Summary of AncReg

Summarize the results of AncReg. For models with degree = 0 only the instantaneous graph is returned and for models with degree > 0 the summary graph is returned as well.

Usage

# S3 method for class 'AncReg'
summary(object, alpha = 0.05, verbose = FALSE, corr = TRUE, ...)

Arguments

object

output from AncReg()

alpha

significance level for determin whether a connection is significant

verbose

should information be printed?

corr

should multiplicity correction be applied?

...

Further arguments passed to or from other methods.

Value

A list containing: If degree = 0:

p.val

A numeric matrix of p-values for the instantaneous graph

graph

A boolean matrix indicating whether one variable affects another instantaneously

alpha

The significance level to avoid cycles

If degree > 0:

inst.p.val

A numeric matrix of p-values for the instantaneous graph

inst.graph

A boolean matrix indicating whether one variable affects another instantaneously

inst.alpha

The significance level to avoid cycles

sum.p.val

A numeric matrix of p-values for the summary graph

sum.graph

A boolean matrix indicating whether one variable affects another

See also

AncReg, instant_graph, summary_graph, instant_p.val, summary_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)
# collect ancestral p-values and graph
res <- summary(fit, alpha = 1)
res
#> $p.val
#>           A           B         C         D         E
#> A 1.0000000 0.355083093 0.0493693 0.5107251 0.9777551
#> B 0.6612617 1.000000000 0.5350391 0.1672114 0.5645382
#> C 0.3190314 0.224148437 1.0000000 0.9467940 0.9968308
#> D 0.8740048 0.006213275 0.3530013 1.0000000 0.2938822
#> E 0.4328651 0.798593250 0.4182837 0.1692897 1.0000000
#> 
#> $graph
#>       A     B     C     D     E
#> A FALSE FALSE  TRUE FALSE FALSE
#> B FALSE FALSE FALSE FALSE FALSE
#> C FALSE FALSE FALSE FALSE FALSE
#> D FALSE  TRUE FALSE FALSE FALSE
#> E FALSE FALSE FALSE FALSE FALSE
#> 
#> $alpha
#> [1] 1
#> 
#> attr(,"class")
#> [1] "summary.AncReg"