# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #------------------------------------------------------------------------------ # R source file to validate OLS multiple regression tests in # org.apache.commons.math.stat.regression.OLSMultipleLinearRegressionTest # # To run the test, install R, put this file and testFunctions # into the same directory, launch R from this directory and then enter # source("") # #------------------------------------------------------------------------------ tol <- 1E-8 # default error tolerance for tests #------------------------------------------------------------------------------ # Function definitions source("testFunctions") # utility test functions options(digits=16) # override number of digits displayed # function to verify OLS computations verifyRegression <- function(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expecteAdjRSquare, modelName) { betaHat <- as.vector(coefficients(model)) residuals <- as.vector(residuals(model)) errors <- as.vector(as.matrix(coefficients(summary(model)))[,2]) stdError <- summary(model)$sigma rSquare <- summary(model)$r.squared adjRSquare <- summary(model)$adj.r.squared output <- c("Parameter test dataset = ", modelName) if (assertEquals(expectedBeta,betaHat,tol,"Parameters")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } output <- c("Residuals test dataset = ", modelName) if (assertEquals(expectedResiduals,residuals,tol,"Residuals")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } output <- c("Errors test dataset = ", modelName) if (assertEquals(expectedErrors,errors,tol,"Errors")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } output <- c("Standard Error test dataset = ", modelName) if (assertEquals(expectedStdError,stdError,tol,"Regression Standard Error")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } output <- c("RSquared test dataset = ", modelName) if (assertEquals(expectedRSquare,rSquare,tol,"RSquared")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } output <- c("Adjusted RSquared test dataset = ", modelName) if (assertEquals(expecteAdjRSquare,adjRSquare,tol,"Adjusted RSquared")) { displayPadded(output, SUCCEEDED, WIDTH) } else { displayPadded(output, FAILED, WIDTH) } } #-------------------------------------------------------------------------- cat("Multiple regression OLS test cases\n") # Perfect fit x1 <- c(0,2,0,0,0,0) x2 <- c(0,0,3,0,0,0) x3 <- c(0,0,0,4,0,0) x4 <- c(0,0,0,0,5,0) x5 <- c(0,0,0,0,0,6) y <- c(11, 12, 13, 14, 15, 16) model <- lm(y ~ x1 + x2 + x3 + x4 + x5) expectedBeta <- c(11.0,0.5,0.666666666666667,0.75,0.8,0.8333333333333333) expectedResiduals <- c(0,0,0,0,0,0) expectedErrors <- c(NaN,NaN,NaN,NaN,NaN,NaN) expectedStdError <- NaN expectedRSquare <- 1 expectedAdjRSquare <- NaN verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expectedAdjRSquare, "perfect fit") # Longley # # Data Source: J. Longley (1967) "An Appraisal of Least Squares Programs for the # Electronic Computer from the Point of View of the User", # Journal of the American Statistical Association, # vol. 62. September, pp. 819-841. # # Certified values (and data) are from NIST: # http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat # design <- matrix(c(60323,83.0,234289,2356,1590,107608,1947, 61122,88.5,259426,2325,1456,108632,1948, 60171,88.2,258054,3682,1616,109773,1949, 61187,89.5,284599,3351,1650,110929,1950, 63221,96.2,328975,2099,3099,112075,1951, 63639,98.1,346999,1932,3594,113270,1952, 64989,99.0,365385,1870,3547,115094,1953, 63761,100.0,363112,3578,3350,116219,1954, 66019,101.2,397469,2904,3048,117388,1955, 67857,104.6,419180,2822,2857,118734,1956, 68169,108.4,442769,2936,2798,120445,1957, 66513,110.8,444546,4681,2637,121950,1958, 68655,112.6,482704,3813,2552,123366,1959, 69564,114.2,502601,3931,2514,125368,1960, 69331,115.7,518173,4806,2572,127852,1961, 70551,116.9,554894,4007,2827,130081,1962), nrow = 16, ncol = 7, byrow = TRUE) y <- design[,1] x1 <- design[,2] x2 <- design[,3] x3 <- design[,4] x4 <- design[,5] x5 <- design[,6] x6 <- design[,7] model <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6) estimates <- matrix(c(-3482258.63459582,890420.383607373, 15.0618722713733,84.9149257747669, -0.358191792925910E-01,0.334910077722432E-01, -2.02022980381683,0.488399681651699, -1.03322686717359,0.214274163161675, -0.511041056535807E-01,0.226073200069370, 1829.15146461355,455.478499142212), nrow = 7, ncol = 2, byrow = TRUE) expectedBeta <- estimates[,1] expectedErrors <- estimates[,2] expectedResiduals <- c(267.340029759711,-94.0139423988359,46.28716775752924, -410.114621930906,309.7145907602313,-249.3112153297231,-164.0489563956039, -13.18035686637081,14.30477260005235,455.394094551857,-17.26892711483297, -39.0550425226967,-155.5499735953195,-85.6713080421283,341.9315139607727, -206.7578251937366) expectedStdError <- 304.8540735619638 expectedRSquare <- 0.995479004577296 expectedAdjRSquare <- 0.992465007628826 verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expectedAdjRSquare, "Longley") # Model with no intercept model <- lm(y ~ 0 + x1 + x2 + x3 + x4 + x5 + x6) estimates <- matrix(c(-52.99357013868291, 129.54486693117232, 0.07107319907358, 0.03016640003786, -0.42346585566399, 0.41773654056612, -0.57256866841929, 0.27899087467676, -0.41420358884978, 0.32128496193363, 48.41786562001326, 17.68948737819961), nrow = 6, ncol = 2, byrow = TRUE) expectedBeta <- estimates[,1] expectedErrors <- estimates[,2] expectedResiduals <- c(279.90274927293092, -130.32465380836874, 90.73228661967445, -401.31252201634948, -440.46768772620027, -543.54512853774793, 201.32111639536299, 215.90889365977932, 73.09368242049943, 913.21694494481869, 424.82484953610174, -8.56475876776709, -361.32974610842876, 27.34560497213464, 151.28955976355002, -492.49937355336846) expectedStdError <- 475.1655079819517 expectedRSquare <- 0.9999670130706 expectedAdjRSquare <- 0.999947220913 verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expectedAdjRSquare, "Longley No Intercept") # Swiss Fertility (R dataset named "swiss") design <- matrix(c(80.2,17.0,15,12,9.96, 83.1,45.1,6,9,84.84, 92.5,39.7,5,5,93.40, 85.8,36.5,12,7,33.77, 76.9,43.5,17,15,5.16, 76.1,35.3,9,7,90.57, 83.8,70.2,16,7,92.85, 92.4,67.8,14,8,97.16, 82.4,53.3,12,7,97.67, 82.9,45.2,16,13,91.38, 87.1,64.5,14,6,98.61, 64.1,62.0,21,12,8.52, 66.9,67.5,14,7,2.27, 68.9,60.7,19,12,4.43, 61.7,69.3,22,5,2.82, 68.3,72.6,18,2,24.20, 71.7,34.0,17,8,3.30, 55.7,19.4,26,28,12.11, 54.3,15.2,31,20,2.15, 65.1,73.0,19,9,2.84, 65.5,59.8,22,10,5.23, 65.0,55.1,14,3,4.52, 56.6,50.9,22,12,15.14, 57.4,54.1,20,6,4.20, 72.5,71.2,12,1,2.40, 74.2,58.1,14,8,5.23, 72.0,63.5,6,3,2.56, 60.5,60.8,16,10,7.72, 58.3,26.8,25,19,18.46, 65.4,49.5,15,8,6.10, 75.5,85.9,3,2,99.71, 69.3,84.9,7,6,99.68, 77.3,89.7,5,2,100.00, 70.5,78.2,12,6,98.96, 79.4,64.9,7,3,98.22, 65.0,75.9,9,9,99.06, 92.2,84.6,3,3,99.46, 79.3,63.1,13,13,96.83, 70.4,38.4,26,12,5.62, 65.7,7.7,29,11,13.79, 72.7,16.7,22,13,11.22, 64.4,17.6,35,32,16.92, 77.6,37.6,15,7,4.97, 67.6,18.7,25,7,8.65, 35.0,1.2,37,53,42.34, 44.7,46.6,16,29,50.43, 42.8,27.7,22,29,58.33), nrow = 47, ncol = 5, byrow = TRUE) y <- design[,1] x1 <- design[,2] x2 <- design[,3] x3 <- design[,4] x4 <- design[,5] model <- lm(y ~ x1 + x2 + x3 + x4) estimates <- matrix(c(91.05542390271397,6.94881329475087, -0.22064551045715,0.07360008972340, -0.26058239824328,0.27410957467466, -0.96161238456030,0.19454551679325, 0.12441843147162,0.03726654773803), nrow = 5, ncol = 2, byrow = TRUE) expectedBeta <- estimates[,1] expectedErrors <- estimates[,2] expectedResiduals <- c(7.1044267859730512,1.6580347433531366, 4.6944952770029644,8.4548022690166160,13.6547432343186212, -9.3586864458500774,7.5822446330520386,15.5568995563859289, 0.8113090736598980,7.1186762732484308,7.4251378771228724, 2.6761316873234109,0.8351584810309354,7.1769991119615177, -3.8746753206299553,-3.1337779476387251,-0.1412575244091504, 1.1186809170469780,-6.3588097346816594,3.4039270429434074, 2.3374058329820175,-7.9272368576900503,-7.8361010968497959, -11.2597369269357070,0.9445333697827101,6.6544245101380328, -0.9146136301118665,-4.3152449403848570,-4.3536932047009183, -3.8907885169304661,-6.3027643926302188,-7.8308982189289091, -3.1792280015332750,-6.7167298771158226,-4.8469946718041754, -10.6335664353633685,11.1031134362036958,6.0084032641811733, 5.4326230830188482,-7.2375578629692230,2.1671550814448222, 15.0147574652763112,4.8625103516321015,-7.1597256413907706, -0.4515205619767598,-10.2916870903837587,-15.7812984571900063) expectedStdError <- 7.73642194433223 expectedRSquare <- 0.649789742860228 expectedAdjRSquare <- 0.6164363850373927 verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expectedAdjRSquare, "Swiss Fertility") # model with no intercept model <- lm(y ~ 0 + x1 + x2 + x3 + x4) estimates <- matrix(c(0.52191832900513, 0.10470063765677, 2.36588087917963, 0.41684100584290, -0.94770353802795, 0.43370143099691, 0.30851985863609, 0.07694953606522), nrow = 4, ncol = 2, byrow = TRUE) expectedBeta <- estimates[,1] expectedErrors <- estimates[,2] expectedResiduals <- c(44.138759883538249, 27.720705122356215, 35.873200836126799, 34.574619581211977, 26.600168342080213, 15.074636243026923, -12.704904871199814, 1.497443824078134, 2.691972687079431, 5.582798774291231, -4.422986561283165, -9.198581600334345, 4.481765170730647, 2.273520207553216, -22.649827853221336, -17.747900013943308, 20.298314638496436, 6.861405135329779, -8.684712790954924, -10.298639278062371, -9.896618896845819, 4.568568616351242, -15.313570491727944, -13.762961360873966, 7.156100301980509, 16.722282219843990, 26.716200609071898, -1.991466398777079, -2.523342564719335, 9.776486693095093, -5.297535127628603, -16.639070567471094, -10.302057295211819, -23.549487860816846, 1.506624392156384, -17.939174438345930, 13.105792202765040, -1.943329906928462, -1.516005841666695, -0.759066561832886, 20.793137744128977, -2.485236153005426, 27.588238710486976, 2.658333257106881, -15.998337823623046, -5.550742066720694, -14.219077806826615) expectedStdError <- 17.24710630547 expectedRSquare <- 0.946350722085 expectedAdjRSquare <- 0.9413600915813 verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors, expectedStdError, expectedRSquare, expectedAdjRSquare, "Swiss Fertility No Intercept") displayDashes(WIDTH)