pcalg (version 2.7-12)

corGraph: Computing the correlation graph

Description

Computes the correlation graph. This is the graph in which an edge is drawn between node i and node j, if the null hypothesis “Correlation between \(X_i\) and \(X_j\) is zero” can be rejected at the given significance level \(\alpha (alpha)\).

Usage

corGraph(dm, alpha=0.05, Cmethod="pearson")

Value

Undirected correlation graph, a graph-class object (package graph); getGraph for the “fitted”

graph.

Arguments

dm

numeric matrix with rows as samples and columns as variables.

alpha

significance level for correlation test (numeric)

Cmethod

a character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.

Author

Markus Kalisch () and Martin Maechler

Examples

## create correlated samples
x1 <- rnorm(100)
x2 <- rnorm(100)
mat <- cbind(x1,x2, x3 = x1+x2)

if (require(Rgraphviz)) {
## ``analyze the data''
(g <- corGraph(mat)) # a 'graphNEL' graph, undirected
plot(g) # ==> (1) and (2) are each linked to (3)

## use different significance level and different method
(g2 <- corGraph(mat, alpha=0.01, Cmethod="kendall"))
plot(g2) ## same edges as 'g'
}