How to Perform Regression Analysis in Julia
Overview
This brief introduction presents the GLM.jl package for conducting regression analysis in Julia, emphasizing its similarity to the interface in R and thus, skipping detailed explanations1.
Code
Julia
using GLM, RDatasets
faithful = dataset("datasets", "faithful")
out1 = lm(@formula(Waiting ~ Eruptions), faithful)
The result of running the above code is as follows:
julia> out1 = lm(@formula(Waiting ~ Eruptions), faithful)
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}
Waiting ~ 1 + Eruptions
Coefficients:
───────────────────────────────────────────────────────────────────────
Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept) 33.4744 1.15487 28.99 <1e-84 31.2007 35.7481
Eruptions 10.7296 0.314753 34.09 <1e-99 10.11 11.3493
───────────────────────────────────────────────────────────────────────
Compare this with the results from regression analysis in R.
Comparison with R
out1<-lm(waiting~eruptions,data=faithful); summary(out1)
out1 = lm(@formula(Waiting ~ Eruptions), faithful)
The above is the code in R, and below is the code in Julia. The @formula macro was used to input variables, almost perfectly replicating the convention in R.
Environment
- OS: Windows
- julia: v1.7.0
- GLM v1.8.0