FastStepGraph {FastStepGraph}

Fast Stepwise Gaussian Graphical Model

Description

Improved and faster implementation of the Stepwise Gaussian Graphical Algorithm.

Usage

FastStepGraph(
  x,
  alpha_f,
  alpha_b = NULL,
  nei.max = 5,
  data_scale = FALSE,
  max.iterations = NULL
)

Arguments

x

Data matrix (of size n_samples x p_variables).

alpha_f

Forward threshold (no default value).

alpha_b

Backward threshold. If alpha_b=NULL, then the rule alpha_b <- 0.5*alpha_f is applied.

nei.max

Maximum number of variables in every neighborhood (default value 5).

data_scale

Boolean parameter (TRUE or FALSE), when to scale data to zero mean and unit variance (default FALSE).

max.iterations

Maximum number of iterations (integer), the defaults values is set to p*(p-1).

Value

A list with the values:

vareps

Response variables.

beta

Regression coefficients.

Edges

Estimated set of edges.

Omega

Estimated precision matrix.

Author(s)

Prof. Juan G. Colonna, PhD. [e-mail]

Prof. Marcelo Ruiz, PhD. [e-mail]

Examples

data <- FastStepGraph::SigmaAR(30, 50, 0.4) # Simulate Gaussian Data
G <- FastStepGraph::FastStepGraph(data$X, alpha_f = 0.22, alpha_b = 0.14, data_scale=TRUE)