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Java example source code file (multipleOLSRegressionTestCases)

This example Java source code file (multipleOLSRegressionTestCases) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

failed, fertility, license, longley, ols, residuals, rsquared, see, standard, succeeded, swiss, true, width, you

The multipleOLSRegressionTestCases Java example source code

# 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("<name-of-this-file>")
#
#------------------------------------------------------------------------------
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)

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