| glarma {Glarmadillo} | R Documentation |
Solve Graphical Lasso with Armadillo
Description
This function solves the Graphical Lasso (GLasso) problem using the Armadillo library. GLasso is a technique used in statistical learning and network analysis to estimate sparse inverse covariance matrices from observed data.
Usage
glarma(s, rho, mtol = 1e-04, maxIterations = 10000, ltol = 1e-06)
Arguments
s |
A symmetric, positive-definite sample covariance matrix. It should be a square matrix representing the covariance matrix of the variables. |
rho |
A positive scalar representing the regularization parameter. It controls the sparsity level of the inverse covariance matrix. |
mtol |
A numeric value representing the convergence threshold for the main algorithm. It determines the condition under which the iterative process will stop. Default is 1e-4. |
maxIterations |
An integer value specifying the maximum number of iterations allowed for the algorithm. Default is 10000. |
ltol |
A numeric value representing the convergence threshold for the Lasso solver. It is used to control the Lasso solving process within the algorithm. Default is 1e-6. |
Value
Returns a covariance matrix W and a estimated sparse inverse covariance matrix Theta estimated by solving the Graphical Lasso problem. The sparsity is controlled by the 'rho' parameter.
Examples
# Generate a sample covariance matrix
s <- matrix(runif(100), nrow = 10)
s <- t(s) %*% s
# Solve the Graphical Lasso problem with default parameters
inv_cov_matrix <- glarma(s, rho = 0.1)
# Solve with custom convergence thresholds and maximum iterations
inv_cov_matrix <- glarma(s, rho = 0.1, mtol = 1e-5, maxIterations = 5000, ltol = 1e-6)