Merge pull request #9 from yufongpeng/release-0.2.3

Release 0.2.3

Project.toml

@@ -1,7 +1,7 @@
 name = "AnovaFixedEffectModels"
 uuid = "e4965305-65d6-464d-9c03-ae3e5cffadab"
 authors = ["yufongpeng <54415349+yufongpeng@users.noreply.github.com> and contributors"]
-version = "0.2.2"
+version = "0.2.3"
 
 [deps]
 AnovaBase = "946dddda-6a23-4b48-8e70-8e60d9b8d680"

src/AnovaFixedEffectModels.jl

@@ -7,7 +7,7 @@ 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
+using AnovaBase: select_super_interaction, extract_contrasts, canonicalgoodnessoffit, subformula, dof_asgn, lrt_nested, ftest_nested, _diff, _diffn, has_intercept
 import AnovaBase: anova, nestedmodels, anovatable, prednames, predictors, formula
 using Tables: columntable
 

src/anova.jl

@@ -54,7 +54,7 @@ function anova(::Type{FTest},
     assign = asgn(predictors(aovm))
     fullpred = predictors(aovm.model)
     fullasgn = asgn(fullpred)
-    df = filter(>(0), dof_asgn(assign))
+    df = tuple(filter(>(0), dof_asgn(assign))...)
     # May exist some floating point error from dof_residual
     varβ = vcov(aovm.model)
     β = aovm.model.coef

src/fit.jl

@@ -5,4 +5,24 @@ predictors(model::T) where {T <: FixedEffectModel} = model.formula_schema.rhs.te
 
 # Variable dispersion
 dof_pred(model::FixedEffectModel) = nobs(model) - dof_residual(model)
-# define dof on NestedModels?
+# define dof on NestedModels?
+
+isfe(::AbstractTerm) = false
+isfe(::FunctionTerm{typeof(fe)}) = true
+isfe(term::InteractionTerm) = any(isfe, term.terms)
+
+"""
+    nestedmodels(modeltype::Type{FixedEffectModel}, f::FormulaTerm, data; null = true, kwargs...)
+
+Generate nested models from modeltype, formula and data. The null model will be an empty model if the keyword argument null is true (default).
+"""
+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!")))
+    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))
+end

test/Manifest.toml

@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.8.3"
+julia_version = "1.8.5"
 manifest_format = "2.0"
 project_hash = "49564b7d6a8af6473e51894f1112145c59b07d7c"
 
@@ -71,7 +71,7 @@ version = "4.6.0"
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "0.5.2+0"
+version = "1.0.1+0"
 
 [[deps.Crayons]]
 git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15"

test/runtests.jl

@@ -64,5 +64,12 @@ isapprox(x::NTuple{N, Float64}, y::NTuple{N, Float64}, atol::NTuple{N, Float64}
             @test isapprox(first(teststat(aovf2)), first(teststat(aovl2)))
             @test isapprox(teststat(aovfs)[2], teststat(aovfs2)[2])
         end
+        @testset "nestedmodels" begin
+            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
 end