpha=alpha, labels = V, verbose = TRUE)

if (require(Rgraphviz)) {
  ## show estimated CPDAG
  par(mfrow=c(1,2))
  plot(pc.fit, main = "Estimated CPDAG")
  plot(gmG8$g, main = "True DAG")
}

a <- 6
b <- 1
c <- 8
checkTriple(a, b, c,
            nbrsA = c(1,5,7),
            nbrsC = c(1,5),
            sepsetA = pc.fit@sepset[[a]][[c]],
            sepsetC = pc.fit@sepset[[c]][[a]],
            suffStat=suffStat, indepTest=indepTest, alpha=alpha,
            version.unf = c(2,2),
            verbose = TRUE) -> ct
str(ct)
## List of 4
## $ decision: int 2
## $ version : int 1
## $ SepsetA : int [1:2] 1 5
## $ SepsetC : int 1

checkTriple(a, b, c,
            nbrsA = c(1,5,7),
            nbrsC = c(1,5),
            sepsetA = pc.fit@sepset[[a]][[c]],
            sepsetC = pc.fit@sepset[[c]][[a]],
            version.unf = c(1,1),
            suffStat=suffStat, indepTest=indepTest, alpha=alpha) -> c2
stopifnot(identical(ct, c2)) ## in this case,  'version.unf' had no effect

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