corgraph: Graph of unconditional associations

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

Graph of unconditional associations

Description

Calcualtes the graph of unconditional associations. If the correlation (Pearson, Spearman) or the G^2 test of independence, between pairs of continuous or categorical variables respectively is not statistically significant, there is no edge between the two respective nodes.

Usage

corgraph(dataset, test = "testIndFisher", threshold = 0.01)

Arguments

dataset	A matrix with the variables. The user must know if they are continuous or if they are categorical. If you have a matrix with categorical data, i.e. 0, 1, 2, 3 where each number indicates a category, the minimum number for each variable must be 0.
test	The conditional independence test to use. Default value is "testIndFisher". This procedure allows for "testIndFisher", "testIndSPearman" for continuous variables and "gSquare" for categorical variables.
threshold	Threshold ( suitable values in (0, 1) ) for assessing p-values significance. Default value is 0.05.

Value

A list including:

runtime	The run time of the algorithm. A numeric vector. The first element is the user time, the second element is the system time and the third element is the elapsed time.
stat	A matrix with the test statistics.
pvalue	A matrix with the p-values.
G	The adjancency matrix. A value of 1 in G[i, j] appears in G[j, i] also, indicating that i and j have an edge between them.

Author(s)

Michail Tsagris

R implementation and documentation: Giorgos Athineou <[e-mail]> and Michail Tsagris [e-mail]

See Also

pc.skel, mmhc.skel, corfs.network, bn.skel.utils

Examples

# simulate a dataset with continuous data
y <- rdag2(500, p = 20, nei = 3)
x <- y$x
a <- mmhc.skel(x, max_k = 5, threshold = 0.01, test = "testIndFisher" ) 
b <- pc.skel( x, alpha = 0.01 ) 
d <- corgraph(x, test = "testIndFisher", threshold = 0.01) 

MXM documentation built on Aug. 25, 2022, 9:05 a.m.