Merge pull request #13 from matthieugomez/patch-7

rename df_Fstat to dof_tstat

Project.toml

@@ -1,18 +1,20 @@
 name = "Vcov"
 uuid = "ec2bfdc2-55df-4fc9-b9ae-4958c2cf2486"
-version = "0.5.2"
+version = "0.6.0"
 
 [deps]
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 GroupedArrays = "6407cd72-fade-4a84-8a1e-56e431fc1533"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
 Combinatorics = "1"
 GroupedArrays = "0.3"
-StatsBase = "0.32, 0.33"
+StatsAPI = "1"
+StatsBase = "0.33"
 Tables = "1"
 julia = "1"
 

README.md

@@ -12,8 +12,8 @@ completecases(table, ::CovarianceEstimator) = trues(size(df, 1))
 materialize(table, v::CovarianceEstimator) = v
 # return variance-covariance matrix
 vcov(x::RegressionModel, ::CovarianceEstimator) = error("vcov not defined for this type")
-# returns the degree of freedom for the F-statistic
-df_FStat(x::RegressionModel, ::CovarianceEstimator, hasintercept::Bool) = dof_residual(x) - hasintercept
+# returns the degree of freedom for the t-statistics and F-statistic
+dof_tstat(x::RegressionModel, ::CovarianceEstimator, hasintercept::Bool) = dof_residual(x) - hasintercept
 ```
 
 For now, it includes `Vcov.simple()`, `Vcov.robust()`, and `Vcov.cluster(...)`.

src/Vcov.jl

@@ -3,7 +3,8 @@ module Vcov
 using Combinatorics: combinations
 using GroupedArrays: GroupedArray
 using LinearAlgebra: cholesky!, Symmetric, Hermitian, svd, rmul!, eigen, Diagonal
-using StatsBase: StatsBase, dof_residual, RegressionModel, CovarianceEstimator, modelmatrix, crossmodelmatrix, residuals, dof_residual
+using StatsAPI: StatsAPI, RegressionModel, modelmatrix, crossmodelmatrix, residuals, dof_residual
+using StatsBase: CovarianceEstimator
 using Tables: Tables
 using Base: @propagate_inbounds
 
@@ -18,10 +19,10 @@ struct VcovData{T, N} <: RegressionModel
     residuals::Array{Float64, N} # vector or matrix of residuals (matrix in the case of IV, residuals of Xendo on (Z, Xexo))
     dof_residual::Int
 end
-StatsBase.modelmatrix(x::VcovData) = x.modelmatrix
-StatsBase.crossmodelmatrix(x::VcovData) = x.crossmodelmatrix
-StatsBase.residuals(x::VcovData) = x.residuals
-StatsBase.dof_residual(x::VcovData) = x.dof_residual
+StatsAPI.modelmatrix(x::VcovData) = x.modelmatrix
+StatsAPI.crossmodelmatrix(x::VcovData) = x.crossmodelmatrix
+StatsAPI.residuals(x::VcovData) = x.residuals
+StatsAPI.dof_residual(x::VcovData) = x.dof_residual
 
 ##############################################################################
 ##
@@ -34,7 +35,7 @@ function completecases(table, ::CovarianceEstimator)
 end
 materialize(table, v::CovarianceEstimator) = v
 S_hat(x::RegressionModel, ::CovarianceEstimator) = error("S_hat not defined for this type")
-df_FStat(x::RegressionModel, ::CovarianceEstimator, hasintercept::Bool) = dof_residual(x) - hasintercept
+dof_tstat(x::RegressionModel, ::CovarianceEstimator, hasintercept::Bool) = dof_residual(x) - hasintercept
 
 
 

src/covarianceestimators/vcovcluster.jl

@@ -71,7 +71,7 @@ function nclusters(v::ClusterCovariance)
     NamedTuple{names(v)}(map(x -> x.ngroups, v.clusters))
 end
 
-function df_FStat(x::RegressionModel, v::ClusterCovariance, ::Bool)
+function dof_tstat(x::RegressionModel, v::ClusterCovariance, ::Bool)
     minimum(nclusters(v)) - 1
 end
 
@@ -110,7 +110,7 @@ function helper_cluster(X::Matrix, res::Union{Vector, Matrix}, g::GroupedArray)
     return Symmetric(X2' * X2)
 end
 
-function StatsBase.vcov(x::RegressionModel, v::ClusterCovariance)
+function StatsAPI.vcov(x::RegressionModel, v::ClusterCovariance)
     xtx = inv(crossmodelmatrix(x))
     pinvertible(Symmetric(xtx * S_hat(x, v) * xtx))
 end

src/covarianceestimators/vcovrobust.jl

@@ -27,7 +27,7 @@ function S_hat(x::RegressionModel, ::RobustCovariance)
     Symmetric(rmul!(S2, size(m, 1) / dof_residual(x)))
 end
 
-function StatsBase.vcov(x::RegressionModel, v::RobustCovariance)
+function StatsAPI.vcov(x::RegressionModel, v::RobustCovariance)
     xtx = inv(crossmodelmatrix(x))
     pinvertible(Symmetric(xtx * S_hat(x, v) * xtx))
 end

src/covarianceestimators/vcovsimple.jl

@@ -14,7 +14,7 @@ function S_hat(x::RegressionModel, ::SimpleCovariance)
 	rmul!(crossmodelmatrix(x), sum(abs2, residuals(x)))
 end
 
-function StatsBase.vcov(x::RegressionModel, ::SimpleCovariance)
+function StatsAPI.vcov(x::RegressionModel, ::SimpleCovariance)
     invcrossmodelmatrix = Matrix(inv(crossmodelmatrix(x)))
     rmul!(invcrossmodelmatrix, sum(abs2, residuals(x)) /  dof_residual(x))
     Symmetric(invcrossmodelmatrix)