Added some initial OLS multiple regression tests.
git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/branches/MATH_2_0@664519 13f79535-47bb-0310-9956-ffa450edef68
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Phil Steitz
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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#------------------------------------------------------------------------------
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# R source file to validate OLS multiple regression tests in
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# org.apache.commons.math.stat.regression.OLSMultipleLinearRegressionTest
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#
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# To run the test, install R, put this file and testFunctions
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# into the same directory, launch R from this directory and then enter
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# source("<name-of-this-file>")
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#
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#------------------------------------------------------------------------------
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tol <- 1E-8 # error tolerance for tests
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#------------------------------------------------------------------------------
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# Function definitions
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source("testFunctions") # utility test functions
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options(digits=16) # override number of digits displayed
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# function to verify OLS computations
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verifyRegression <- function(model, expectedBeta, expectedResiduals,
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expectedErrors, modelName) {
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betaHat <- as.vector(coefficients(model))
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residuals <- as.vector(residuals(model))
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errors <- as.vector(as.matrix(coefficients(summary(model)))[,2])
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output <- c("Parameter test dataset = ", modelName)
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if (assertEquals(expectedBeta,betaHat,tol,"Parameters")) {
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displayPadded(output, SUCCEEDED, WIDTH)
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} else {
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displayPadded(output, FAILED, WIDTH)
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}
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output <- c("Residuals test dataset = ", modelName)
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if (assertEquals(expectedResiduals,residuals,tol,"Residuals")) {
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displayPadded(output, SUCCEEDED, WIDTH)
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} else {
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displayPadded(output, FAILED, WIDTH)
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}
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output <- c("Errors test dataset = ", modelName)
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if (assertEquals(expectedErrors,errors,tol,"Errors")) {
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displayPadded(output, SUCCEEDED, WIDTH)
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} else {
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displayPadded(output, FAILED, WIDTH)
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}
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}
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#--------------------------------------------------------------------------
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cat("Multiple regression OLS test cases\n")
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# Perfect fit
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x1 <- c(0,2,0,0,0,0)
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x2 <- c(0,0,3,0,0,0)
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x3 <- c(0,0,0,4,0,0)
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x4 <- c(0,0,0,0,5,0)
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x5 <- c(0,0,0,0,0,6)
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y <- c(11, 12, 13, 14, 15, 16)
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model <- lm(y ~ x1 + x2 + x3 + x4 + x5)
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expectedBeta <- c(11.0,0.5,0.666666666666667,0.75,0.8,0.8333333333333333)
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expectedResiduals <- c(0,0,0,0,0,0)
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expectedErrors <- c(NaN,NaN,NaN,NaN,NaN,NaN)
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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"perfect fit")
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# Longly
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#
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# Data Source: J. Longley (1967) "An Appraisal of Least Squares Programs for the
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# Electronic Computer from the Point of View of the User",
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# Journal of the American Statistical Association,
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# vol. 62. September, pp. 819-841.
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#
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# Certified values (and data) are from NIST:
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# http://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
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#
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design <- matrix(c(60323,83.0,234289,2356,1590,107608,1947,
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61122,88.5,259426,2325,1456,108632,1948,
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60171,88.2,258054,3682,1616,109773,1949,
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61187,89.5,284599,3351,1650,110929,1950,
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63221,96.2,328975,2099,3099,112075,1951,
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63639,98.1,346999,1932,3594,113270,1952,
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64989,99.0,365385,1870,3547,115094,1953,
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63761,100.0,363112,3578,3350,116219,1954,
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66019,101.2,397469,2904,3048,117388,1955,
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67857,104.6,419180,2822,2857,118734,1956,
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68169,108.4,442769,2936,2798,120445,1957,
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66513,110.8,444546,4681,2637,121950,1958,
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68655,112.6,482704,3813,2552,123366,1959,
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69564,114.2,502601,3931,2514,125368,1960,
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69331,115.7,518173,4806,2572,127852,1961,
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70551,116.9,554894,4007,2827,130081,1962),
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nrow = 16, ncol = 7, byrow = TRUE)
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y <- design[,1]
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x1 <- design[,2]
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x2 <- design[,3]
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x3 <- design[,4]
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x4 <- design[,5]
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x5 <- design[,6]
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x6 <- design[,7]
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model <- lm(y ~ x1 + x2 + x3 + x4 + x5 + x6)
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estimates <- matrix(c(-3482258.63459582,890420.383607373,
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15.0618722713733,84.9149257747669,
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-0.358191792925910E-01,0.334910077722432E-01,
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-2.02022980381683,0.488399681651699,
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-1.03322686717359,0.214274163161675,
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-0.511041056535807E-01,0.226073200069370,
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1829.15146461355,455.478499142212),
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nrow = 7, ncol = 2, byrow = TRUE)
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expectedBeta <- estimates[,1]
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expectedErrors <- estimates[,2]
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expectedResiduals <- c( 267.340029759711,-94.0139423988359,46.28716775752924,
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-410.114621930906,309.7145907602313,-249.3112153297231,-164.0489563956039,
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-13.18035686637081,14.30477260005235,455.394094551857,-17.26892711483297,
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-39.0550425226967,-155.5499735953195,-85.6713080421283,341.9315139607727,
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-206.7578251937366)
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verifyRegression(model, expectedBeta, expectedResiduals, expectedErrors,
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"Longly")
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displayDashes(WIDTH)
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@ -44,6 +44,9 @@ source("anovaTestCases")
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# descriptive
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source("descriptiveTestCases")
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# multiple regression
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source("multipleOLSRegressionTestCases")
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#------------------------------------------------------------------------------
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# if output has been diverted, change it back
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if (sink.number()) {
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