v1.0.0 (#25)

* v1.0.0

* Update Project.toml

* Update README.md

* Update fit.jl

* Update InteractiveFixedEffectModels.jl

* update

* update

Author: matthieugomez

.travis.yml

@@ -1,8 +1,8 @@
 language: julia
 julia:
-- 1.0
-- 1.1
-- 1.2
+- 1.3
+- 1.4
+- 1.5
 - nightly
 script:
 -  if [[ -a .git/shallow ]]; then git fetch --unshallow; fi

Project.toml

@@ -1,6 +1,6 @@
 name = "InteractiveFixedEffectModels"
 uuid = "80307280-efb2-5c5d-af8b-a9c15821677b"
-version = "0.6.4"
+version = "1.0.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -21,14 +21,14 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 DataFrames = "0.19, 0.20, 0.21"
 Distributions = "0"
 FillArrays = "0"
-FixedEffectModels = "0.10.3, 0.10.4, 0.11"
-FixedEffects = "0.7"
+FixedEffectModels = "0.11, 1"
+FixedEffects = "0.7, 1"
 LeastSquaresOptim = "0.7"
 Reexport = "0"
 StatsBase = "0"
 StatsModels = "0.6"
 Tables = "0, 1"
-julia = "1"
+julia = "1.3"
 
 [extras]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

README.md

@@ -17,16 +17,16 @@ The definition of interactive fixed effects follows Bai (2009).Formally, denote
 using DataFrames, RDatasets, InteractiveFixedEffectModels
 df = dataset("plm", "Cigar")
 regife(df, @formula(Sales ~ Price + ife(State, Year, 2) + fe(State)))
-#                      Linear Factor Model                      
-#================================================================
-#Number of obs:             1380  Degree of freedom:          199
-#R2:                       0.976  R2 within:                0.435
-#Iterations:                 436  Converged:                 true
-#================================================================
-#        Estimate Std.Error  t value Pr(>|t|) Lower 95% Upper 95%
-#----------------------------------------------------------------
-#Price  -0.425372 0.0141163 -30.1334    0.000 -0.453068 -0.397677
-#================================================================
+                 Interactive Fixed Effect Model
+================================================================
+Number of obs:             1380  Degree of freedom:           47
+R2:                       0.976  R2 within:                0.435
+Iterations:                 436  Converged:                 true
+================================================================
+        Estimate Std.Error  t value Pr(>|t|) Lower 95% Upper 95%
+----------------------------------------------------------------
+Price  -0.425372 0.0132871 -32.0139    0.000 -0.451438 -0.399306
+================================================================
 ```
 
 

src/InteractiveFixedEffectModels.jl

@@ -5,13 +5,10 @@ module InteractiveFixedEffectModels
 ##
 ##############################################################################
 
-
 using Base
 using LinearAlgebra
 using Statistics
 using Printf
-
-
 using StatsBase
 using StatsModels
 using Tables
@@ -23,10 +20,6 @@ using Reexport
 using FixedEffects
 @reexport using FixedEffectModels
 
-
-if !isdefined(FixedEffectModels, :ModelTerm)
-    ModelTerm = Model
-end
 ##############################################################################
 ##
 ## Exported methods and types 
@@ -46,5 +39,4 @@ include("types.jl")
 include("methods/gauss_seidel.jl")
 include("methods/ls.jl")
 include("fit.jl")
-include("deprecated.jl")
 end

src/deprecated.jl

@@ -1,12 +1,4 @@
 
-##############################################################################
-##
-## Fit is the only exported function
-##
-##############################################################################
-function regife(df, m::FixedEffectModels.ModelTerm; kwargs...)
-    regife(df, m.f; m.dict..., kwargs...)
-end
 
 function regife(df, f::FormulaTerm, vcov::CovarianceEstimator = Vcov.simple();
             weights::Union{Symbol, Nothing} = nothing, 
@@ -16,56 +8,26 @@ function regife(df, f::FormulaTerm, vcov::CovarianceEstimator = Vcov.simple();
              maxiter::Integer = 10_000, 
              tol::Real = 1e-9, 
              save::Union{Bool, Nothing} = nothing,
-             contrasts::Dict = Dict{Symbol, Any}(),
-             feformula::Union{Symbol, Expr, Nothing} = nothing,
-            ifeformula::Union{Symbol, Expr, Nothing} = nothing,
-            vcovformula::Union{Symbol, Expr, Nothing} = nothing,
-            subsetformula::Union{Symbol, Expr, Nothing} = nothing)
+             contrasts::Dict = Dict{Symbol, Any}())
 
     ##############################################################################
     ##
     ## Transform DataFrame -> Matrix
     ##
     ##############################################################################
     df = DataFrame(df; copycols = false)
-
-    # to deprecate
-    if vcovformula != nothing
-        if (vcovformula == :simple) | (vcovformula == :(simple()))
-            vcov = Vcov.Simple()
-        elseif (vcovformula == :robust) | (vcovformula == :(robust()))
-            vcov = Vcov.Robust()
-        else
-            vcov = Vcov.cluster(StatsModels.termvars(@eval(@formula(0 ~ $(vcovformula.args[2]))))...)
-        end
-    end
-    if subsetformula != nothing
-        subset = eval(evaluate_subset(df, subsetformula))
-    end
-
     if  (ConstantTerm(0) ∉ FixedEffectModels.eachterm(f.rhs)) & (ConstantTerm(1) ∉ FixedEffectModels.eachterm(f.rhs))
         formula = FormulaTerm(f.lhs, tuple(ConstantTerm(1), FixedEffectModels.eachterm(f.rhs)...))
     end
 
     formula, formula_endo, formula_iv = FixedEffectModels.parse_iv(f)
 
     m, formula = parse_interactivefixedeffect(df, formula)
-    if ifeformula != nothing # remove after depreciation
-        m = OldInteractiveFixedEffectFormula(ifeformula)
-    end
-
-    ## parse formula 
-    if formula_iv != nothing
-        error("partial_out does not support instrumental variables")
-    end
     has_weights = (weights != nothing)
 
 
     ## create a dataframe without missing values & negative weightss
     vars = StatsModels.termvars(formula)
-    if feformula != nothing # remove after depreciation
-        vars = vcat(vars, StatsModels.termvars(@eval(@formula(0 ~ $(feformula)))))
-    end
     factor_vars = [m.id, m.time]
     all_vars = unique(vcat(vars, factor_vars))
     esample = completecases(df[!, all_vars])
@@ -101,10 +63,6 @@ function regife(df, f::FormulaTerm, vcov::CovarianceEstimator = Vcov.simple();
        end
     end
     fes, ids, formula = FixedEffectModels.parse_fixedeffect(df, formula)
-    if feformula != nothing # remove after depreciation
-        feformula = @eval(@formula(0 ~ $(feformula)))
-        fes, ids = FixedEffectModels.oldparse_fixedeffect(df, feformula)
-    end 
     has_fes = !isempty(fes)
     has_fes_intercept = false
     ## Compute factors, an array of AbtractFixedEffects

src/fit.jl

@@ -1,5 +1,5 @@
 using DataFrames, InteractiveFixedEffectModels,Test, CSV
-df = df = CSV.read(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv"))
+df = df = DataFrame(CSV.File(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv")))
 
 for method in [:gauss_seidel,  :dogleg, :levenberg_marquardt]
 	println(method)

test/factormodel.jl

@@ -1,7 +1,7 @@
 using DataFrames, InteractiveFixedEffectModels, CSV, LinearAlgebra, Test
 
 precision = 2e-1
-df = CSV.read(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv"))
+df = DataFrame(CSV.File(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv")))
 
 
 
@@ -73,15 +73,15 @@ precision = 2e-1
 for method in [:levenberg_marquardt, :dogleg]
 	println(method)
 
-	df = CSV.read(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv"))
+	local df = DataFrame(CSV.File(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/Cigar.csv")))
 	df.State = categorical(df.State)
 	df.Year = categorical(df.Year)
 	model = @formula Sales ~ Price + ife(State, Year, 1) + fe(State)
 	result = regife(df, model, weights = :Pop, method = method, save = true)
 	model = @formula Sales ~ Price + ife(State, Year, 2) + fe(State)
 	result = regife(df, model, weights = :Pop, method = method, save = true)
 
-	df = CSV.read(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/EmplUK.csv"))
+	local df = DataFrame(CSV.File(joinpath(dirname(pathof(InteractiveFixedEffectModels)), "../dataset/EmplUK.csv")))
 	df.id1 = df.Firm
 	df.id2 = df.Year
 	df.pid1 = categorical(df.id1)