summary_graph {AncReg} | R Documentation |
Summary graph
Description
Construct summary graph from p-values and significance level. Recursively constructs all ancestral connections by adding ancestors of ancestors.
Usage
summary_graph(lin.anc, alpha = 0.05, corr = TRUE)
Arguments
lin.anc |
output from AncReg() |
alpha |
significance level |
corr |
should multiplicity correction be applied? |
Value
A boolean matrix indicating whether one variable affects another
See Also
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 summary graph
summary_graph(fit, alpha = 0.1)
[Package AncReg version 1.0.1 Index]