pcalg (version 2.6-8)

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.

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

Details

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

References

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

Examples

# NOT RUN {
##################################################
## 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
# }
# NOT RUN {
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
# }