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use released GLM in docs now #288

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Apr 4, 2023
Merged

use released GLM in docs now #288

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3b95829
can use released glm now
kleinschmidt Apr 4, 2023
a52e3ac
it burns, my eyes
kleinschmidt Apr 4, 2023
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1 change: 0 additions & 1 deletion .github/workflows/docs.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@ jobs:
- uses: julia-actions/setup-julia@v1
with:
version: '1.8'
- run: julia --project=docs -e 'using Pkg; pkg"add GLM#dfk/statsmodels-7"'
- uses: julia-actions/julia-buildpkg@latest
- uses: julia-actions/julia-docdeploy@latest
env:
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8 changes: 4 additions & 4 deletions docs/src/formula.md
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Expand Up @@ -220,7 +220,7 @@ julia> using GLM


julia> lm(@formula(log(y) ~ 1 + a + b), df)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

:(log(y)) ~ 1 + a + b

Expand All @@ -236,7 +236,7 @@ b -1.63199 1.12678 -1.45 0.1977 -4.38911 1.12513
julia> df.log_y = log.(df.y);

julia> lm(@formula(log_y ~ 1 + a + b), df) # equivalent
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

log_y ~ 1 + a + b

Expand Down Expand Up @@ -368,7 +368,7 @@ julia> response(f, df)
-2.980055366491228

julia> lm(f, df)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

:(log(y)) ~ 1 + a + b

Expand Down Expand Up @@ -412,7 +412,7 @@ julia> ϵ = randn(rng, 100)*0.1;
julia> data.y = X*β_true .+ ϵ;

julia> mod = fit(LinearModel, @formula(y ~ 1 + a*b), data)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + a + b + a & b

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8 changes: 4 additions & 4 deletions docs/src/internals.md
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Expand Up @@ -513,7 +513,7 @@ julia> sim_dat = DataFrame(a=rand(rng, 100).-0.5, b=randn(rng, 100).-0.5);
julia> sim_dat.y = randn(rng, 100) .+ 1 .+ 2*sim_dat.a .+ 3*sim_dat.b.^2;

julia> fit(LinearModel, @formula(y ~ 1 + poly(a,2) + poly(b,2)), sim_dat)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + poly(a, 2) + poly(b, 2)

Expand Down Expand Up @@ -622,7 +622,7 @@ Predictors:
poly(b, 2)

julia> fit(LinearModel, poly_formula, sim_dat)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + poly(a, 2) + poly(b, 2)

Expand Down Expand Up @@ -729,7 +729,7 @@ julia> sim_dat = DataFrame(b=randn(rng, 100));
julia> sim_dat.y = randn(rng, 100) .+ 1 .+ 2*sim_dat.b .+ 3*sim_dat.b.^2;

julia> fit(LinearModel, @formula(y ~ 1 + poly(b,2)), sim_dat)
LinearModel
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + :(poly(b, 2))

Expand All @@ -742,7 +742,7 @@ poly(b, 2) 2.95861 0.174347 16.97 <1e-30 2.61262 3.30459
───────────────────────────────────────────────────────────────────────

julia> fit(GeneralizedLinearModel, @formula(y ~ 1 + poly(b,2)), sim_dat, Normal())
GeneralizedLinearModel
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Normal{Float64}, IdentityLink}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + poly(b, 2)

Expand Down