multiple dispatch for (X, y) style call

Project.toml

@@ -1,7 +1,7 @@
+authors = ["Mehmet Hakan Satman <[email protected]>"]
 name = "LinRegOutliers"
 uuid = "6d4de0fb-32d9-4c65-aac1-cc9ed8b94b1a"
-authors = ["Mehmet Hakan Satman <[email protected]>"]
-version = "0.3.15"
+version = "0.4.0"
 
 [deps]
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"

src/.dev/test.jl

@@ -14,6 +14,21 @@ include("outlier.jl")
     @test applyColumns(mean, mat) == [1.0, 0.0]
 end
 
+@testset "Ordinary Least Squares" begin
+    tol = 0.0001
+    #  The model is exatly y = 5 + 5x
+    var1 = Float64[1, 2, 3, 4, 5]
+    X = hcat(ones(5), var1)
+    betas = [5.0, 5.0]
+    y = X * betas
+
+    # OLS 
+    olsreg = ols(X, y)
+    @test isapprox(coef(olsreg), betas, atol=tol)
+    @test isapprox(residuals(olsreg), zeros(Float64, 5), atol=tol)
+    @test isapprox(predict(olsreg), y, atol=tol) 
+end
+
 @testset "Basis - createRegressionSetting, designMatrix, responseVector" begin
     dataset = DataFrame(
         x=[1.0, 2, 3, 4, 5],

src/LinRegOutliers.jl

@@ -121,6 +121,9 @@ export jacknifedS
 export cooks
 export mahalabonisSquaredMatrix
 
+# Ordinary least squares
+export OLS, ols, wls, residuals, predict
+
 # Algorithms
 export hs93, hs93initialset, hs93basicsubset
 export ks89

test/runtests.jl

@@ -15,6 +15,21 @@ using LinRegOutliers
     @test applyColumns(mean, mat) == [1.0, 0.0]
 end
 
+@testset "Ordinary Least Squares" begin
+    tol = 0.0001
+    #  The model is exatly y = 5 + 5x
+    var1 = Float64[1, 2, 3, 4, 5]
+    X = hcat(ones(5), var1)
+    betas = [5.0, 5.0]
+    y = X * betas
+
+    # OLS 
+    olsreg = ols(X, y)
+    @test isapprox(coef(olsreg), betas, atol=tol)
+    @test isapprox(residuals(olsreg), zeros(Float64, 5), atol=tol)
+    @test isapprox(predict(olsreg), y, atol=tol) 
+end
+
 @testset "Basis - createRegressionSetting, designMatrix, responseVector" begin
     dataset = DataFrame(
         x=[1.0, 2, 3, 4, 5],