avoid X'y issue (#255)

* Update fit.jl

* Update predict.jl

* Update Project.toml

* Update fit.jl

* Update fit.jl

Author: matthieugomez

Project.toml

@@ -1,6 +1,6 @@
 name = "FixedEffectModels"
 uuid = "9d5cd8c9-2029-5cab-9928-427838db53e3"
-version = "1.10.0"
+version = "1.10.1"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

src/fit.jl

@@ -424,7 +424,8 @@ function StatsAPI.fit(::Type{FixedEffectModel},
     ## Do the regression
     ##
     ##############################################################################
-    Xy = Symmetric(hvcat(2, XhatXhat, Xhat'y, 
+    # use X'Y instead of X'y because of https://github.com/FixedEffects/FixedEffectModels.jl/issues/249
+    Xy = Symmetric(hvcat(2, XhatXhat, Xhat'reshape(y, length(y), 1), 
                          zeros(size(Xhat, 2))', [0.0]))
     invsym!(Xy; diagonal = 1:size(Xhat, 2))
     invXhatXhat = Symmetric(.- Xy[1:(end-1),1:(end-1)])

test/predict.jl

@@ -66,7 +66,7 @@ end
     pred = predict(m, df)
     @test pred isa Vector{Union{Missing, Float64}}
     @test ismissing(pred[1])
-    @test pred[2:end] ≈ df.y[2:end]
+    @test norm(pred[2:end] .- df.y[2:end]) < 1e-6
 
     # Two groups + missing observation of non-FE
     df = DataFrame(x = [missing; rand(99)], g = rand(["a", "b"], 100))