Correct p-values and CI with clustered SE (#202)

* Update fit.jl

* Update FixedEffectModel.jl

* Update Project.toml

* Update FixedEffectModel.jl

Author: matthieugomez

Project.toml

@@ -1,6 +1,6 @@
 name = "FixedEffectModels"
 uuid = "9d5cd8c9-2029-5cab-9928-427838db53e3"
-version = "1.6.6"
+version = "1.6.7"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

src/FixedEffectModel.jl

@@ -25,6 +25,7 @@ struct FixedEffectModel <: RegressionModel
 
     nobs::Int64             # Number of observations
     dof_residual::Int64      # nobs - degrees of freedoms
+    df_FStat::Int64
 
     rss::Float64            # Sum of squared residuals
     tss::Float64            # Total sum of squares
@@ -65,7 +66,7 @@ StatsBase.mss(m::FixedEffectModel) = deviance(m) - rss(m)
 
 
 function StatsBase.confint(m::FixedEffectModel; level::Real = 0.95)
-    scale = tdistinvcdf(dof_residual(m), 1 - (1 - level) / 2)
+    scale = tdistinvcdf(m.df_FStat, 1 - (1 - level) / 2)
     se = stderror(m)
     hcat(m.coef -  scale * se, m.coef + scale * se)
 end
@@ -154,7 +155,7 @@ function StatsBase.coeftable(m::FixedEffectModel; level = 0.95)
     end
     tt = cc ./ se
     CoefTable(
-        hcat(cc, se, tt, fdistccdf.(Ref(1), Ref(dof_residual(m)), abs2.(tt)), conf_int[:, 1:2]),
+        hcat(cc, se, tt, fdistccdf.(Ref(1), Ref(m.df_FStat), abs2.(tt)), conf_int[:, 1:2]),
         ["Estimate","Std.Error","t value", "Pr(>|t|)", "Lower 95%", "Upper 95%" ],
         ["$(coefnms[i])" for i = 1:length(cc)], 4)
 end
@@ -224,7 +225,7 @@ function Base.show(io::IO, m::FixedEffectModel)
         coefnms = coefnms[newindex]
     end
     tt = cc ./ se
-    mat = hcat(cc, se, tt, fdistccdf.(Ref(1), Ref(dof_residual(m)), abs2.(tt)), conf_int[:, 1:2])
+    mat = hcat(cc, se, tt, fdistccdf.(Ref(1), Ref(m.df_FStat), abs2.(tt)), conf_int[:, 1:2])
     nr, nc = size(mat)
     colnms = ["Estimate","Std.Error","t value", "Pr(>|t|)", "Lower 95%", "Upper 95%"]
     rownms = ["$(coefnms[i])" for i = 1:length(cc)]

src/fit.jl

@@ -449,5 +449,5 @@ function reg(
         esample = trues(N)
     end
 
-    return FixedEffectModel(coef, matrix_vcov, vcov, nclusters, esample, residuals2, augmentdf, fekeys, coef_names, response_name, formula_origin, formula_schema, contrasts, nobs, dof_residual_,  rss, tss_total, r2, adjr2, F, p, iterations, converged, r2_within, F_kp, p_kp)
+    return FixedEffectModel(coef, matrix_vcov, vcov, nclusters, esample, residuals2, augmentdf, fekeys, coef_names, response_name, formula_origin, formula_schema, contrasts, nobs, dof_residual_, df_FStat_, rss, tss_total, r2, adjr2, F, p, iterations, converged, r2_within, F_kp, p_kp)
 end