Update to FillArrays 0.12 (#45)

* Update Project.toml

* Update Project.toml

* Update fit.jl

* Update InteractiveFixedEffectModels.jl

* Update fit.jl

* Update interactivefixedeffectsmodel.jl

Author: matthieugomez

Project.toml

@@ -7,6 +7,7 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FixedEffectModels = "9d5cd8c9-2029-5cab-9928-427838db53e3"
 FixedEffects = "c8885935-8500-56a7-9867-7708b20db0eb"
+GroupedArrays = "6407cd72-fade-4a84-8a1e-56e431fc1533"
 LeastSquaresOptim = "0fc2ff8b-aaa3-5acd-a817-1944a5e08891"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -20,9 +21,10 @@ Vcov = "ec2bfdc2-55df-4fc9-b9ae-4958c2cf2486"
 
 [compat]
 DataFrames = "0.21, 0.22, 1.0"
-FillArrays = "0.7, 0.8, 0.9, 0.10, 0.11"
+FillArrays = "0.7, 0.8, 0.9, 0.10, 0.11, 0.12"
 FixedEffectModels = "1.5"
 FixedEffects = "2"
+GroupedArrays = "0.3"
 LeastSquaresOptim = "0.7, 0.8"
 Reexport = "0.2, 1.0"
 StatsBase = "0.33"

src/InteractiveFixedEffectModels.jl

@@ -7,6 +7,7 @@ module InteractiveFixedEffectModels
 using DataFrames
 using FillArrays
 using FixedEffects
+using GroupedArrays
 using LeastSquaresOptim
 using LinearAlgebra
 using Printf
@@ -42,4 +43,4 @@ include("utils/formula.jl")
 include("methods/gauss_seidel.jl")
 include("methods/ls.jl")
 include("fit.jl")
-end
+end

src/fit.jl

@@ -86,8 +86,8 @@ function regife(
     converged = false
     # get two dimensions
 
-    id = FixedEffects.group(df[esample, m.id])
-    time = FixedEffects.group(df[esample, m.time])
+    id = GroupedArray(df[esample, m.id])
+    time = GroupedArray(df[esample, m.time])
 
     ##############################################################################
     ##
@@ -135,26 +135,26 @@ function regife(
     ##############################################################################
 
     # initialize factor models at 0.1
-    idpool = fill(0.1, id.n, m.rank)
-    timepool = fill(0.1, time.n, m.rank)
+    idpool = fill(0.1, id.ngroups, m.rank)
+    timepool = fill(0.1, time.ngroups, m.rank)
 
     y .= y .* sqrtw 
     X .= X .* sqrtw
     if !has_regressors
         # factor model 
-        fp = FactorModel(y, sqrtw, id.refs, time.refs, m.rank)
+        fp = FactorModel(y, sqrtw, id.groups, time.groups, m.rank)
         fs = FactorSolution(idpool, timepool)
         (fs, iterations, converged) = 
             fit!(Val{method}, fp, fs; maxiter = maxiter, tol = tol, lambda = lambda)
     else 
         # interactive fixed effect
         coef = X \ y
-        fp = FactorModel(y - X * coef, sqrtw, id.refs, time.refs, m.rank)
+        fp = FactorModel(y - X * coef, sqrtw, id.groups, time.groups, m.rank)
         fs = FactorSolution(idpool, timepool)
         fit!(Val{:levenberg_marquardt}, fp, fs; maxiter = 100, tol = 1e-3, lambda = lambda)
 
         fs = InteractiveFixedEffectsSolution(coef, fs.idpool, fs.timepool)
-        fp = InteractiveFixedEffectsModel(y, sqrtw, X, id.refs, time.refs, m.rank)
+        fp = InteractiveFixedEffectsModel(y, sqrtw, X, id.groups, time.groups, m.rank)
 
 
         ym = copy(y)
@@ -193,7 +193,7 @@ function regife(
     ##############################################################################
 
     # compute residuals
-    fp = FactorModel(copy(y), sqrtw, id.refs, time.refs, m.rank)
+    fp = FactorModel(copy(y), sqrtw, id.groups, time.groups, m.rank)
     if has_regressors
         LinearAlgebra.BLAS.gemm!('N', 'N', -1.0, X, fs.b, 1.0, fp.y)
     end
@@ -261,7 +261,7 @@ function regife(
             if has_regressors
                 LinearAlgebra.BLAS.gemm!('N', 'N', -1.0, oldX, coef, 1.0, oldresiduals)
             end
-            fp = FactorModel(oldresiduals, sqrtw, id.refs, time.refs, m.rank)
+            fp = FactorModel(oldresiduals, sqrtw, id.groups, time.groups, m.rank)
             subtract_factor!(fp, fs)
             axpy!(-1.0, residuals, oldresiduals)
             # get fixed effect

test/interactivefixedeffectsmodel.jl

@@ -61,7 +61,7 @@ for method in [:dogleg, :levenberg_marquardt, :gauss_seidel]
 end
 
 # check high dimentional fixed effects are part of factor models
-df.State2 = categorical(df.State)
+df.State2 = deepcopy(df.State)
 @test_throws ErrorException regife(df, @formula(Sales ~ Price + ife(State, Year, 2) + fe(State2)))
 
 
@@ -74,8 +74,6 @@ for method in [:levenberg_marquardt, :dogleg]
 	println(method)
 
 	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)
@@ -84,18 +82,16 @@ for method in [:levenberg_marquardt, :dogleg]
 	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)
-	df.pid2 = categorical(df.id2)
 	df.y = df.Wage
 	df.x1 = df.Emp
 	df.w = df.Output
-	model = @formula y ~ x1 + ife(pid1, pid2, 2) 
+	model = @formula y ~ x1 + ife(id1, id2, 2) 
 	result = regife(df, model, method = method, save = true)
 	@test norm(result.coef ./ [4.53965, -0.0160858] .- 1) < precision
-	model = @formula y ~ x1 + ife(pid1, pid2, 2)
+	model = @formula y ~ x1 + ife(id1, id2, 2)
 	result = regife(df, model, weights = :w, method = method, save = true)
 	@test norm(result.coef ./ [3.47551,-0.017366] .- 1) < precision
-	model = @formula y ~ x1 + ife(pid1, pid2, 1)
+	model = @formula y ~ x1 + ife(id1, id2, 1)
 	result = regife(df, model, weights = :w, method = method, save = true)
 	@test norm(result.coef ./ [ -2.62105, -0.0470005] .- 1) < precision
 end