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Get rid of TableRegressionModel by storing formula directly in model #339

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Nov 20, 2022
Merged

Get rid of TableRegressionModel by storing formula directly in model #339

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5df0fb5
Get rid of TableRegressionModel by storing formula directly in model
nalimilan Oct 3, 2019
d3f9430
Cleanup
nalimilan Jun 19, 2022
b670a14
Reuse existing test instead of adding another one
nalimilan Jun 19, 2022
a83cb1b
Tables.jl fixes
nalimilan Jun 19, 2022
bd00e51
Fixes
nalimilan Jun 19, 2022
c244147
Docs, etc.
nalimilan Jun 19, 2022
fc0704a
Docs, contrasts
nalimilan Jun 19, 2022
399979f
Review fixes, more tests
nalimilan Sep 11, 2022
8365831
Merge remote-tracking branch 'origin/master' into nl/tableregressionm…
nalimilan Sep 11, 2022
1a012f1
Fix doctests on Julia 1.8
nalimilan Sep 11, 2022
f9f3007
Use ≈
nalimilan Sep 11, 2022
93b3d97
Review fixes
nalimilan Oct 8, 2022
fa2e5e8
Merge remote-tracking branch 'origin/master' into nl/tableregressionm…
nalimilan Oct 8, 2022
5fb340d
More fixes
nalimilan Oct 21, 2022
1925039
Update test/runtests.jl
nalimilan Oct 21, 2022
21a047c
Merge remote-tracking branch 'origin/master' into nl/tableregressionm…
nalimilan Nov 20, 2022
c262432
Merge remote-tracking branch 'origin/nl/tableregressionmodel' into nl…
nalimilan Nov 20, 2022
5c6bf20
Add deprecation to access formula
nalimilan Nov 20, 2022
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2 changes: 2 additions & 0 deletions Project.toml
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Expand Up @@ -14,6 +14,7 @@ StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

[compat]
CSV = "0.7, 0.8"
Expand All @@ -27,6 +28,7 @@ StatsAPI = "1.4"
StatsBase = "0.33.5"
StatsFuns = "0.6, 0.7, 0.8, 0.9, 1.0"
StatsModels = "0.6.23"
Tables = "1"
julia = "1"

[extras]
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4 changes: 2 additions & 2 deletions docs/src/api.md
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Expand Up @@ -25,7 +25,7 @@ The most general approach to fitting a model is with the `fit` function, as in
julia> using Random

julia> fit(LinearModel, hcat(ones(10), 1:10), randn(MersenneTwister(12321), 10))
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}:
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────
Expand All @@ -41,7 +41,7 @@ This model can also be fit as
julia> using Random

julia> lm(hcat(ones(10), 1:10), randn(MersenneTwister(12321), 10))
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}:
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────
Expand Down
34 changes: 9 additions & 25 deletions docs/src/examples.md
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Expand Up @@ -20,9 +20,7 @@ julia> data = DataFrame(X=[1,2,3], Y=[2,4,7])
3 │ 3 7

julia> ols = lm(@formula(Y ~ X), data)
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Y ~ 1 + X
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
─────────────────────────────────────────────────────────────────────────
Expand Down Expand Up @@ -99,9 +97,7 @@ julia> data = DataFrame(X=[1,2,2], Y=[1,0,1])
3 │ 2 1

julia> probit = glm(@formula(Y ~ X), data, Binomial(), ProbitLink())
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Binomial{Float64}, ProbitLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Y ~ 1 + X
GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Binomial{Float64}, ProbitLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────────────
Expand Down Expand Up @@ -140,9 +136,7 @@ julia> quine = dataset("MASS", "quine")
131 rows omitted

julia> nbrmodel = glm(@formula(Days ~ Eth+Sex+Age+Lrn), quine, NegativeBinomial(2.0), LogLink())
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, NegativeBinomial{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Days ~ 1 + Eth + Sex + Age + Lrn
GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, NegativeBinomial{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────────────────
Expand All @@ -158,9 +152,7 @@ Lrn: SL 0.296768 0.185934 1.60 0.1105 -0.0676559 0.661191
────────────────────────────────────────────────────────────────────────────

julia> nbrmodel = negbin(@formula(Days ~ Eth+Sex+Age+Lrn), quine, LogLink())
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, NegativeBinomial{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Days ~ 1 + Eth + Sex + Age + Lrn
GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, NegativeBinomial{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────────────────
Expand All @@ -175,7 +167,7 @@ Age: F3 0.356955 0.247228 1.44 0.1488 -0.127602 0.841513
Lrn: SL 0.292138 0.18565 1.57 0.1156 -0.0717297 0.656006
────────────────────────────────────────────────────────────────────────────

julia> println("Estimated theta = ", round(nbrmodel.model.rr.d.r, digits=5))
julia> println("Estimated theta = ", round(nbrmodel.rr.d.r, digits=5))
Estimated theta = 1.27489

```
Expand Down Expand Up @@ -207,9 +199,7 @@ julia> form = dataset("datasets", "Formaldehyde")
6 │ 0.9 0.782

julia> lm1 = fit(LinearModel, @formula(OptDen ~ Carb), form)
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

OptDen ~ 1 + Carb
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
───────────────────────────────────────────────────────────────────────────
Expand Down Expand Up @@ -256,9 +246,7 @@ julia> LifeCycleSavings = dataset("datasets", "LifeCycleSavings")
35 rows omitted

julia> fm2 = fit(LinearModel, @formula(SR ~ Pop15 + Pop75 + DPI + DDPI), LifeCycleSavings)
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

SR ~ 1 + Pop15 + Pop75 + DPI + DDPI
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
─────────────────────────────────────────────────────────────────────────────────
Expand Down Expand Up @@ -364,9 +352,7 @@ julia> dobson = DataFrame(Counts = [18.,17,15,20,10,21,25,13,13],
9 │ 13.0 3 3

julia> gm1 = fit(GeneralizedLinearModel, @formula(Counts ~ Outcome + Treatment), dobson, Poisson())
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Poisson{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Counts ~ 1 + Outcome + Treatment
GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Poisson{Float64}, LogLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────────────────
Expand Down Expand Up @@ -421,9 +407,7 @@ julia> round(optimal_bic.minimizer, digits = 5) # Optimal λ
0.40935

julia> glm(@formula(Volume ~ Height + Girth), trees, Normal(), PowerLink(optimal_bic.minimizer)) # Best model
StatsModels.TableRegressionModel{GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Normal{Float64}, PowerLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}, Matrix{Float64}}

Volume ~ 1 + Height + Girth
GeneralizedLinearModel{GLM.GlmResp{Vector{Float64}, Normal{Float64}, PowerLink}, GLM.DensePredChol{Float64, LinearAlgebra.Cholesky{Float64, Matrix{Float64}}}}:

Coefficients:
────────────────────────────────────────────────────────────────────────────
Expand Down
8 changes: 2 additions & 6 deletions docs/src/index.md
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Expand Up @@ -85,9 +85,7 @@ julia> data = DataFrame(y = rand(rng, 100), x = categorical(repeat([1, 2, 3, 4],


julia> lm(@formula(y ~ x), data)
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

y ~ 1 + x
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
───────────────────────────────────────────────────────────────────────────
Expand All @@ -108,9 +106,7 @@ julia> using StableRNGs
julia> data = DataFrame(y = rand(StableRNG(1), 100), x = repeat([1, 2, 3, 4], 25));

julia> lm(@formula(y ~ x), data, contrasts = Dict(:x => DummyCoding()))
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}}}}, Matrix{Float64}}

y ~ 1 + x
LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}:

Coefficients:
───────────────────────────────────────────────────────────────────────────
Expand Down
6 changes: 4 additions & 2 deletions src/GLM.jl
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Expand Up @@ -10,12 +10,14 @@ module GLM
import Base: (\), convert, show, size
import LinearAlgebra: cholesky, cholesky!
import Statistics: cor
import StatsBase: coef, coeftable, confint, deviance, nulldeviance, dof, dof_residual,
loglikelihood, nullloglikelihood, nobs, stderror, vcov, residuals, predict,
import StatsBase: coef, coeftable, coefnames, confint, deviance, nulldeviance, dof, dof_residual,
loglikelihood, nullloglikelihood, nobs, stderror, vcov,
residuals, predict, predict!,
fitted, fit, model_response, response, modelmatrix, r2, r², adjr2, adjr², PValue
import StatsFuns: xlogy
import SpecialFunctions: erfc, erfcinv, digamma, trigamma
import StatsModels: hasintercept
import Tables
export coef, coeftable, confint, deviance, nulldeviance, dof, dof_residual,
loglikelihood, nullloglikelihood, nobs, stderror, vcov, residuals, predict,
fitted, fit, fit!, model_response, response, modelmatrix, r2, r², adjr2, adjr²,
Expand Down
11 changes: 11 additions & 0 deletions src/deprecated.jl
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Expand Up @@ -2,3 +2,14 @@

@deprecate confint(obj::LinearModel, level::Real) confint(obj, level=level)
@deprecate confint(obj::AbstractGLM, level::Real) confint(obj, level=level)

function Base.getproperty(mm::LinPredModel, f::Symbol)
if f === :model
Base.depwarn("accessing the `model` field of GLM.jl models is deprecated, " *
"as they are no longer wrapped in a `TableRegressionModel` " *
"and can be used directly now", :getproperty)
return mm
else
return getfield(mm, f)
end
end
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5 changes: 2 additions & 3 deletions src/ftest.jl
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Expand Up @@ -108,8 +108,7 @@ julia> model = lm(@formula(Result ~ 1 + Treatment), dat);

julia> bigmodel = lm(@formula(Result ~ 1 + Treatment + Other), dat);


julia> ftest(nullmodel.model, model.model)
julia> ftest(nullmodel, model)
F-test: 2 models fitted on 12 observations
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
Expand All @@ -118,7 +117,7 @@ F-test: 2 models fitted on 12 observations
[2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-07
──────────────────────────────────────────────────────────────────

julia> ftest(nullmodel.model, model.model, bigmodel.model)
julia> ftest(nullmodel, model, bigmodel)
F-test: 3 models fitted on 12 observations
──────────────────────────────────────────────────────────────────
DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
Expand Down
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