regression table latest version

Gkreindler · Dec 22, 2023 · 5e3c807 · 5e3c807
1 parent 25de03b
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Showing 8 changed files with 94 additions and 115 deletions.
diff --git a/Project.toml b/Project.toml
@@ -24,6 +24,7 @@ Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Vcov = "ec2bfdc2-55df-4fc9-b9ae-4958c2cf2486"
 
 [compat]

diff --git a/examples/example_ols_2step.jl b/examples/example_ols_2step.jl
@@ -57,16 +57,36 @@ myopts = GMMTools.GMMOptions(
                 trace=1)
 
 # estimate model
-myfit = fit(df, ols_moments_fn, theta0, mode=:twostep, opts=myopts)
+myfit = GMMTools.fit(df, ols_moments_fn, theta0, mode=:twostep, opts=myopts)
 
 # compute asymptotic variance-covariance matrix and save in myfit.vcov
     vcov_simple(df, ols_moments_fn, myfit)
 
 # print table with results
+
+    # temp = GMMTools.GMMModel(myfit)
+    # dfsdfsdf
+    # RegressionTables.get_coefname()
+    # # formula_schema
+    # formula(temp)
+
+    temp = GMMTools.GMMModel(myfit)
+    formula(temp)
+    display(temp)
+
+    dsfd
+    # RegressionTables.formula(m::GMMModel) = term(m.responsename) ~ sum(term.(String.(m.coefnames)))
+
     GMMTools.regtable(myfit)
 
+fsdfds
+    f1 = term("mpg") ~ term("acceleration") + term("acceleration2")
+    get_coefname(f1.rhs)
+
 
+
 
+sdf
 
 
 

diff --git a/examples/temp/step1/Wstep2.csv b/examples/temp/step1/Wstep2.csv
@@ -1,2 +1,2 @@
-0.716038140204011,-0.04348751629684115
--0.04348751629684115,0.00271677294881702
+0.7160381402040145,-0.043487516296841375
+-0.043487516296841375,0.0027167729488170337
diff --git a/examples/temp/step1/results.csv b/examples/temp/step1/results.csv
@@ -1,21 +1,21 @@
 idx,obj_value,converged,iterations,iteration_limit_reached,time_it_took,theta_hat,theta0,is_optimum
-1,1.0090134771009219e-23,true,3,false,3.5336567,"[4.969793004358799, 1.1912045293436935]","[0.03833248646601629, 0.213845359778582]",0
-2,1.7558726393742873e-23,true,3,false,0.0002598,"[4.969793004392259, 1.1912045293416091]","[-0.7042838077754542, -0.4007992090593657]",0
-3,3.485090023460716e-24,true,3,false,0.0002258,"[4.9697930043155, 1.1912045293463907]","[1.4214984593810815, -0.5777261953257666]",0
-4,1.709405940914258e-23,true,3,false,0.0001495,"[4.969793004390416, 1.191204529341724]","[-0.4658959078249555, -0.7303008298610437]",0
-5,2.7773874886452683e-24,true,4,false,0.0002099,"[4.969793004308891, 1.1912045293468023]","[-1.365080912437378, -1.3208174975844296]",0
-6,6.909694086715038e-24,true,3,false,0.0002126,"[4.969793004340717, 1.19120452934482]","[0.5431992343373382, 0.09271546518564394]",0
-7,1.0369010653532012e-23,true,3,false,0.0001562,"[4.969793004360255, 1.1912045293436029]","[-0.7590535993444354, 0.9209639329677229]",0
-8,5.088050807273377e-27,true,4,false,0.0002343,"[4.969793004256079, 1.1912045293500921]","[0.745620993889791, -2.216096423049429]",1
-9,5.7608307585384585e-24,true,3,false,0.0005141,"[4.969793004333189, 1.1912045293452889]","[0.8442354651341021, -0.3265277272261217]",0
-10,1.1371844801828514e-23,true,3,false,0.000318,"[4.969793004365242, 1.1912045293432922]","[-1.0120757369891493, 1.1470306234136702]",0
-11,7.622488159174278e-24,true,3,false,0.000372,"[4.969793004345065, 1.191204529344549]","[0.7232770971838263, -0.728534008508048]",0
-12,2.729599738585982e-24,true,3,false,0.0003055,"[4.96979300430846, 1.1912045293468292]","[0.5202990342876057, 1.1204520037446837]",0
-13,1.1443429254297673e-25,true,3,false,0.0003278,"[4.969793004265068, 1.1912045293495324]","[1.6996228215478644, 1.0844860147154771]",0
-14,1.0212181766944047e-26,true,4,false,0.0003378,"[4.969793004257214, 1.1912045293500215]","[-1.0989445324831357, -0.0993402202293609]",0
-15,2.4923787040363005e-24,true,3,false,0.000244,"[4.96979300430602, 1.1912045293469813]","[1.5447563259135333, -0.22651795438142638]",0
-16,4.8183300124265374e-24,true,3,false,0.0002471,"[4.969793004326375, 1.1912045293457134]","[1.7376928272513674, -1.2953938355637125]",0
-17,1.7932649305832416e-24,true,4,false,0.0003495,"[4.969793004298091, 1.1912045293474751]","[-1.3972500397371668, -1.1529219835943225]",0
-18,1.4139785609879107e-23,true,3,false,0.0002761,"[4.969793004378028, 1.1912045293424958]","[-0.27322961221704645, -0.5101173409267433]",0
-19,1.732439662937055e-26,true,4,false,0.0003514,"[4.969793004258192, 1.1912045293499605]","[-1.7983567634893314, 0.8231200506166791]",0
-20,1.6558411471442423e-23,true,3,false,0.0005128,"[4.969793004388284, 1.191204529341857]","[-0.6634712267123949, -0.2399591176621829]",0
+1,6.008047737764278e-24,true,3,false,5.5143167,"[4.969793004334855, 1.1912045293451852]","[0.7333549537269617, -0.08702752778732076]",0
+2,1.0557870385606695e-23,true,3,false,0.0003319,"[4.969793004361183, 1.1912045293435451]","[0.3348882897775347, -0.7803017039276787]",0
+3,2.127064708754081e-24,true,3,false,0.0003428,"[4.9697930043020655, 1.1912045293472275]","[1.4969905819055915, 0.35078506512446334]",0
+4,5.678514061361174e-27,true,3,false,0.0002609,"[4.9697930042562595, 1.191204529350081]","[1.9104529355027664, 1.3598422250610078]",1
+5,4.821438071086582e-24,true,3,false,0.0002897,"[4.969793004326411, 1.191204529345711]","[0.9728232798844213, -0.14067410011660123]",0
+6,7.813903509746564e-24,true,3,false,0.0003732,"[4.969793004346201, 1.1912045293444784]","[-0.4584614418357323, 1.0411399628260511]",0
+7,1.3648352663569037e-23,true,3,false,0.0003689,"[4.969793004375888, 1.191204529342629]","[0.9174389714694687, -1.5084014988779522]",0
+8,1.0390507291325953e-23,true,3,false,0.0003546,"[4.969793004360332, 1.191204529343598]","[0.12249553087948825, -0.3423467875721143]",0
+9,9.437838946189772e-25,true,4,false,0.0005973,"[4.969793004285966, 1.1912045293482305]","[-0.9204024669915305, -1.2627650563754986]",0
+10,2.298844392076946e-25,true,4,false,0.0007173,"[4.969793004269693, 1.1912045293492444]","[-1.425317850889396, -0.13188760801442104]",0
+11,1.2036885185904109e-23,true,3,false,0.0006497,"[4.969793004368461, 1.1912045293430917]","[-0.15268667522019008, -0.17239262787204485]",0
+12,1.7481638769830322e-24,true,3,false,0.0007074,"[4.969793004297571, 1.1912045293475075]","[1.3023574705316272, 0.5077729828299388]",0
+13,4.069665898792638e-24,true,3,false,0.008444,"[4.969793004320511, 1.1912045293460785]","[0.15426908206241488, 1.5402712760936221]",0
+14,1.8133175621256172e-23,true,3,false,0.0003636,"[4.969793004394511, 1.191204529341469]","[-1.7267252812920018, 1.0219210821259985]",0
+15,8.52026540787376e-24,true,3,false,0.0003829,"[4.969793004350281, 1.1912045293442242]","[0.2747757058146626, 0.1720449857423505]",0
+16,4.67837413012653e-24,true,3,false,0.0004382,"[4.969793004325323, 1.191204529345779]","[1.0110359111683276, -0.17639294266435857]",0
+17,1.531655549498546e-24,true,4,false,0.0007845,"[4.969793004294775, 1.1912045293476818]","[-2.136663990166246, -0.006788132262980114]",0
+18,3.1420837462174614e-26,true,4,false,0.0008888,"[4.969793004259756, 1.1912045293498632]","[-1.2334796855846475, 0.23491144426446822]",0
+19,1.3659190164525006e-23,true,3,false,0.000863,"[4.969793004375919, 1.191204529342627]","[-0.2143326300938984, -0.5149354181924408]",0
+20,8.313046066730828e-24,true,3,false,0.0004578,"[4.969793004349079, 1.191204529344299]","[-0.6336756499029971, 1.4154829458615907]",0
diff --git a/examples/temp/step2/results.csv b/examples/temp/step2/results.csv
@@ -1,21 +1,21 @@
 idx,obj_value,converged,iterations,iteration_limit_reached,time_it_took,theta_hat,theta0,is_optimum
-1,2.6104875942959174e-30,true,3,false,3.4166789,"[4.969793004253967, 1.1912045293502238]","[0.03833248646601629, 0.213845359778582]",0
-2,1.4614007105013917e-30,true,3,false,0.0002614,"[4.9697930042539635, 1.191204529350224]","[-0.7042838077754542, -0.4007992090593657]",0
-3,1.4614007105013917e-30,true,3,false,0.0002229,"[4.9697930042539635, 1.191204529350224]","[1.4214984593810815, -0.5777261953257666]",0
-4,2.1743011519099462e-30,true,3,false,0.0002113,"[4.969793004253983, 1.1912045293502227]","[-0.4658959078249555, -0.7303008298610437]",0
-5,4.503755737267253e-31,true,4,false,0.0002083,"[4.969793004253949, 1.191204529350225]","[-1.365080912437378, -1.3208174975844296]",0
-6,3.6860454164630275e-31,true,3,false,0.0001864,"[4.96979300425393, 1.1912045293502262]","[0.5431992343373382, 0.09271546518564394]",0
-7,3.017625637198401e-31,true,3,false,0.000243,"[4.969793004253935, 1.1912045293502258]","[-0.7590535993444354, 0.9209639329677229]",0
-8,2.4187576534063886e-31,true,4,false,0.0191076,"[4.969793004253909, 1.1912045293502274]","[0.745620993889791, -2.216096423049429]",0
-9,5.549664479705941e-31,true,3,false,0.0003322,"[4.969793004253944, 1.1912045293502254]","[0.8442354651341021, -0.3265277272261217]",0
-10,1.6492852457751382e-30,true,3,false,0.0002759,"[4.96979300425396, 1.1912045293502243]","[-1.0120757369891493, 1.1470306234136702]",0
-11,1.0024279650486883e-30,true,3,false,0.0002529,"[4.969793004253946, 1.1912045293502251]","[0.7232770971838263, -0.728534008508048]",0
-12,9.987757014848227e-31,true,3,false,0.00028,"[4.9697930042539396, 1.1912045293502256]","[0.5202990342876057, 1.1204520037446837]",0
-13,2.4187576534063886e-31,true,3,false,0.0002269,"[4.969793004253909, 1.1912045293502274]","[1.6996228215478644, 1.0844860147154771]",0
-14,2.3803135524615715e-32,true,4,false,0.0003642,"[4.969793004253907, 1.1912045293502276]","[-1.0989445324831357, -0.0993402202293609]",1
-15,1.0024279650486883e-30,true,3,false,0.0003356,"[4.969793004253946, 1.1912045293502251]","[1.5447563259135333, -0.22651795438142638]",0
-16,1.2764725426287234e-30,true,3,false,0.0003944,"[4.969793004253947, 1.1912045293502251]","[1.7376928272513674, -1.2953938355637125]",0
-17,4.299320562841262e-30,true,4,false,0.0003926,"[4.969793004253975, 1.1912045293502234]","[-1.3972500397371668, -1.1529219835943225]",0
-18,4.5866524809437025e-30,true,3,false,0.0004796,"[4.969793004253978, 1.1912045293502231]","[-0.27322961221704645, -0.5101173409267433]",0
-19,1.4564838996979999e-30,true,4,false,0.0003656,"[4.969793004253953, 1.1912045293502247]","[-1.7983567634893314, 0.8231200506166791]",0
-20,2.1766270139140864e-30,true,3,false,0.0004111,"[4.969793004253967, 1.191204529350224]","[-0.6634712267123949, -0.2399591176621829]",0
+1,2.6104875942959304e-30,true,3,false,5.4577173,"[4.969793004253967, 1.1912045293502238]","[0.7333549537269617, -0.08702752778732076]",0
+2,4.590800393903285e-30,true,3,false,0.0003906,"[4.969793004254001, 1.1912045293502218]","[0.3348882897775347, -0.7803017039276787]",0
+3,1.0024279650486932e-30,true,3,false,0.0003002,"[4.969793004253946, 1.1912045293502251]","[1.4969905819055915, 0.35078506512446334]",0
+4,3.6860454164630258e-31,true,3,false,0.0002825,"[4.96979300425393, 1.1912045293502262]","[1.9104529355027664, 1.3598422250610078]",0
+5,5.549664479705953e-31,true,3,false,0.0003598,"[4.969793004253943, 1.1912045293502254]","[0.9728232798844213, -0.14067410011660123]",0
+6,1.4614007105013984e-30,true,3,false,0.0004908,"[4.9697930042539635, 1.191204529350224]","[-0.4584614418357323, 1.0411399628260511]",0
+7,1.1980422091934478e-30,true,3,false,0.0004401,"[4.969793004253957, 1.1912045293502245]","[0.9174389714694687, -1.5084014988779522]",0
+8,3.1674241591278934e-30,true,3,false,0.0004537,"[4.969793004253981, 1.191204529350223]","[0.12249553087948825, -0.3423467875721143]",0
+9,1.702209400015085e-31,true,4,false,0.0006301,"[4.96979300425394, 1.1912045293502254]","[-0.9204024669915305, -1.2627650563754986]",0
+10,7.968513078100302e-32,true,4,false,0.0006387,"[4.969793004253918, 1.191204529350227]","[-1.425317850889396, -0.13188760801442104]",1
+11,1.1980422091934478e-30,true,3,false,0.0006252,"[4.969793004253957, 1.1912045293502245]","[-0.15268667522019008, -0.17239262787204485]",0
+12,1.5522176898026683e-31,true,3,false,0.000804,"[4.969793004253927, 1.1912045293502265]","[1.3023574705316272, 0.5077729828299388]",0
+13,1.2764725426287269e-30,true,3,false,0.0009039,"[4.969793004253947, 1.1912045293502251]","[0.15426908206241488, 1.5402712760936221]",0
+14,5.3396474586135005e-30,true,3,false,0.0007302,"[4.969793004253985, 1.1912045293502227]","[-1.7267252812920018, 1.0219210821259985]",0
+15,1.058419851027467e-31,true,3,false,0.0007494,"[4.969793004253948, 1.191204529350225]","[0.2747757058146626, 0.1720449857423505]",0
+16,2.0380276004360407e-30,true,3,false,0.0005622,"[4.969793004253965, 1.191204529350224]","[1.0110359111683276, -0.17639294266435857]",0
+17,5.201360867700783e-31,true,4,false,0.00058,"[4.969793004253942, 1.1912045293502254]","[-2.136663990166246, -0.006788132262980114]",0
+18,1.0359523636777318e-31,true,4,false,0.0006136,"[4.969793004253933, 1.1912045293502258]","[-1.2334796855846475, 0.23491144426446822]",0
+19,4.94134966795121e-30,true,3,false,0.0004195,"[4.969793004253996, 1.191204529350222]","[-0.2143326300938984, -0.5149354181924408]",0
+20,1.4614007105013984e-30,true,3,false,0.0006631,"[4.9697930042539635, 1.191204529350224]","[-0.6336756499029971, 1.4154829458615907]",0
diff --git a/src/GMMTools.jl b/src/GMMTools.jl
@@ -12,6 +12,7 @@ using StatsAPI
 
 # for (Bayesian) bootstrap
 using StatsBase # take samples
+using StatsModels
 using Distributions # Dirichlet distribution for bootstrap
 
 using DelimitedFiles

diff --git a/src/functions_estimation.jl b/src/functions_estimation.jl
@@ -1,51 +1,3 @@
-#= 
-TODOS:
-1. add try ... catch blocks
-1. add optimizer options / variants. Where to save the optimizer-specific options? kwargs.
-1. save bootstrap weights?
-1. add parameter names in the problem object
-1. switch to PrettyTables.jl (cf Peter's suggestion)
-=#
-
-
-# Base.@kwdef mutable struct GMMProblem
-#     data
-#     cache = nothing
-#     W=I                 # weight Matrix
-#     theta0::Array
-#     weights             # weights for each observation (e.g. used in Bayesian bootstrap)
-#     theta_names::Union{Vector{String}, Nothing} = nothing
-# end
-
-# function create_GMMProblem(;
-#     data, 
-#     cache=nothing, 
-#     W=I, 
-#     theta0, 
-#     weights=nothing,
-#     theta_names::Union{Vector{String}, Nothing} = nothing)
-
-#     if isa(theta0, Vector)
-#         theta0 = Matrix(Transpose(theta0))
-#     end
-
-#     if isnothing(theta_names)
-#         nparams = size(theta0, 2)
-#         theta_names = ["θ_" * string(i) for i=1:nparams]
-#     end
-
-#     return GMMProblem(data, cache, W, theta0, weights, theta_names) 
-# end
-
-# function Base.show(io::IO, prob::GMMProblem)
-#     println("GMM Problem, fields:")
-#     println("- data")
-#     !isnothing(prob.cache) && println("- cache")
-#     println("- W = weighting matrix for GMM")
-#     !isnothing(prob.theta_names) && println("- theta_names ", prob.theta_names)
-#     println("- theta0 = initial conditions ", prob.theta0)
-#     println("- weights = observation weights ")
-# end
 
 Base.@kwdef mutable struct GMMFit
     theta0::Vector

diff --git a/src/functions_regtable.jl b/src/functions_regtable.jl
@@ -1,5 +1,5 @@
 
-Base.@kwdef struct GMMResultTable <: RegressionModel
+Base.@kwdef struct GMMModel <: RegressionModel
     coef::Vector{Float64}   # Vector of coefficients
     vcov::Matrix{Float64}   # Covariance matrix
     vcov_type::CovarianceEstimator
@@ -38,24 +38,29 @@ Base.@kwdef struct GMMResultTable <: RegressionModel
 end
 
 
-has_iv(m::GMMResultTable) = m.F_kp !== nothing
-has_fe(m::GMMResultTable) = false
-
-StatsAPI.coef(m::GMMResultTable) = m.coef
-StatsAPI.coefnames(m::GMMResultTable) = m.coefnames
-StatsAPI.responsename(m::GMMResultTable) = m.responsename
-StatsAPI.vcov(m::GMMResultTable) = m.vcov
-StatsAPI.nobs(m::GMMResultTable) = m.nobs
-StatsAPI.dof(m::GMMResultTable) = m.dof
-StatsAPI.dof_residual(m::GMMResultTable) = m.dof_residual
-StatsAPI.r2(m::GMMResultTable) = r2(m, :devianceratio)
-StatsAPI.islinear(m::GMMResultTable) = true
-StatsAPI.deviance(m::GMMResultTable) = rss(m)
-StatsAPI.nulldeviance(m::GMMResultTable) = m.tss
-StatsAPI.rss(m::GMMResultTable) = m.rss
-StatsAPI.mss(m::GMMResultTable) = nulldeviance(m) - rss(m)
+has_iv(m::GMMModel) = m.F_kp !== nothing
+has_fe(m::GMMModel) = false
+
+# RegressionTables.get_coefname(x::Tuple{Vararg{Term}}) = RegressionTables.get_coefname.(x)
+# RegressionTables.replace_name(x::Tuple{Vararg{Any}}, a::Dict{String, String}, b::Dict{String, String}) = [RegressionTables.replace_name(x[i], a, b) for i=1:length(x)]
+
+RegressionTables.formula(m::GMMModel) = term(m.responsename) ~ sum(term.(String.(m.coefnames)))
+
+StatsAPI.coef(m::GMMModel) = m.coef
+StatsAPI.coefnames(m::GMMModel) = m.coefnames
+StatsAPI.responsename(m::GMMModel) = m.responsename
+StatsAPI.vcov(m::GMMModel) = m.vcov
+StatsAPI.nobs(m::GMMModel) = m.nobs
+StatsAPI.dof(m::GMMModel) = m.dof
+StatsAPI.dof_residual(m::GMMModel) = m.dof_residual
+StatsAPI.r2(m::GMMModel) = r2(m, :devianceratio)
+StatsAPI.islinear(m::GMMModel) = true
+StatsAPI.deviance(m::GMMModel) = rss(m)
+StatsAPI.nulldeviance(m::GMMModel) = m.tss
+StatsAPI.rss(m::GMMModel) = m.rss
+StatsAPI.mss(m::GMMModel) = nulldeviance(m) - rss(m)
 # StatsModels.formula(m::GMMResultTable) = m.formula_schema
-dof_fes(m::GMMResultTable) = m.dof_fes
+dof_fes(m::GMMModel) = m.dof_fes
 
 function vcov(r::GMMFit)
     if isnothing(r.vcov)
@@ -74,7 +79,7 @@ function vcov_method(r::GMMFit)
     end
 end
 
-function GMMResultTable(r::GMMFit)
+function GMMModel(r::GMMFit)
 
     nobs = r.N
 
@@ -87,15 +92,15 @@ function GMMResultTable(r::GMMFit)
         r.theta_names = ["theta_$i" for i=1:length(r.theta_hat)]
     end
 
-    GMMResultTable(
+    GMMModel(
         coef = r.theta_hat,
         vcov = vcov(r),
         vcov_type=vcov_method(r),
         esample=[],
         fe=DataFrame(),
         fekeys=[],
         coefnames=r.theta_names,
-        responsename="",
+        responsename="a",
         # formula::FormulaTerm        # Original formula
         # formula_schema::FormulaTerm # Schema for predict
         contrasts=Dict(),
@@ -113,11 +118,11 @@ end
 
 # TODO: integrate better with RegressionModels, allow mixed inputs etc. Should be easy.
 function regtable(r::GMMFit)
-    RegressionTables.regtable(GMMResultTable(r);  
-        labels = Dict("__LABEL_ESTIMATOR_OLS__" => "GMM"), 
-        renderSettings = asciiOutput())
+    RegressionTables.regtable(GMMModel(r), render = AsciiTable())
 end
 
+        # labels = Dict("__LABEL_ESTIMATOR_OLS__" => "GMM"), 
+
 
 ### Table
 # function coef(r::GMMResult)