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Merge pull request #11 from yufongpeng/release-0.2.5
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Release 0.2.5
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@yufongpeng
yufongpeng authored Feb 28, 2024
2 parents 620bd31 + ef30afc commit 8d348c3
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Showing 6 changed files with 72 additions and 17 deletions.
6 changes: 3 additions & 3 deletions Project.toml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
name = "AnovaFixedEffectModels"
uuid = "e4965305-65d6-464d-9c03-ae3e5cffadab"
authors = ["yufongpeng <[email protected]> and contributors"]
version = "0.2.4"
version = "0.2.5"

[deps]
AnovaBase = "946dddda-6a23-4b48-8e70-8e60d9b8d680"
Expand All @@ -16,11 +16,11 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"

[compat]
AnovaBase = "0.7"
AnovaBase = "0.8"
Distributions = "0.23, 0.24, 0.25"
FixedEffectModels = "1.3, 1.4, 1.5, 1.6, 1.7, 1.8"
Reexport = "0.2, 1"
StatsBase = "0.33, 0.34"
StatsModels = "0.7"
Tables = "1.7"
julia = "1.6, 1.7, 1.8, 1.9"
julia = "1.6, 1.7, 1.8, 1.9, 1.10"
4 changes: 2 additions & 2 deletions src/AnovaFixedEffectModels.jl
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Expand Up @@ -7,10 +7,10 @@ import StatsBase: fit!, fit
import StatsModels: TableRegressionModel, vectorize, width, apply_schema,
ModelFrame, ModelMatrix, columntable, asgn

using AnovaBase: select_super_interaction, extract_contrasts, canonicalgoodnessoffit, subformula, dof_asgn, lrt_nested, ftest_nested, _diff, _diffn, has_intercept
using AnovaBase: select_super_interaction, extract_contrasts, canonicalgoodnessoffit, subformula, dof_asgn, lrt_nested, ftest_nested, _diff, _diffn, testname
import AnovaBase: anova, nestedmodels, anovatable, prednames, predictors, formula
using Tables: columntable

import Base: show
export anova_lfe, lfe

include("anova.jl")
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12 changes: 6 additions & 6 deletions src/anova.jl
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Expand Up @@ -66,10 +66,10 @@ function anova(::Type{FTest},
end
elseif aovm.type == 2
fstat = ntuple(last(fullasgn) - offset) do fix
select1 = sort!(collect(select_super_interaction(fullpred, fix + offset)))
select2 = setdiff(select1, fix + offset)
select1 = findall(in(select1), fullasgn)
select2 = findall(in(select2), fullasgn)
s1 = sort!(collect(select_super_interaction(fullpred, fix + offset)))
s2 = setdiff(s1, fix + offset)
select1 = findall(in(s1), fullasgn)
select2 = findall(in(s2), fullasgn)
(β[select1]' * (varβ[select1, select1] \ β[select1]) - β[select2]' * (varβ[select2, select2] \ β[select2])) / df[fix]
end
else
Expand All @@ -83,7 +83,7 @@ function anova(::Type{FTest},
σ² = rss(aovm.model) / dfr
devs = @. fstat * σ² * df
pvalue = @. ccdf(FDist(df, dfr), abs(fstat))
AnovaResult{FTest}(aovm, df, devs, fstat, pvalue, NamedTuple())
AnovaResult(aovm, FTest, df, devs, fstat, pvalue, NamedTuple())
end

# =================================================================================================================
Expand All @@ -102,7 +102,7 @@ function anova(::Type{FTest},
dev = rss.(models)
# check comparable and nested
check && @warn "Could not check whether models are nested: results may not be meaningful"
ftest_nested(NestedModels{M}(models), df, dfr, dev, last(dev) / last(dfr))
ftest_nested(NestedModels(models), df, dfr, dev, last(dev) / last(dfr))
end

function anova(::Type{FTest}, aovm::NestedModels{M}) where {M <: FixedEffectModel}
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5 changes: 2 additions & 3 deletions src/fit.jl
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@@ -1,6 +1,5 @@
# ==========================================================================================================
# Backend funcion
formula(model::T) where {T <: FixedEffectModel} = model.formula
predictors(model::T) where {T <: FixedEffectModel} = model.formula_schema.rhs.terms

# Variable dispersion
Expand All @@ -19,10 +18,10 @@ Generate nested models from modeltype, formula and data. The null model will be
function nestedmodels(modeltype::Type{FixedEffectModel}, f::FormulaTerm, data; null = true, kwargs...)
fullm = lfe(f, data; kwargs...)
predterms = predictors(fullm)
has_intercept(predterms) || (length(predterms) > 1 ? (predterms = predterms[2:end]) : throw(ArgumentError("Empty model is given!")))
hasintercept(predterms) || (length(predterms) > 1 ? (predterms = predterms[2:end]) : throw(ArgumentError("Empty model is given!")))
feterms = filter(isfe, f.rhs)
subm = ntuple(length(predterms) - 1) do i
lfe(FormulaTerm(f.lhs, (predterms[1:i]..., feterms...)), data; kwargs...)
end
NestedModels{FixedEffectModel}(null ? (lfe(FormulaTerm(f.lhs, (ConstantTerm(0), feterms...)), data; kwargs...), subm..., fullm) : (subm..., fullm))
NestedModels(null ? (lfe(FormulaTerm(f.lhs, (ConstantTerm(0), feterms...)), data; kwargs...), subm..., fullm) : (subm..., fullm))
end
57 changes: 56 additions & 1 deletion src/io.jl
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Expand Up @@ -38,4 +38,59 @@ function anovatable(aov::AnovaResult{<: NestedModels{<: FixedEffectModel, N}, FT
],
["DOF", "ΔDOF", "Res.DOF", "R²", "ΔR²", "R²_within", "ΔR²_within", "Res.SS", "Exp.SS", "F value", "Pr(>|F|)"],
rownames, 11, 10)
end
end

function show(io::IO, anovamodel::FullModel{<: T}) where {T <: FixedEffectModel}
println(io, "FullModel for type $(anovamodel.type) test")
println(io)
println(io, "Predictors:")
println(io, join(prednames(anovamodel), ", "))
println(io)
println(io, "Formula:")
println(io, anovamodel.model.formula)
println(io)
println(io, "Coefficients:")
show(io, coeftable(anovamodel.model))
end

function show(io::IO, anovamodel::NestedModels{M, N}) where {M <: FixedEffectModel, N}
println(io, "NestedModels with $N models")
println(io)
println(io, "Formulas:")
for(id, m) in enumerate(anovamodel.model)
println(io, "Model $id: ", m.formula)
end
println(io)
println(io, "Coefficients:")
show(io, coeftable(first(anovamodel.model)))
println(io)
N > 2 && print(io, " .\n" ^ 3)
show(io, coeftable(last(anovamodel.model)))
end

# Show function that delegates to anovatable
function show(io::IO, aov::AnovaResult{<: FullModel{<: FixedEffectModel}, T}) where {T <: GoodnessOfFit}
at = anovatable(aov)
println(io, "Analysis of Variance")
println(io)
println(io, "Type $(anova_type(aov)) test / $(testname(T))")
println(io)
println(io, aov.anovamodel.model.formula)
println(io)
println(io, "Table:")
show(io, at)
end

function show(io::IO, aov::AnovaResult{<: MultiAovModels{<: FixedEffectModel}, T}) where {T <: GoodnessOfFit}
at = anovatable(aov)
println(io,"Analysis of Variance")
println(io)
println(io, "Type $(anova_type(aov)) test / $(testname(T))")
println(io)
for(id, m) in enumerate(aov.anovamodel.model)
println(io, "Model $id: ", m.formula)
end
println(io)
println(io, "Table:")
show(io, at)
end
5 changes: 3 additions & 2 deletions test/runtests.jl
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Expand Up @@ -52,12 +52,14 @@ isapprox(x::NTuple{N, Float64}, y::NTuple{N, Float64}, atol::NTuple{N, Float64}
global aovl1 = AnovaGLM.anova(lm1)
global aovf2 = AFE.anova(fem2, type = 3)
global aovl2 = AnovaGLM.anova(lm2, type = 3)
global aovfs = AFE.anova(NestedModels{FixedEffectModel}(fem0, fem1))
global aovfs = AFE.anova(NestedModels(fem0, fem1))
global aovfs2 = AFE.anova(fem0, fem1)
global aovls = AnovaGLM.anova(lm0, lm1)
@test !(@test_error test_show(aovf1))
@test !(@test_error test_show(aovf1.anovamodel))
@test !(@test_error test_show(aovf2))
@test !(@test_error test_show(aovfs))
@test !(@test_error test_show(aovfs.anovamodel))
@test nobs(aovf1) == nobs(aovl1)
@test last(dof(aovf1)) == dof(aovl1)[end - 1]
@test isapprox(first(deviance(aovf2)), first(deviance(aovl2)))
Expand All @@ -68,7 +70,6 @@ isapprox(x::NTuple{N, Float64}, y::NTuple{N, Float64}, atol::NTuple{N, Float64}
df = DataFrame(y = randn(1000), x = rand(1:5, 1000), z = rand(["1", "2"], 1000), t = 1:1000)
fems1 = nestedmodels(FixedEffectModel, @formula(y ~ t + fe(z) + fe(x)), df)
fems2 = nestedmodels(FixedEffectModel, @formula(y ~ z + t & fe(x)), df)
@test formula(fems1.model[1]).rhs == @formula(y ~ 0 + fe(z) + fe(x)).rhs
@test AFE.predictors(fems2.model[2])[1] == InterceptTerm{true}()
end
end
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Registration pull request created: JuliaRegistries/General/101934

Tip: Release Notes

Did you know you can add release notes too? Just add markdown formatted text underneath the comment after the text
"Release notes:" and it will be added to the registry PR, and if TagBot is installed it will also be added to the
release that TagBot creates. i.e.

@JuliaRegistrator register

Release notes:

## Breaking changes

- blah

To add them here just re-invoke and the PR will be updated.

Tagging

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.2.5 -m "<description of version>" 8d348c306d586cfc7557d3eb83e02847a5b81b13
git push origin v0.2.5

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