instant_graph: Instantaneous graph

In AncReg: Ancestor Regression

Instantaneous graph

Description

Construct instantaneous graph from p-values and significance level. Recursively constructs all ancestral connections by adding ancestors of ancestors.

Usage

instant_graph(lin.anc, alpha = 0.05, verbose = FALSE, corr = TRUE)

Arguments

lin.anc	output from AncReg()
alpha	significance level
verbose	should information be printed?
corr	should multiplicity correction be applied?

Value

A list containing:

rec.ancs	A boolean matrix indicating whether one variable affects another instantaneously
alpha	The significance level to avoid cycles

See Also

AncReg, instant_p.val

Examples

# random DAGS for simulation
set.seed(1234)

p <- 5 #number of nodes
DAG <- pcalg::randomDAG(p, prob = 0.5)

B <- matrix(0, p, p) # represent DAG as matrix
for (i in 2:p){
  for(j in 1:(i-1)){
    # store edge weights
    B[i,j] <- max(0, DAG@edgeData@data[[paste(j,"|",i, sep="")]]$weight)
  }
}
colnames(B) <- rownames(B) <- LETTERS[1:p]

# solution in terms of noise
Bprime <- MASS::ginv(diag(p) - B)

n <- 500
N <- matrix(rexp(n * p), ncol = p)
X <- t(Bprime %*% t(N))
colnames(X) <- LETTERS[1:p]

# fit ancestor regression
fit <- AncReg(X)

# generate instantaneous graph
instant_graph(fit, alpha = 0.01, verbose = TRUE)

AncReg documentation built on Aug. 8, 2025, 7:48 p.m.