Change dof_residual definition to not depend on dispersion parameter

JuliaStats · May 8, 2024 · 60add18 · 60add18
1 parent e2f6c98
commit 60add18
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Showing 5 changed files with 6 additions and 8 deletions.
diff --git a/Project.toml b/Project.toml
@@ -17,7 +17,7 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
-CSV = "0.7, 0.8"
+CSV = "0.7, 0.8, 0.9, 0.10"
 CategoricalArrays = "0.8, 0.9, 0.10"
 DataFrames = "0.22, 1"
 Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"

diff --git a/docs/src/examples.md b/docs/src/examples.md
@@ -57,7 +57,7 @@ julia> round(adjr2(ols); digits=5)
 julia> round(deviance(ols); digits=5)
 0.16667
 
-julia> dof(ols)
+julia> dof(old)
 3
 
 julia> dof_residual(ols)

diff --git a/src/glmfit.jl b/src/glmfit.jl
@@ -351,10 +351,7 @@ function nullloglikelihood(m::GeneralizedLinearModel)
  return ll
 end
 
-function dof(x::GeneralizedLinearModel)
- modelrank = linpred_rank(x.pp)
- dispersion_parameter(x.rr.d) ? modelrank + 1 : modelrank
-end
+dof(obj::GeneralizedLinearModel) = linpred_rank(obj) + dispersion_parameter(obj.rr.d)
 
 function _fit!(m::AbstractGLM, verbose::Bool, maxiter::Integer, minstepfac::Real,
  atol::Real, rtol::Real, start)

diff --git a/src/linpred.jl b/src/linpred.jl
@@ -387,10 +387,11 @@ coef(obj::LinPredModel) = coef(obj.pp)
 coefnames(x::LinPredModel) =
  x.formula === nothing ? ["x$i" for i in 1:length(coef(x))] : coefnames(formula(x).rhs)
 
-dof_residual(obj::LinPredModel) = nobs(obj) - dof(obj) + 1
+dof_residual(obj::LinPredModel) = nobs(obj) - linpred_rank(obj)
 
 hasintercept(m::LinPredModel) = any(i -> all(==(1), view(m.pp.X , :, i)), 1:size(m.pp.X, 2))
 
+linpred_rank(x::LinPredModel) = linpred_rank(x.pp)
 linpred_rank(x::LinPred) = length(x.beta0)
 linpred_rank(x::DensePredChol{<:Any, <:CholeskyPivoted}) = rank(x.chol)
 linpred_rank(x::DensePredChol{<:Any, <:Cholesky}) = rank(x.chol.U)

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1919,7 +1919,7 @@ end
  @test deviance(mdl) ≈ 135.68506068159
  @test loglikelihood(mdl) ≈ -67.8425303407948
  @test dof(mdl) == 3
- @test dof_residual(mdl) == 98
+ @test dof_residual(mdl) == 97
  @test aic(mdl) ≈ 141.68506068159
  @test GLM.dispersion(mdl, true) ≈ 1
  @test predict(mdl)[1:3] ≈ [0.4241893070433117, 0.3754516361306202, 0.6327877688720133] atol = 1.0E-6