Extend response and modelmatrix from StatsAPI

src/Loess.jl

@@ -1,11 +1,11 @@
 module Loess
 
 import Distances: euclidean
-import StatsAPI: fitted, predict, residuals
+import StatsAPI: fitted, modelmatrix, predict, residuals, response
 
 using Statistics, LinearAlgebra
 
-export loess, fitted, predict, residuals
+export loess, fitted, modelmatrix, predict, residuals, response
 
 include("kd.jl")
 
@@ -17,6 +17,10 @@ struct LoessModel{T <: AbstractFloat}
     kdtree::KDTree{T}
 end
 
+modelmatrix(model::LoessModel) = model.xs
+
+response(model::LoessModel) = model.ys
+
 """
     loess(xs, ys; normalize=true, span=0.75, degree=2)
 
@@ -199,9 +203,9 @@ function predict(model::LoessModel, zs::AbstractMatrix)
     end
 end
 
-fitted(model::LoessModel) = predict(model, model.xs)
+fitted(model::LoessModel) = predict(model, modelmatrix(model))
 
-residuals(model::LoessModel) = fitted(model) .- model.ys
+residuals(model::LoessModel) = fitted(model) .- response(model)
 
 """
     tricubic(u)

test/runtests.jl

@@ -22,6 +22,8 @@ using RDatasets
     x = [13.0,14.0,14.35,15.0,16.0]
     y = [0.369486,  0.355579, 0.3545, 0.356952, 0.36883]
     model = loess(x,y)
+    @test response(model) == y
+    @test modelmatrix(model) == reshape(x, :, 1)
     @test fitted(model) ≈ y
     @test all(isapprox(0; atol=1e-12), residuals(model))
 end