Use Vcov + correct partial_out with weights + allow nthreads option (#124)

* add align kwarg

* Update partial_out.jl

* update to 1.2

* Update README.md

* Update README.md

Author: matthieugomez

Project.toml

@@ -1,41 +1,36 @@
 name = "FixedEffectModels"
 uuid = "9d5cd8c9-2029-5cab-9928-427838db53e3"
-version = "1.1.0"
+version = "1.2.0"
 
 [deps]
-Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 FixedEffects = "c8885935-8500-56a7-9867-7708b20db0eb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+Vcov = "ec2bfdc2-55df-4fc9-b9ae-4958c2cf2486"
 
 [compat]
-Combinatorics = "0, 1"
 DataFrames = "0.21"
-Distributions = "0"
-FillArrays = "0"
-FixedEffects = "1"
-Reexport = "0"
-StatsBase = "0.32, 0.33"
+FixedEffects = "1.1"
+Reexport = "0.1, 0.2"
+StatsBase = "0.33"
+StatsFuns = "0.9"
 StatsModels = "0.6"
 Tables = "1"
+Vcov = "0.3"
 julia = "1.3"
 
 [extras]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["CSV", "CUDA", "LinearAlgebra", "Random", "Statistics", "Test"]
+test = ["CSV", "CUDA", "Random", "Test"]

README.md

@@ -51,7 +51,7 @@ reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State), weig
 	reg(df, term(:Sales) ~ term(:NDI) + fe(:State) + fe(:Year))
 	```
 
-- Standard errors are indicated with the prefix `Vcov`.
+- Standard errors are indicated with the prefix `Vcov` (with the package [Vcov](http://github.com/matthieugomez/Vcov.jl))
 	```julia
 	Vcov.robust()
 	Vcov.cluster(:State)
@@ -61,11 +61,8 @@ reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), Vcov.cluster(:State), weig
 	```julia
 	weights = :Pop
 	```
-- The option `subset` specifies a subset of the data
-	```julia
-	subset = df.State .>= 30
-	```
-- The option `save` can be set to one of the following:  `:residuals` to save residuals, `:fe` to save fixed effects, `true` to save both. You can access the result with `residuals()` and `fe()`
+
+- The option `save` can be set to one of the following:  `none` (default) to save nothing `:residuals` to save residuals, `:fe` to save fixed effects. You can access the result with `residuals()` and `fe()`
 
 - The option `method` can be set to one of the following: `:cpu`, `:gpu` (see Performances below).
 
@@ -106,9 +103,9 @@ reg(df, @formula(Sales ~ NDI + fe(State) + fe(Year)), method = :gpu, double_prec
 ## Solution Method
 Denote the model `y = X β + D θ + e` where X is a matrix with few columns and D is the design matrix from categorical variables. Estimates for `β`, along with their standard errors, are obtained in two steps:
 
-1. `y, X`  are regressed on `D` using the package [FixedEffects.jl](https://github.com/matthieugomez/FixedEffects.jl)
+1. `y, X`  are regressed on `D` using the package [FixedEffects.jl](https://github.com/FixedEffects/FixedEffects.jl)
 2.  Estimates for `β`, along with their standard errors, are obtained by regressing the projected `y` on the projected `X` (an application of the Frisch Waugh-Lovell Theorem)
-3. With the option `save = true`, estimates for the high dimensional fixed effects are obtained after regressing the residuals of the full model minus the residuals of the partialed out models on `D` using the package [FixedEffects.jl](https://github.com/matthieugomez/FixedEffects.jl)
+3. With the option `save = true`, estimates for the high dimensional fixed effects are obtained after regressing the residuals of the full model minus the residuals of the partialed out models on `D` using the package [FixedEffects.jl](https://github.com/FixedEffects/FixedEffects.jl)
 
 # References
 

src/FixedEffectModel.jl

@@ -63,7 +63,7 @@ StatsBase.mss(x::FixedEffectModel) = deviance(x) - rss(x)
 
 
 function StatsBase.confint(x::FixedEffectModel)
-    scale = quantile(TDist(dof_residual(x)), 1 - (1-0.95)/2)
+    scale = tdistinvcdf(dof_residual(x), 1 - (1 - 0.95) / 2)
     se = stderror(x)
     hcat(x.coef -  scale * se, x.coef + scale * se)
 end
@@ -172,7 +172,7 @@ function StatsBase.coeftable(x::FixedEffectModel)
     end
     tt = cc ./ se
     CoefTable2(
-        hcat(cc, se, tt, ccdf.(Ref(FDist(1, dof_residual(x))), abs2.(tt)), conf_int[:, 1:2]),
+        hcat(cc, se, tt, fdistccdf.(Ref(1), Ref(dof_residual(x)), 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, ctitle, ctop)
 end

src/FixedEffectModels.jl

@@ -1,54 +1,42 @@
 
 module FixedEffectModels
 
-##############################################################################
-##
-## Dependencies
-##
-##############################################################################
+if isdefined(Base, :Experimental) && isdefined(Base.Experimental, Symbol("@optlevel"))
+	@eval Base.Experimental.@optlevel 1
+end
+
+using DataFrames
+using FixedEffects
 using LinearAlgebra
-using Statistics
 using Printf
-using FillArrays
-using DataFrames
-using Distributions
 using Reexport
-using Tables
-using FixedEffects
-@reexport using StatsBase
+using Statistics
+using StatsBase
+using StatsFuns
 @reexport using StatsModels
-
-##############################################################################
-##
-## Exported methods and types
-##
-##############################################################################
-
-export reg,
-partial_out,
-fe,
-FixedEffectModel,
-has_iv,
-has_fe,
-Vcov
-
-##############################################################################
-##
-## Load files
-##
-##############################################################################
-include("vcov/Vcov.jl")
+using Tables
+using Vcov
 
 include("utils/fixedeffects.jl")
 include("utils/basecol.jl")
 include("utils/tss.jl")
-include("utils/ranktest.jl")
 include("utils/formula.jl")
-
-
 include("FixedEffectModel.jl")
 include("fit.jl")
 include("partial_out.jl")
 
+include("precompile.jl")
+_precompile_()
+
+# Export from StatsBase
+export coef, coefnames, responsename, vcov, stderror, nobs, dof_residual, r2, adjr2, islinear, deviance, rss, mss, confint, predict, residuals
+
+export reg,
+partial_out,
+fe,
+FixedEffectModel,
+has_iv,
+has_fe,
+Vcov
 
-end  # module FixedEffectModels
+end