BranchGLM-package: BranchGLM: Efficient Best Subset Selection for GLMs via...

In BranchGLM: Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms

BranchGLM: Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms

Description

Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.

Author(s)

Maintainer: Jacob Seedorff [e-mail]

References

Seedorff J, Cavanaugh JE. Assessing Variable Importance for Best Subset Selection. Entropy. 2024; 26(9):801. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.3390/e26090801")}

See Also

Useful links:

• https://github.com/JacobSeedorff21/BranchGLM

• Report bugs at https://github.com/JacobSeedorff21/BranchGLM/issues

Examples

# Using iris data to demonstrate package usage
Data <- iris

# Fitting linear regression model
Fit <- BranchGLM(Sepal.Length ~ ., data = Data, family = "gaussian", link = "identity")
Fit

# Doing branch and bound best subset selection 
VS <- VariableSelection(Fit, type = "branch and bound", metric = "BIC", 
showprogress = FALSE, bestmodels = 10)
VS

## Plotting results
plot(VS, ptype = "variables")

BranchGLM documentation built on Sept. 28, 2024, 9:07 a.m.