Filip

test_filip.jl

using LinearRegressionKit, StatsModels, DataFrames, CSV

y = [0.8116, 0.9072, 0.9052, 0.9039, 0.8053, 0.8377, 0.8667, 0.8809, 0.7975, 0.8162, 0.8515, 0.8766, 0.8885, 0.8859, 0.8959, 0.8913, 0.8959, 0.8971, 0.9021, 0.909, 0.9139, 0.9199, 0.8692, 0.8872, 0.89, 0.891, 0.8977, 0.9035, 0.9078, 0.7675, 0.7705, 0.7713, 0.7736, 0.7775, 0.7841, 0.7971, 0.8329, 0.8641, 0.8804, 0.7668, 0.7633, 0.7678, 0.7697, 0.77, 0.7749, 0.7796, 0.7897, 0.8131, 0.8498, 0.8741, 0.8061, 0.846, 0.8751, 0.8856, 0.8919, 0.8934, 0.894, 0.8957, 0.9047, 0.9129, 0.9209, 0.9219, 0.7739, 0.7681, 0.7665, 0.7703, 0.7702, 0.7761, 0.7809, 0.7961, 0.8253, 0.8602, 0.8809, 0.8301, 0.8664, 0.8834, 0.8898, 0.8964, 0.8963, 0.9074, 0.9119, 0.9228]
x = [-6.860120914, -4.324130045, -4.358625055, -4.358426747, -6.955852379, -6.661145254, -6.355462942, -6.118102026, -7.115148017, -6.815308569, -6.519993057, -6.204119983, -5.853871964, -6.109523091, -5.79832982, -5.482672118, -5.171791386, -4.851705903, -4.517126416, -4.143573228, -3.709075441, -3.499489089, -6.300769497, -5.953504836, -5.642065153, -5.031376979, -4.680685696, -4.329846955, -3.928486195, -8.56735134, -8.363211311, -8.107682739, -7.823908741, -7.522878745, -7.218819279, -6.920818754, -6.628932138, -6.323946875, -5.991399828, -8.781464495, -8.663140179, -8.473531488, -8.247337057, -7.971428747, -7.676129393, -7.352812702, -7.072065318, -6.774174009, -6.478861916, -6.159517513, -6.835647144, -6.53165267, -6.224098421, -5.910094889, -5.598599459, -5.290645224, -4.974284616, -4.64454848, -4.290560426, -3.885055584, -3.408378962, -3.13200249, -8.726767166, -8.66695597, -8.511026475, -8.165388579, -7.886056648, -7.588043762, -7.283412422, -6.995678626, -6.691862621, -6.392544977, -6.067374056, -6.684029655, -6.378719832, -6.065855188, -5.752272167, -5.132414673, -4.811352704, -4.098269308, -3.66174277, -3.2644011]

df = DataFrame(x= x, y= y)

f = @formula(y ~ x + x^2 + x^3 + x^4 + x^5 + x^6 + x^7 + x^8 + x^9 + x^10)
lrk= regress(f, df, req_stats=["default"])
lrk


delta(a,b) = abs(a-b) ≈ 0. ?  0. : abs(a-b)

println("Filip - LRK")

println(string("delta B0 ", delta(0.673565789473684E-03, lrk.coefs[1])))
println(string("delta B1 ", delta(0.732059160401003E-06, lrk.coefs[2])))
println(string("delta B2 ", delta(-0.316081871345029E-14, lrk.coefs[3])))
println(string("delta std err B0 ", delta(0.107938612033077E-03, lrk.stderrors[1])))
println(string("delta std err B1 ", delta(0.157817399981659E-09, lrk.stderrors[2])))
println(string("delta std err B2 ", delta(0.486652849992036E-16, lrk.stderrors[3])))
println(string("delta resid std dev ", delta(0.205177424076185E-03, lrk.RMSE)))
println(string("delta R2 ", delta(0.999999900178537, lrk.R2)))

using GLM
lr = GLM.lm(f, df)

println("Filip - GLM")
println(string("delta B0 ", delta(0.673565789473684E-03, coef(lr)[1])))
println(string("delta B1 ", delta(0.732059160401003E-06, coef(lr)[2])))
println(string("delta B2 ", delta(-0.316081871345029E-14, coef(lr)[3])))
println(string("delta std err B0 ", delta(0.107938612033077E-03, stderror(lr)[1])))
println(string("delta std err B1 ", delta(0.157817399981659E-09, stderror(lr)[2])))
println(string("delta std err B2 ", delta(0.486652849992036E-16, stderror(lr)[3])))
println(string("delta resid std dev ", delta(0.205177424076185E-03, deviance(lr)/dof_residual(lr))))
println(string("delta R2 ", delta(0.999999900178537, r2(lr))))