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diff --git a/tests/examplefiles/test.plot b/tests/examplefiles/test.plot deleted file mode 100644 index cef0f908..00000000 --- a/tests/examplefiles/test.plot +++ /dev/null @@ -1,333 +0,0 @@ -# -# $Id: prob2.dem,v 1.9 2006/06/14 03:24:09 sfeam Exp $ -# -# Demo Statistical Approximations version 1.1 -# -# Copyright (c) 1991, Jos van der Woude, jvdwoude@hut.nl - -# History: -# -- --- 1991 Jos van der Woude: 1st version -# 06 Jun 2006 Dan Sebald: Added plot methods for better visual effect. - -print "" -print "" -print "" -print "" -print "" -print "" -print " Statistical Approximations, version 1.1" -print "" -print " Copyright (c) 1991, 1992, Jos van de Woude, jvdwoude@hut.nl" -print "" -print "" -print "" -print "" -print "" -print "" -print "" -print "" -print "" -print "" -print "" -print " NOTE: contains 10 plots and consequently takes some time to run" -print " Press Ctrl-C to exit right now" -print "" -pause -1 " Press Return to start demo ..." - -load "stat.inc" -rnd(x) = floor(x+0.5) -r_xmin = -1 -r_sigma = 4.0 - -# Binomial PDF using normal approximation -n = 25; p = 0.15 -mu = n * p -sigma = sqrt(n * p * (1.0 - p)) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * binom(floor((n+1)*p), n, p) #mode of binomial PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "binomial PDF using normal approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot binom(rnd(x), n, p) with histeps, normal(x, mu, sigma) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Binomial PDF using poisson approximation -n = 50; p = 0.1 -mu = n * p -sigma = sqrt(mu) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * binom(floor((n+1)*p), n, p) #mode of binomial PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample (xmax - xmin + 3) -set title "binomial PDF using poisson approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot binom(x, n, p) with histeps, poisson(x, mu) with histeps -pause -1 "Hit return to continue" -unset arrow -unset label - -# Geometric PDF using gamma approximation -p = 0.3 -mu = (1.0 - p) / p -sigma = sqrt(mu / p) -lambda = p -rho = 1.0 - p -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * p -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "geometric PDF using gamma approximation" -set arrow from mu, 0 to mu, gmm(mu, rho, lambda) nohead -set arrow from mu, gmm(mu + sigma, rho, lambda) \ - to mu + sigma, gmm(mu + sigma, rho, lambda) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, gmm(mu + sigma, rho, lambda) -plot geometric(rnd(x),p) with histeps, gmm(x, rho, lambda) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Geometric PDF using normal approximation -p = 0.3 -mu = (1.0 - p) / p -sigma = sqrt(mu / p) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * p -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "geometric PDF using normal approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot geometric(rnd(x),p) with histeps, normal(x, mu, sigma) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Hypergeometric PDF using binomial approximation -nn = 75; mm = 25; n = 10 -p = real(mm) / nn -mu = n * p -sigma = sqrt(real(nn - n) / (nn - 1.0) * n * p * (1.0 - p)) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * hypgeo(floor(mu), nn, mm, n) #mode of binom PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample (xmax - xmin + 3) -set title "hypergeometric PDF using binomial approximation" -set arrow from mu, 0 to mu, binom(floor(mu), n, p) nohead -set arrow from mu, binom(floor(mu + sigma), n, p) \ - to mu + sigma, binom(floor(mu + sigma), n, p) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, binom(floor(mu + sigma), n, p) -plot hypgeo(x, nn, mm, n) with histeps, binom(x, n, p) with histeps -pause -1 "Hit return to continue" -unset arrow -unset label - -# Hypergeometric PDF using normal approximation -nn = 75; mm = 25; n = 10 -p = real(mm) / nn -mu = n * p -sigma = sqrt(real(nn - n) / (nn - 1.0) * n * p * (1.0 - p)) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * hypgeo(floor(mu), nn, mm, n) #mode of binom PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "hypergeometric PDF using normal approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot hypgeo(rnd(x), nn, mm, n) with histeps, normal(x, mu, sigma) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Negative binomial PDF using gamma approximation -r = 8; p = 0.6 -mu = r * (1.0 - p) / p -sigma = sqrt(mu / p) -lambda = p -rho = r * (1.0 - p) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * gmm((rho - 1) / lambda, rho, lambda) #mode of gamma PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "negative binomial PDF using gamma approximation" -set arrow from mu, 0 to mu, gmm(mu, rho, lambda) nohead -set arrow from mu, gmm(mu + sigma, rho, lambda) \ - to mu + sigma, gmm(mu + sigma, rho, lambda) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, gmm(mu + sigma, rho, lambda) -plot negbin(rnd(x), r, p) with histeps, gmm(x, rho, lambda) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Negative binomial PDF using normal approximation -r = 8; p = 0.4 -mu = r * (1.0 - p) / p -sigma = sqrt(mu / p) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * negbin(floor((r-1)*(1-p)/p), r, p) #mode of gamma PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "negative binomial PDF using normal approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot negbin(rnd(x), r, p) with histeps, normal(x, mu, sigma) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Normal PDF using logistic approximation -mu = 1.0; sigma = 1.5 -a = mu -lambda = pi / (sqrt(3.0) * sigma) -xmin = mu - r_sigma * sigma -xmax = mu + r_sigma * sigma -ymax = 1.1 * logistic(mu, a, lambda) #mode of logistic PDF used -set key box -unset zeroaxis -set xrange [xmin: xmax] -set yrange [0 : ymax] -set xlabel "x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%.1f" -set format y "%.2f" -set sample 200 -set title "normal PDF using logistic approximation" -set arrow from mu,0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot logistic(x, a, lambda), normal(x, mu, sigma) -pause -1 "Hit return to continue" -unset arrow -unset label - -# Poisson PDF using normal approximation -mu = 5.0 -sigma = sqrt(mu) -xmin = floor(mu - r_sigma * sigma) -xmin = xmin < r_xmin ? r_xmin : xmin -xmax = ceil(mu + r_sigma * sigma) -ymax = 1.1 * poisson(mu, mu) #mode of poisson PDF used -set key box -unset zeroaxis -set xrange [xmin - 1 : xmax + 1] -set yrange [0 : ymax] -set xlabel "k, x ->" -set ylabel "probability density ->" -set ytics 0, ymax / 10.0, ymax -set format x "%2.0f" -set format y "%3.2f" -set sample 200 -set title "poisson PDF using normal approximation" -set arrow from mu, 0 to mu, normal(mu, mu, sigma) nohead -set arrow from mu, normal(mu + sigma, mu, sigma) \ - to mu + sigma, normal(mu + sigma, mu, sigma) nohead -set label "mu" at mu + 0.5, ymax / 10 -set label "sigma" at mu + 0.5 + sigma, normal(mu + sigma, mu, sigma) -plot poisson(rnd(x), mu) with histeps, normal(x, mu, sigma) -pause -1 "Hit return to continue" -reset |