pascalTestCases

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# 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
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#
#      http://www.apache.org/licenses/LICENSE-2.0
#
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# 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 Pascal distribution tests in
# org.apache.commons.math.distribution.PascalDistributionTest
#
# 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>")
#
# R functions used
# dnbinom(x, size, prob, mu, log = FALSE) <- density
# pnbinom(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE) <- distribution
# qnbinom(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE) <- quantiles
#------------------------------------------------------------------------------
tol <- 1E-9                       # error tolerance for tests
#------------------------------------------------------------------------------
# Function definitions

source("testFunctions")           # utility test functions

# function to verify density computations

verifyDensity <- function(points, expected, size, p, tol) {
    rDensityValues <- rep(0, length(points))
    i <- 0
    for (point in points) {
        i <- i + 1
        rDensityValues[i] <- dnbinom(point, size, p)
    }
    output <- c("Density test size = ", size, ", p = ", p)
    if (assertEquals(expected,rDensityValues,tol,"Density Values")) {
        displayPadded(output, SUCCEEDED, WIDTH)
    } else {
        displayPadded(output, FAILED, WIDTH)
    }       
}

# function to verify distribution computations

verifyDistribution <- function(points, expected, size, p, tol) {
    rDistValues <- rep(0, length(points))
    i <- 0
    for (point in points) {
        i <- i + 1
        rDistValues[i] <- pnbinom(point, size, p)
    }
    output <- c("Distribution test size = ", size, ", p = ", p)
    if (assertEquals(expected,rDistValues,tol,"Distribution Values")) {
        displayPadded(output, SUCCEEDED, WIDTH)
    } else {
        displayPadded(output, FAILED, WIDTH)
    }       
}

#--------------------------------------------------------------------------
cat("Negative Binomial test cases\n")

size <- 10.0
probability <- 0.70

densityPoints <- c(-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
densityValues <- c(0, 0.0282475249, 0.0847425747, 0.139825248255, 0.167790297906, 0.163595540458,
          0.137420253985, 0.103065190489, 0.070673273478, 0.0450542118422, 0.0270325271053,
          0.0154085404500, 0.0084046584273)
distributionValues <- c(0, 0.0282475249, 0.1129900996, 0.252815347855, 0.420605645761, 0.584201186219,
          0.721621440204, 0.824686630693, 0.895359904171, 0.940414116013, 0.967446643119,
          0.982855183569, 0.991259841996)
inverseCumPoints <- c( 0, 0.001, 0.010, 0.025, 0.050, 0.100, 0.999,
          0.990, 0.975, 0.950, 0.900)
inverseCumValues <- c(-1, -1, -1, -1, 0, 0, 13, 10, 9, 8, 7)

verifyDensity(densityPoints,densityValues,size,probability,tol)
verifyDistribution(densityPoints, distributionValues, size, probability, tol)

i <- 0
rInverseCumValues <- rep(0,length(inverseCumPoints))
for (point in inverseCumPoints) {
  i <- i + 1
  rInverseCumValues[i] <- qnbinom(point, size, probability)
}

output <- c("Inverse Distribution test n = ", size, ", p = ", probability)
# R defines quantiles from the right, need to subtract one
if (assertEquals(inverseCumValues, rInverseCumValues-1, tol,
    "Inverse Dist Values")) {
    displayPadded(output, SUCCEEDED, 80)
} else {
    displayPadded(output, FAILED, 80)
}       

# Degenerate cases

size <- 5
probability <- 0.0

densityPoints <- c(-1, 0, 1, 10, 11)
# Note: commons math returns 0's below
densityValues <- c(NaN, NaN, NaN, NaN, NaN)
distributionPoints <- c(-1, 0, 1, 5, 10)
# Note: commons math returns 0's below
distributionValues <- c(NaN, NaN, NaN, NaN, NaN)

output <- c("Density test n = ", size, ", p = ", probability)
verifyDensity(densityPoints,densityValues,size,probability,tol)
output <- c("Distribution test n = ", size, ", p = ", probability)
verifyDistribution(distributionPoints,distributionValues,size,probability,tol)

size <- 5
probability <- 1.0

densityPoints <- c(-1, 0, 1, 2, 5, 10)
densityValues <- c(0, 1, 0, 0, 0, 0)
distributionPoints <- c(-1, 0, 1, 2, 5, 10)
distributionValues <- c(0, 1, 1, 1, 1, 1)

output <- c("Density test n = ", size, ", p = ", probability)
verifyDensity(densityPoints,densityValues,size,probability,tol)
output <- c("Distribution test n = ", size, ", p = ", probability)
verifyDistribution(distributionPoints,distributionValues,size,probability,tol)

displayDashes(WIDTH)