A julia package to implement model selection algorithms on basic regression models.
You can install BestModelSubset.jl using Julia's package manager
julia> using Pkg; Pkg.add(url="https://github.com/waitasecant/BestModelSubset.jl.git")Instantiate a ModelSelection object
# To execute forward step-wise selection with primary parameter to be R-squared score
# and secondary parameter to be aic.
julia> obj = ModelSelection("forward", "r2", "aic")
ModelSelection(BestModelSubset.forward_stepwise, nothing, StatsAPI.r2, nothing,
StatsAPI.aic, nothing, StatsAPI.r2, StatsAPI.aic)Fit the ModelSelectionobject to the data
# The fit! function updates the fields of the `ModelSelection` object.
julia> Random.seed!(123); df = hcat(rand(Float64, (50, 21))); # 50*21 Matrix
julia> fit!(obj, df)
1-element Vector{Vector{Int64}}:
[4, 5, 6, 16, 17, 18, 20]Access various statistics like r2, adjr2, aic and bic for the selected model
julia> obj.r2
0.8174272341858757
julia> obj.aic
22.84823396671912Instantiate a ModelSelection object
# To execute best subset selection with primary parameter to be deviance
# and secondary parameter to be bic.
julia> obj = ModelSelection("best", "deviance", "bic")
ModelSelection(BestModelSubset.best_subset, StatsAPI.deviance, nothing, nothing,
nothing, StatsAPI.bic, StatsAPI.deviance, StatsAPI.bic)Fit the ModelSelectionobject to the data
# The fit! function updates the fields of the `ModelSelection` object.
julia> Random.seed!(123); df = hcat(rand(Float64, (50, 20)), rand([0, 1], (50, 1))); # 50*21 Matrix
julia> fit!(obj, df)
1-element Vector{Vector{Int64}}:
[8, 13]Access various statistics like r2, adjr2, aic and bic for the selected model
julia> obj.deviance
61.39326332090434
julia> obj.bic
69.2173093317606