Estimate the undirected graph of the metabolic data.

Uses the PC-algorithm and is mostly a wrapper around pcalg::skeleton().

pc_estimate_undirected_graph(data, alpha = 0.01)

Arguments

data

Input numeric data that forms the basis of the underlying graph.

alpha

Significance level threshold applied to each test to determine conditional dependence for if an edge exists.

Value

A pcAlgo object that contains the DAG skeleton, aka undirected graph.

Details

This function estimates the "skeleton of a DAG", meaning a graph without arrowheads, aka an undirected graph. The default estimation method used is the "PC-stable" method, which estimates the order-independent skeleton of the DAG, meaning the order of the variables given does not impact the results (older versions of the algorithm were order-dependent). The method also assumes no latent variables.

An edge is determined by testing for conditional dependence between two nodes based on the pcalg::gaussCItest(). Conditional independence exists when the nodes have zero partial correlation determined from a p-value based hypothesis test against the correlation matrix of the data from the nodes. The estimated edges exists between the start and end nodes when the start and end variables are conditionally dependent given the subset of remaining variables.

See also

The help documentation of pcalg::skeleton() has more details.