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Release 0.2.2 #9

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Mar 19, 2023
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Release 0.2.2 #9

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30 changes: 15 additions & 15 deletions src/anova.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -41,25 +41,25 @@ anova(models::FullModel{<: TableRegressionModel{<: LinearModel}};
kwargs...) =
anova(test, models; kwargs...)

anova(anovamodel::NestedModels{<: TableRegressionModel{<: LinearModel}};
anova(aovm::NestedModels{<: TableRegressionModel{<: LinearModel}};
test::Type{<: GoodnessOfFit} = FTest,
kwargs...) =
anova(test, anovamodel; kwargs...)
anova(test, aovm; kwargs...)

anova(models::Vararg{TableRegressionModel{<: GeneralizedLinearModel}};
test::Type{<: GoodnessOfFit} = canonicalgoodnessoffit(models[1].model.rr.d),
kwargs...) =
anova(test, models...; kwargs...)

anova(anovamodel::FullModel{<: TableRegressionModel{<: GeneralizedLinearModel}};
test::Type{<: GoodnessOfFit} = canonicalgoodnessoffit(anovamodel.model.model.rr.d),
anova(aovm::FullModel{<: TableRegressionModel{<: GeneralizedLinearModel}};
test::Type{<: GoodnessOfFit} = canonicalgoodnessoffit(aovm.model.model.rr.d),
kwargs...) =
anova(test, anovamodel; kwargs...)
anova(test, aovm; kwargs...)

anova(anovamodel::NestedModels{<: TableRegressionModel{<: GeneralizedLinearModel}};
test::Type{<: GoodnessOfFit} = canonicalgoodnessoffit(anovamodel.model[1].model.rr.d),
anova(aovm::NestedModels{<: TableRegressionModel{<: GeneralizedLinearModel}};
test::Type{<: GoodnessOfFit} = canonicalgoodnessoffit(aovm.model[1].model.rr.d),
kwargs...) =
anova(test, anovamodel; kwargs...)
anova(test, aovm; kwargs...)

# ==================================================================================================================
# ANOVA by F test
Expand All @@ -74,7 +74,7 @@ function anova(::Type{FTest}, aovm::FullModel{<: TRM_LM})
assign = asgn(predictors(aovm))
fullpred = predictors(aovm.model)
fullasgn = asgn(fullpred)
df = dof_asgn(assign)
df = tuple(dof_asgn(assign)...)
varβ = vcov(aovm.model.model)
β = aovm.model.model.pp.beta0
offset = first(assign) + last(fullasgn) - last(assign) - 1
Expand Down Expand Up @@ -110,7 +110,7 @@ function anova(::Type{FTest},
devs = deviances(aovm; kwargs...)
assign = asgn(collect(predictors(aovm)))
#length(vdf) ≡ length(devs) + 1 && popfirst!(vdf)
df = dof_asgn(assign)
df = tuple(dof_asgn(assign)...)
msr = devs ./ df
fstat = msr ./ dispersion(aovm.model.model, true)
dfr = round(Int, dof_residual(aovm.model))
Expand All @@ -130,7 +130,7 @@ function anova(::Type{LRT},
Δdev = deviances(aovm)
assign = asgn(collect(predictors(aovm)))
#isnullable(trm.model) || popfirst!(vdf)
df = dof_asgn(assign)
df = tuple(dof_asgn(assign)...)
# den = last(ss) / (nobs(trm) - dof(trm) + 1)
# lrstat = ss[1:end - 1] ./ den
σ² = dispersion(aovm.model.model, true)
Expand Down Expand Up @@ -175,11 +175,11 @@ function anova(::Type{LRT},
lrt_nested(NestedModels{M}(trms), df, deviance.(trms), dispersion(last(trms).model, true))
end

anova(::Type{FTest}, anovamodel::NestedModels{M}) where {M <: TableRegressionModel{<: Union{LinearModel, GeneralizedLinearModel}}} =
ftest_nested(anovamodel, dof.(anovamodel.model), round.(Int, dof_residual.(anovamodel.model)), deviance.(anovamodel.model), dispersion(last(anovamodel.model).model, true))
anova(::Type{FTest}, aovm::NestedModels{M}) where {M <: TableRegressionModel{<: Union{LinearModel, GeneralizedLinearModel}}} =
ftest_nested(aovm, dof.(aovm.model), round.(Int, dof_residual.(aovm.model)), deviance.(aovm.model), dispersion(last(aovm.model).model, true))

anova(::Type{LRT}, anovamodel::NestedModels{M}) where {M <: TableRegressionModel{<: Union{LinearModel, GeneralizedLinearModel}}} =
lrt_nested(anovamodel, dof.(anovamodel.model), deviance.(anovamodel.model), dispersion(last(anovamodel.model).model, true))
anova(::Type{LRT}, aovm::NestedModels{M}) where {M <: TableRegressionModel{<: Union{LinearModel, GeneralizedLinearModel}}} =
lrt_nested(aovm, dof.(aovm.model), deviance.(aovm.model), dispersion(last(aovm.model).model, true))
# =================================================================================================================================
# Fit new models

Expand Down