{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Statistical Addendum to \"Improving the Reliability of Test Functions Generators\"" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T19:04:58.366022Z", "start_time": "2019-04-15T19:04:57.980Z" } }, "outputs": [], "source": [ "library(\"xtable\")\n", "library(\"car\")\n", "library(\"SPOT\") \n", "library(\"MASS\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SAMP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Original Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Generation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\pi$ is drawn from $\\Pi$. We consider $n_{\\pi}$ instances to be drawn.\n", "The algorithm is run $n=30$ times on each test instance." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:37.627210Z", "start_time": "2019-04-15T18:46:37.607Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t120 obs. of 3 variables:\n", " $ Y : num 2.82e+01 7.11e-15 0.00 2.18e+01 2.84e+01 ...\n", " $ PInst : Factor w/ 4 levels \"1\",\"2\",\"3\",\"4\": 1 1 1 1 1 1 1 1 1 1 ...\n", " $ Repeat: Factor w/ 30 levels \"1\",\"2\",\"3\",\"4\",..: 1 2 3 4 5 6 7 8 9 10 ...\n" ] } ], "source": [ "load(\"2018-05-29.GLG.tuned.GenSA.sasp.RData\")\n", "#load(\"Experiments.d/results/2018-05-29.smoof.tuned.DEoptim.sasp.RData\") \n", "df01 <- data.frame(cbind(Y=resultDataFrame$yBest,\n", " PInst = resultDataFrame$testInstance,\n", " Repeat = resultDataFrame$repetition))\n", "df01$PInst <- factor(df01$PInst)\n", "df01$Repeat <- factor(df01$Repeat)\n", "#df01\n", "str(df01)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:38.808832Z", "start_time": "2019-04-15T18:46:38.757Z" } }, "outputs": [ { "data": { "image/png": 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HVCI0CAAAECBAgQIECgUYEWPkGqe+c/fbG+l+bnmxJVJFVhVJ8knZI4PXFe4qGJ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\nf5k4OXHbxOFEFTg/mdho782Duvtd9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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(Y ~ PInst, df01, xlab = \"PInst\", ylab = \"y\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:39.603028Z", "start_time": "2019-04-15T18:46:39.586Z" } }, "outputs": [ { "data": { "text/html": [ "4.736951571734e-16" ], "text/latex": [ "4.736951571734e-16" ], "text/markdown": [ "4.736951571734e-16" ], "text/plain": [ "[1] 4.736952e-16" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "1.80270469984451e-15" ], "text/latex": [ "1.80270469984451e-15" ], "text/markdown": [ "1.80270469984451e-15" ], "text/plain": [ "[1] 1.802705e-15" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean(df01[df01$PInst == 3,]$Y)\n", "sd(df01[df01$PInst == 3,]$Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Corrected Data" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:41.652944Z", "start_time": "2019-04-15T18:46:41.638Z" } }, "outputs": [], "source": [ "df01 <- df01[df01$PInst != 3,]\n", "# recalculate the factor levels:\n", "df01$PInst <- factor(df01$PInst)\n", "df01$Repeat <- factor(df01$Repeat)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:42.304207Z", "start_time": "2019-04-15T18:46:42.263Z" } }, "outputs": [ { "data": { "image/png": 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865v90IECBAgAABAj0KnJKi60p2BoHm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\ngAABAgQIEBiHwP8HaQZv2hPvBPYAAAAASUVORK5CYII=", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(Y ~ factor(PInst), df01, xlab = \"PInst\", ylab = \"y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ANOVA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generate ANOVA Table" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:45.204315Z", "start_time": "2019-04-15T18:46:45.188Z" } }, "outputs": [], "source": [ "# note: a == n_pi == r \n", "a <- nlevels(df01$PInst)\n", "n <- dim(df01)[1]/(a) # repeats (balanced design)\n", "g <- lm(Y ~ PInst, data = df01)\n", "g.anova <- anova(g)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:45.801224Z", "start_time": "2019-04-15T18:46:45.784Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "
DfSum SqMean SqF valuePr(>F)
PInst 2 140.213 70.1065 0.44865860.6399529
Residuals87 13594.447156.2580 NA NA
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", "\\hline\n", "\tPInst & 2 & 140.213 & 70.1065 & 0.4486586 & 0.6399529\\\\\n", "\tResiduals & 87 & 13594.447 & 156.2580 & NA & NA\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) |\n", "|---|---|---|---|---|---|\n", "| PInst | 2 | 140.213 | 70.1065 | 0.4486586 | 0.6399529 |\n", "| Residuals | 87 | 13594.447 | 156.2580 | NA | NA |\n", "\n" ], "text/plain": [ " Df Sum Sq Mean Sq F value Pr(>F) \n", "PInst 2 140.213 70.1065 0.4486586 0.6399529\n", "Residuals 87 13594.447 156.2580 NA NA" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g.anova" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract MeanSq and df" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:48.379733Z", "start_time": "2019-04-15T18:46:48.359Z" } }, "outputs": [ { "data": { "text/html": [ "70.11" ], "text/latex": [ "70.11" ], "text/markdown": [ "70.11" ], "text/plain": [ "[1] 70.11" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "156.26" ], "text/latex": [ "156.26" ], "text/markdown": [ "156.26" ], "text/plain": [ "[1] 156.26" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "2" ], "text/latex": [ "2" ], "text/markdown": [ "2" ], "text/plain": [ "[1] 2" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "87" ], "text/latex": [ "87" ], "text/markdown": [ "87" ], "text/plain": [ "[1] 87" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round((MSA <- g.anova$`Mean Sq`[1]),2)\n", "round((MSE <- g.anova$`Mean Sq`[2]),2)\n", "(dfA <- g.anova$Df[1])\n", "(dfE <- g.anova$Df[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [SAMP-1] Perform Hypothesis Test" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:50.557623Z", "start_time": "2019-04-15T18:46:50.533Z" } }, "outputs": [ { "data": { "text/html": [ "0.448658615499934" ], "text/latex": [ "0.448658615499934" ], "text/markdown": [ "0.448658615499934" ], "text/plain": [ "[1] 0.4486586" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "3.10129575666719" ], "text/latex": [ "3.10129575666719" ], "text/markdown": [ "3.10129575666719" ], "text/plain": [ "[1] 3.101296" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "0.639952917243752" ], "text/latex": [ "0.639952917243752" ], "text/markdown": [ "0.639952917243752" ], "text/plain": [ "[1] 0.6399529" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(F0.A <- MSA/MSE)\n", "(qf(0.95, dfA, dfE))\n", "# F* < F, we cannot reject the null hypothesis (there are no differences)\n", "# This is also reflected in the large p-value:\n", "(p.A <- 1-pf(F0.A,dfA,dfE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [SAMP-2] Estimation of the Overall Mean" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:52.132229Z", "start_time": "2019-04-15T18:46:52.118Z" } }, "outputs": [ { "data": { "text/html": [ "8.93" ], "text/latex": [ "8.93" ], "text/markdown": [ "8.93" ], "text/plain": [ "[1] 8.93" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round((Ybar.. = mean(df01$Y) ),2)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:53.374269Z", "start_time": "2019-04-15T18:46:53.355Z" } }, "outputs": [], "source": [ "alpha <- 0.1\n", "sYbar.. <- sqrt(max(0, MSA/(a*n)) )\n", "muLo95 <- Ybar.. - qt(1-alpha/2, a-1) * sYbar..\n", "muHi95 <- Ybar.. + qt(1-alpha/2, a-1) * sYbar.." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:46:53.954753Z", "start_time": "2019-04-15T18:46:53.940Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 6.36
    2. \n", "\t
  2. 11.51
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 6.36\n", "\\item 11.51\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 6.36\n", "2. 11.51\n", "\n", "\n" ], "text/plain": [ "[1] 6.36 11.51" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "round( c(muLo95, muHi95),2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [SAMP-3] Plot of the Confidence Intervals of the Mean" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:47:06.675465Z", "start_time": "2019-04-15T18:47:06.620Z" } }, "outputs": [ { "data": { "image/png": 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5xit8kdpUDaJJiPEyBAgAABAgT6KHBF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\nUhcg876On0kF2w7RjRm/Inl38jvJe5Mrk5uTWua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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# note: a == n_pi in the text\n", "alpha <- seq(0.01,0.5,0.01)\n", "sYbar.. <- sqrt(max(0, MSA/(a*n)) )\n", "muLo <- Ybar.. - qt(1-alpha/2, a-1) * sYbar..\n", "muHi <- Ybar.. + qt(1-alpha/2, a-1) * sYbar..\n", "conf <- 1- alpha/2\n", "\n", "#pdf(\"confInt01.pdf\")\n", "plot(muLo~conf, type = \"l\", ylim=c(min(muHi,muLo), max(muHi,muLo) ), ylab=\"Y\")\n", "abline(Ybar..,0)\n", "abline(v=0.95, lty=2, col = 2)\n", "#abline(h=muLo95)\n", "#abline(h=muHi95)\n", "arrows(0.74, muHi95, 0.95, muHi95, col= 2, code =1)\n", "arrows(0.74, muLo95, 0.95, muLo95, col= 2, code = 1)\n", "lines(conf,muHi)\n", "#dev.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Intraclass Correlation Coefficient" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ratio illustrates the variation between the $\\mu_i$ values, i.e., the\n", "variation of the performance values between different problem instances.\n", "$$\n", "\\frac{\\sigma_{\\mu}^2}{\\sigma_{\\mu}^2 + \\sigma^2}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It compares \n", "1. the variance caused by the different runs of the algorithm with \n", "2. the variance caused by different problem instances.\n" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:47:11.524482Z", "start_time": "2019-04-15T18:47:11.507Z" } }, "outputs": [], "source": [ "alpha <- 0.1\n", "L <- 1/n * (MSA/MSE * 1/ qf(1-alpha/2, a-1, a*(n-1)) -1)\n", "U <- 1/n * (MSA/MSE * 1/ qf(alpha/2, a-1, a*(n-1)) -1)\n", "Lstar <- max(0, L/(L+1) )\n", "Ustar <- max(0, U/(U+1) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With confidence coefficient $\\alpha$, we conclude that that the variability of the \n", "mean performance values of the different problem instances $\\pi_i$ accounts for \n", "between three and 60 percent of the total variability in the \n", "performance values:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:47:12.472482Z", "start_time": "2019-04-15T18:47:12.458Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
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  2. 91.7211849682282
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 55.5623747267529\n", "\\item 91.7211849682282\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 55.5623747267529\n", "2. 91.7211849682282\n", "\n", "\n" ], "text/plain": [ "[1] 55.56237 91.72118" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alpha=0.1\n", "Lsigma2 <- a*(n-1)*MSA / qchisq(p=1-alpha/2, df=a*(n-1))\n", "Usigma2 <- a*(n-1)*MSA / qchisq(p=alpha/2, df=a*(n-1))\n", "c(Lsigma2, Usigma2)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:47:17.018294Z", "start_time": "2019-04-15T18:47:17.004Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 7.45401735487334
    2. \n", "\t
  2. 9.5771177798035
    3. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 7.45401735487334\n", "\\item 9.5771177798035\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 7.45401735487334\n", "2. 9.5771177798035\n", "\n", "\n" ], "text/plain": [ "[1] 7.454017 9.577118" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sqrt(c(Lsigma2, Usigma2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Estimation of $\\sigma_{\\mu}^2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An unbiased estimator of $\\sigma_{\\mu}^2$ is $(MSTR - MSE)/n$.\n", "In some settings, this point estimator is negative. It is a usual practice to consider the \n", "estimator be be zero. This situation occurs in the SAMP example, which leads to the result:\n", "$\\hat{\\sigma}^2_\\mu = 0$.\n", "Since exact confidence intervals for $\\sigma_{\\mu}^2$ cannot be determined,\n", "\\citet{satt46a} developed a procedure for calculating approximate $\\sigma_{\\mu}^2$ confidence intervals. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:47:18.919247Z", "start_time": "2019-04-15T18:47:18.905Z" } }, "outputs": [ { "data": { "text/html": [ "0" ], "text/latex": [ "0" ], "text/markdown": [ "0" ], "text/plain": [ "[1] 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(sMu <- sqrt( max(0, (MSA-MSE) / n) ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MAMP Putting the Pieces Together" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We consider the \n", "2019-03-21.GLG.tuned.SPOT.GenSA.DEoptim.RS.mamp.RData\n", "data set. New experiment (30.01.2019) with \n", "1. SPOT\n", "2. DEopt\n", "3. GenSA and \n", "4. RS (as baseline)\n", "Beside randomly chosen problem instances, i.e., $PInst$, this data set contains \n", "results from different algorithms, i.e., $Algorithm$." ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:30.090008Z", "start_time": "2019-04-15T18:52:30.059Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t480 obs. of 24 variables:\n", " $ id : num 1 2 3 4 5 6 7 8 9 10 ...\n", " $ repetition : int 1 2 3 4 5 6 7 8 9 10 ...\n", " $ yBest : num 9.56e-07 1.09e-05 1.08e-05 2.93e-06 5.67e-06 ...\n", " $ seed : num 10010 10011 10012 10013 10014 ...\n", " $ budget : num 240 240 240 240 240 240 240 240 240 240 ...\n", " $ designSize : num 30 30 30 30 30 30 30 30 30 30 ...\n", " $ designType : num 1 1 1 1 1 1 1 1 1 1 ...\n", " $ modelType : num 1 1 1 1 1 1 1 1 1 1 ...\n", " $ temp : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ qv : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ qa : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ popsize : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ F : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ CR : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ c : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ strategy : num NA NA NA NA NA NA NA NA NA NA ...\n", " $ name : chr \"SPOT\" \"SPOT\" \"SPOT\" \"SPOT\" ...\n", " $ testInstance: int 1 1 1 1 1 1 1 1 1 1 ...\n", " $ type : chr \"GLG\" \"GLG\" \"GLG\" \"GLG\" ...\n", " $ dimension : num 4 4 4 4 4 4 4 4 4 4 ...\n", " $ nGaussian : num 7 7 7 7 7 7 7 7 7 7 ...\n", " $ ratio : num 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 ...\n", " $ alpha : num 0 0 0 0 0 0 0 0 0 0 ...\n", " $ itermax : num NA NA NA NA NA NA NA NA NA NA ...\n" ] } ], "source": [ "rm(resultDataFrame)\n", "#load(\"2018-08-21.smoof.tuned.GenSA.SPOT.DEoptim.mamp.RData\")\n", "load(\"2019-03-21.GLG.tuned.SPOT.GenSA.DEoptim.RS.mamp.RData\")\n", "str(resultDataFrame)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:31.377571Z", "start_time": "2019-04-15T18:52:31.364Z" } }, "outputs": [], "source": [ "df02 <- data.frame(Y = resultDataFrame$yBest,\n", " A = factor(resultDataFrame$name),\n", " B = factor(resultDataFrame$testInstance))\n" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:32.076109Z", "start_time": "2019-04-15T18:52:32.061Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'DEoptim'
    2. \n", "\t
  2. 'GenSA'
    3. \n", "\t
  3. 'RS'
    4. \n", "\t
  4. 'SPOT'
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'DEoptim'\n", "\\item 'GenSA'\n", "\\item 'RS'\n", "\\item 'SPOT'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'DEoptim'\n", "2. 'GenSA'\n", "3. 'RS'\n", "4. 'SPOT'\n", "\n", "\n" ], "text/plain": [ "[1] \"DEoptim\" \"GenSA\" \"RS\" \"SPOT\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "levels(factor(resultDataFrame$name))" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:32.961509Z", "start_time": "2019-04-15T18:52:32.939Z" } }, "outputs": [ { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.000000 0.000007 0.000022 0.179028 0.000058 21.459331 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.00 11.25 21.46 22.94 40.91 47.53 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ " Min. 1st Qu. Median Mean 3rd Qu. Max. \n", " 0.06829 0.82062 1.86608 3.35133 3.97549 21.51819 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "summary(df02$Y[df02$A == \"SPOT\"])\n", "#summary(df02$Y[df02$A == \"RS\"])\n", "summary(df02$Y[df02$A == \"GenSA\"])\n", "summary(df02$Y[df02$A == \"DEoptim\"])" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:33.910874Z", "start_time": "2019-04-15T18:52:33.839Z" } }, "outputs": [ { "data": { "image/png": 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/km6Svi+9LWEQgAAEINDh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Q5jeorfFfGuSl+YepIeH3sF83aGHDeEhD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\nXkH5j+LbAO9Q/P1xn+L6VnxOTYelIIAAAggggAAC7SmQtw5SVzHvrhyg+Lf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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(Y ~ A+B , horizontal=T, df02, ylab = \"Method / PInst\", xlab = \"\", las=2, par(mgp=c(5.5, 1, 0),mar=c(6.7, 6.7, 4.1, 2.1)) ) \n", "mtext(\"y\", side = 1, line = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ANOVA" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:35.719677Z", "start_time": "2019-04-15T18:52:35.693Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
DfSum SqMean SqF valuePr(>F)
A 3 36517.973 12172.65781 213.83093 4.421707e-87
B 3 6009.553 2003.18438 35.18893 1.658606e-20
A:B 9 6630.046 736.67178 12.94074 1.773614e-18
Residuals464 26413.921 56.92655 NA NA
\n" ], "text/latex": [ "\\begin{tabular}{r|lllll}\n", " & Df & Sum Sq & Mean Sq & F value & Pr(>F)\\\\\n", "\\hline\n", "\tA & 3 & 36517.973 & 12172.65781 & 213.83093 & 4.421707e-87\\\\\n", "\tB & 3 & 6009.553 & 2003.18438 & 35.18893 & 1.658606e-20\\\\\n", "\tA:B & 9 & 6630.046 & 736.67178 & 12.94074 & 1.773614e-18\\\\\n", "\tResiduals & 464 & 26413.921 & 56.92655 & NA & NA\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| | Df | Sum Sq | Mean Sq | F value | Pr(>F) |\n", "|---|---|---|---|---|---|\n", "| A | 3 | 36517.973 | 12172.65781 | 213.83093 | 4.421707e-87 |\n", "| B | 3 | 6009.553 | 2003.18438 | 35.18893 | 1.658606e-20 |\n", "| A:B | 9 | 6630.046 | 736.67178 | 12.94074 | 1.773614e-18 |\n", "| Residuals | 464 | 26413.921 | 56.92655 | NA | NA |\n", "\n" ], "text/plain": [ " Df Sum Sq Mean Sq F value Pr(>F) \n", "A 3 36517.973 12172.65781 213.83093 4.421707e-87\n", "B 3 6009.553 2003.18438 35.18893 1.658606e-20\n", "A:B 9 6630.046 736.67178 12.94074 1.773614e-18\n", "Residuals 464 26413.921 56.92655 NA NA" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a <- nlevels(df02$A)\n", "b <- nlevels(df02$B)\n", "n <- dim(df02)[1]/(a*b) # repeats (balanced design)\n", "g <- lm(Y~(A + B)^2, data = df02)\n", "(g.anova <- anova(g))\n", "# xtable(g.anova)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:36.480660Z", "start_time": "2019-04-15T18:52:36.460Z" } }, "outputs": [], "source": [ "MSA <- g.anova$`Mean Sq`[1]\n", "MSB <- g.anova$`Mean Sq`[2]\n", "MSAB <- g.anova$`Mean Sq`[3]\n", "MSE <- g.anova$`Mean Sq`[4]\n", "dfA <- g.anova$Df[1]\n", "dfB <- g.anova$Df[2]\n", "dfAB <- g.anova$Df[3]\n", "dfE <- g.anova$Df[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-1] Do the algorithms differ in performance?" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:38.144732Z", "start_time": "2019-04-15T18:52:38.126Z" } }, "outputs": [ { "data": { "text/html": [ "16.5238551769274" ], "text/latex": [ "16.5238551769274" ], "text/markdown": [ "16.5238551769274" ], "text/plain": [ "[1] 16.52386" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "0.000528527315792671" ], "text/latex": [ "0.000528527315792671" ], "text/markdown": [ "0.000528527315792671" ], "text/plain": [ "[1] 0.0005285273" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# A denotes the algorithm factor\n", "# F-value for A:\n", "(F0.A <- MSA/MSAB)\n", "# p value for A:\n", "(p.A <- 1-pf(F0.A,dfA,dfAB))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-2] Do different problem instances affect the performance?" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:39.769188Z", "start_time": "2019-04-15T18:52:39.750Z" } }, "outputs": [ { "data": { "text/html": [ "35.1889278826887" ], "text/latex": [ "35.1889278826887" ], "text/markdown": [ "35.1889278826887" ], "text/plain": [ "[1] 35.18893" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "0" ], "text/latex": [ "0" ], "text/markdown": [ "0" ], "text/plain": [ "[1] 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# B denotes the test instance factor\n", "# F value for B:\n", "(F0.B <- MSB/MSE)\n", "# p value for B:\n", "(p.B <- 1-pf(F0.B,dfB,dfE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-3] Do the algorithms a and problem instances $\\pi_i$ interact?" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:41.043401Z", "start_time": "2019-04-15T18:52:41.025Z" } }, "outputs": [ { "data": { "text/html": [ "12.9407409559385" ], "text/latex": [ "12.9407409559385" ], "text/markdown": [ "12.9407409559385" ], "text/plain": [ "[1] 12.94074" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "0" ], "text/latex": [ "0" ], "text/markdown": [ "0" ], "text/plain": [ "[1] 0" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# AB denotes the interaction\n", "# F value for AB:\n", "(F0.AB <- MSAB/MSE)\n", "# p value for B:\n", "(p.AB <- 1-pf(F0.AB,dfAB,dfE))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-4] Estimation of the fixed factor effects" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:52:42.496235Z", "start_time": "2019-04-15T18:52:42.465Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. -8.48
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  2. -5.31
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  3. 14.28
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item -8.48\n", "\\item -5.31\n", "\\item 14.28\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. -8.48\n", "2. -5.31\n", "3. 14.28\n", "\n", "\n" ], "text/plain": [ "[1] -8.48 -5.31 14.28" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y.. <- mean(df02$Y)\n", "y1. <- mean(df02$Y[df02$A == \"SPOT\"])\n", "y2. <- mean(df02$Y[df02$A == \"DEoptim\"])\n", "#y3. <- mean(df02$Y[df02$A == \"RS\"])\n", "y4. <- mean(df02$Y[df02$A == \"GenSA\"])\n", "\n", "tau1 <- y1. - y..\n", "tau2 <- y2. - y..\n", "#tau3 <- y3. - y..\n", "tau4 <- y4. - y..\n", "round( (c(tau1, tau2, \n", " # tau3, \n", " tau4)),2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-5] Confidence Intervals for Fixed Effects Contrasts" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:54:08.670080Z", "start_time": "2019-04-15T18:54:08.600Z" }, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "-3.17" ], "text/latex": [ "-3.17" ], "text/markdown": [ "-3.17" ], "text/plain": [ "[1] -3.17" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
    \n", "\t
  1. -14.38
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  2. 8.04
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\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item -14.38\n", "\\item 8.04\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. -14.38\n", "2. 8.04\n", "\n", "\n" ], "text/plain": [ "[1] -14.38 8.04" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "-7.99" ], "text/latex": [ "-7.99" ], "text/markdown": [ "-7.99" ], "text/plain": [ "[1] -7.99" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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  1. -19.2
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RmjjFVWU7ZUrlD85WJtpZql3AqpmuvGvBFA4AOBWlZIHyy1tL5l9ba3lb1Ke/v7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6TpRztJyco5/eEnrO0BtE3jDX4UNAkhCo\nMQHPCdxU8oILXo3ug9J0aar0DeluCYPAuwhM0R6vHnSD9LL0knSZdK+0s+Q5QZ6gZifJ3nU9\nzfk4rJ43IG0IQKAmBPzGba+apJSdRA5UVu/MTnYzmdPUtUdyjNbtCR1XSfO1EMP/vTawumsm\n4ZJpCOSUQKn2aBmV1b1E50pendnPsHaE3Ang59qsG+1RnWtwjNL3Ch27Fu6ziMK/Sv4gfbiw\nLwpuVOSKaKNOIQ5SncCSLARqTKBUg1TjW6QuORqk+lZJqtqjkXpxeHkYdZsco26tTnedVqpL\nstBQfQmROgQgUIqA2yOvOueRUFo0pf8Fv58nNV0w/EHyy7yVpTxZZtqjrA6x20ifFrUD/T1G\n/uC8JV0k2bv2vKO4/VEbX4/vIA4BCEAAAhCoEYFUtUcHhhHhM2GE5yc8ryF1q2uMzh/1A7A1\nKirJQAACwyHwSOhrvzn0jr4t9I25PfQu/kgI7n32M6x/e8zzify8qmmDDIMVg6ZaVh0kdzt6\nzLeGUoerCgQ9vK5b8phN9yRFtq0iT0cbhBCAAAQgAIEaEkhVe3SHmj81gH4L/VZb6J3aF9qe\nqWFZSQoCEEhI4MGwYFE9mI77t15UyDGaoH/ENebp52g6Q3hjdS2qsFRoW3OF0He25of8UEmy\n0FdCrpw2NAGvMDdHcrdkKUfPvUlXS3aWdpTqaQyxqydd0oZA7QgwxK52LEnpHQKpao80znyK\nhtj9SVqg+UdnqqFc/p2sEoMABOpAYBWluZ10sHSp9KTk588uyT1Cp0t7SP7tzshojyIShDUl\nMFqp/UCaVibVU7TfPUoe71hvw0GqN2HSh0BtCNAg1YYjqSxMIJXt0dww6uMaXnefHKXXNB/p\nAC/3vXC22YIABCoksLjO31zy/CC/oL9BminZGZor2RnyAmH7SO+XBhvfSnskQFj9CJT78K2u\nW/qD3AjDQWoEZe4BgeEToEEaPkNSKE8gde3RVM3X7Q7t35CTNEPO0qNa3c5LBmMQgMDgBFbS\n4Y9J+0onStdKHq5qR8jPfE9J7jk+SvLKye+RRkqVGO1RJbQafG6poWkNzsKwb+deolLGvKNS\nVNgHAQhAAAL1IpC69mhiCAs0veG02SFcvEjoPGxk6LtMvUnX94a+73WG7ofqBYJ0IZABAssq\nj2vF5OFv6xTkF+z63+n/3aGHFf5Hulh6sKA3FGI5JpAHBynH1UPRIAABCEAAAsMnsGQIszQd\n4tta9vt0rXB0knSfepZOfSv0HDZwbPj3IAUIpIyAh756NNEEaY2icE1tLyHZXpDcm2N5ee1H\nCvJ2uZceOoTlmQAOUp5rl7JBAAIQgAAEYgQKvUaf1lC7/5GTdMKioWP37tD3k8tCz1kDvU2x\nk4lCIN0Ellb2VpPsBDkcV9D4QriCQq9s3CV5SNwT0pPSvwpxO0CPS29KGAQWIoCDtBAONiAA\nAQhAAAL5JzA6dF3xX82rWCd0fltPkMfsEDq+MTf0fmdMmH9j/ktPCVNOwE6NV15cWfLqcKvG\nFG3bKVpEstnB8fwgO0HW5dL0Qtzbz0u9EgaBxARwkBKj4kQIQAACEIBAfghs0D98qOt4LQP+\n287QedSoMOJvWszhsvmh+wdjBt6056ewlCQNBLyIgR2fFSUvglAsO0SW90fPp3Z+npOeLehO\nhX+SnpbsFFmvShgEakog+gDWNFESgwAEIAABCEAgGwQWC+FljULaW3OSTtNvsJ80KnQ8pDFJ\nv3wtdB+lp1kmo2ejGpuVS6/cuJxkx8dD2opDO0OWj3lRBI3s7Le5+uu5Py8WQjtBd0ju7YlL\n64tgEGg8ARykxjPnjhCAAAQgAIHUEegIPXcrU1tpIYed20LbsUuFzkndofcg7T9f+728MZZ/\nAouqiHZkItn5cdxhJDtBkVMULXSgXf1zfeRsh5ekKPTvAnnbjlAU2jF6TcIgkFoCOEiprRoy\nBgEIQAACEGg8AS3k8HuNW7p8xdDxQzlKp+v3k/aXf/QdOUqe3I5lg4B7apaSvJCBtcwgsgPk\n4w5HS5GpIzHMKOgVhZaHtt0l2QHytsNIOD2CgeWDAA5SPuqRUkAAAhCAAARqRkBLgmkIVPdh\nb4VwjobcHStH6RY5ShdpftJBiwzMCanZvUioLAEvVqARkEErsfc7O3Z44rLjEzlBURg5RO7Z\n8fWReUjbzBKapn12gnwscoaiUNPTMAi0JgEcpNasd0oNAQhAAAIQGJKAnKFn5Sjt2hNGnarp\nI1PaQ8cjPSH84tnQfcKEEOYNmUBrn+BenMjBscNiR6c49L5SssPjc0dKkXklttekWUXyIgVe\nwtr7HS+WnR/qShAwCCQlgIOUlBTnQQACEIAABFqUQHuYf4uKvpkWcpjUFkYctUro2GNq6F5/\nYggLcopEvmFYfBDZebF8Tqm499k5ivfiaDO4V8ZOzuxY6Ljn6Dxc2D+rKPRx7/N1LFctCBgE\n6k0AB6nehEkfAhCAAAQgkA8CfZqHdK4mnly6WOjcOmXOkR2RRSU7JWMLoeOWnZgoHt+O9sfD\nKF78fGTH5PWY7Kx4Owq93LTj5WQnx+fn1aFU0TAI5IdA8RdAfkpGSSAAAQhAAAIQqDkBLV+m\nXpCuK4eZsH5qqd+RiTs1dmyKZYcmvi/ajhyeyBlyWNxb45X33pTca2PZQYnHZ2jbQ9Pix+JO\nUDz+hs7DIACBFiGAg9QiFU0xIQABCEAAAhUS8PwXOzClZIckvj++HcXjYXE8+j2ceJbma8OO\niGWnJYpH297n38iJtovDyPmJQh9neXJBwCAAgcoI4CBVxouzIQABCEAAAnkhcLIKsrZkR8dz\nbuIOj+OdUinTOg39PTPunbET4rA47t+8ebxov8+NFJ0fbTv0stIYBCAAgaYTwEFqehWQAQhA\nAAIQgEBTCHjejFby7lfk5HjbzorDaF8Uer/jdpAwCEAAArklgIOU26qlYBCAAAQgAIFBCRw3\n6FEOQgACEGhRAqXGALcoCooNAQhAAAIQgAAEIAABCLQ6ARykVv8EUH4IQAACEIAABCAAAQhA\n4G0COEhvoyACAQhAAAIQgAAEIAABCLQ6ARykVv8EUH4IQAACEIAABCAAAQhA4G0COEhvoyAC\nAQhAAAIQgAAEIAABCLQ6ARykVv8EUH4IQAACEIAABCAAAQhA4G0CLPP9NgoiEIAABCAAgVwQ\n2Fyl+H4uSkIhIJBfAkuoaNtKDlvFPqqCZqJzBgepVT6SlBMCEIAABFqFwEYq6LKtUljKCYGM\nElhM+fbLjLUzmv9qsr28LppRzYWNvgYHqdHEuR8EIACB4RP4qpL4fEHDT40U8kbgLBXo0LwV\nivJAIGcEHlN5fiGdk7NyDVacA3Vw4mAnpOVYJrq50gKLfEAAAhBICYGvKx8PpyQvZAMCEIAA\nBCCQKwI4SLmqTgoDAQi0AIF1VMbNpAtaoKwUEQIQgAAEINBwAjhIDUfODSEAAQgMi8Buuvr+\ngoaVEBdDAAIQgAAEIPBuAjhI72bCHghAAAJpJmAHid6jNNcQeYMABCAAgUwTyMMiDaNVA5tI\nK0krSH3SLOk+6dHCtgIMAhCAQOYJeMWjNaSLM1+SfBaA9iif9UqpIACBFiOQZQfJeT9C2kda\nuky93an9e0kejoJBAAIQyDoB9x7dJD2T9YLkLP+0RzmrUIoDAQi0NoEsO0hnquq+KJ0hXSW9\nJL0qdUp2mNaVJkl3Sf5hqtslDAIQgEBWCfj7emfpkKwWIMf5zmp7tKbqZFHpzYLeKoQLFGIQ\ngAAEWpZAVh0k/+rwntJ20rUlau9Z7fMQu0ukqdIuEg6SIGAQgEBmCXxKOV9cujSzJchnxrPc\nHl2vKhlXolq6tC9ymkqFbxQdj29H8XgYxe2AeRg8BgEIQCDVBLLqIE0QVX/J+st9KLtOJ+w3\n1EkchwAEIJByAh5ed7U0O+X5bLXsZbk9WkuVZad7Eck9SeUUHR8bO8dzfn1+fF883q5jxeZ2\n206SHaYkmlN0XrTtMIrbgcPpEgQMAhCoHYGsOkjuHZoh7SBdOggOl2+i9Mgg53AIAhCAQNoJ\n+EHU33dfSXtGWzB/WW6P5qu+PDTdqrV1KMHIYYqHjkeKHKxo26EdLw/9c3yxotCOWrHZObKT\nFDlN8dBO2OsljkX7SoU9Oh+DAARanEBWHaRe1dtp0kWS36peLr0ozZT8pby05DlIu0t+Q7aF\nhEEAAhDIKoEdlfFu6aqsFiDH+W7J9mhyCCMkl72c+fNaa+fLP00SOVNx56k47u1IHgK5amHb\nvWXRfse96mCxzdMOO06R7HC9Ftv2/mg7HhbHmcclUBgEskogqw6SeR8u3SGdIvnNarH5zdgl\n0h7SfcUHE277i9XCIAABCDSTgF8EXSp5bgiWPgIt0x7JW1i+M3Qe2aZ2d/3QtZKGaDTSEbBD\nFjkutfgU+BkocpocFsejfQ79LLC85Jeu0XYU2mkrNvdq2WmyPCy2XOhjxZqlfXbUMAhAoEkE\nsuwgGdk10trSatI4yV9WftvzfEFzFQ7HbtHFGyRMYOWE53EaBCAAgUoI+KFsW+mTlVzEuQ0n\nkOv26N8htG8SOr+l6T4/6wt9z8wPvTs32DmqR4XWaojhSGXOPVPRS9VS4ZKF4w79vOIwruLe\nLL8MmS3ZWYpCx4v1qvZZ3h+Fw332UVIYBFqbQNYdpDGqvvdL7na/S/Ibm2L7hHb4i+bm4gMJ\ntj+qc/wFNpQ9rhPslGEQgAAEak3gy0rwBekftU6Y9GpKILft0bzQud2I0HeinKNl5Bz9+LLQ\nc0YOnKNaVr570ezEWNWaf6JkCWkpyc8dVhR3GGlFxd8T215acTtn6tR72+YpNlOKnCeH0bbD\nYs0o7HM5MAhAQASy7CBtpPxfJq1RqEn/g+8vTS1sR8HBivjLoRoHKXpTE6VFCAEIQKDRBDyX\n8iKJlboaTT75/XLZHnWFjvVGhDY7Rp/sC22nvxm6DtVTu9tFrPYE/CL35YIqTd09WHag7CxF\n4TKFbe+zvO25WA4j2SGLzN8vsyU7T36esl6JhfG48+ltL4KBQSCXBLLqIPlNyfmSV5uZVAj3\nUfh7aYJ0jIRBAAIQyDqBL6gAm0qTsl6QHOc/d+2RPKAlFw3th6pg31S93aCJP5t0hq4Hc1yH\nWS+ae34ip6aSsvgZ0M7TsjHZeYq2l1N8Q8mh5f2LSpF5KJ8dJTtML5UIvc+KFtHiJY9gYNkg\nkFUHabzwbiJ9SrpOsvkN65HS0ZLfgJwtYRCAAASySsBve8+SjpB4OE1vLY5X1nLRHmn4xcgd\nQvvX2sKII/QkO1tP3V8cHbouTy96cjZMAvN1faW9VovoGjtLyxeFXp7d+/xCx6G3fV70nBnd\ny86ShwxHoeORni/E/fIbg0BTCUQf3KZmooqbewyuV7O5rejan2h7Mel06RnpWgmDAAQgkDUC\nnld5gfSQ5Bc/WHoJ5KI9mhtGbTMyjDhJvUbjhfrIaaFrygYDS8unlzw5awaBt3TTpwoa6v76\nOPX3OtlZ8v9JFK6kuLVlYb/jnnNlcy+Te6XsLFnPSc8WFI+/pn0YBOpGIKsO0nQR8QPE56UL\npbh9TxteUU4vw8LH4weIQwACEMgIgYOVz/dK7plg4nS6K226spfZ9miuhqWPCh3HqQw76sn0\nvK7Q/amxA8Oi0k2d3GWBQOTs2OF5YIgMj9FxP7vFtYq2La/iGcU7FLfNkZ6W/DLcctyaLtmB\nszPlXisMAlURyKqD5O7YK6VTpC2kX0j+Z7C5Z2k3ycMC/iH5n+ifEgYBCEAgCwQ+pExOlnaX\n3NBj6SaQyfZI46rGLhE6DpZnd4AWYLhTTedmHaHn7nSjJnc5JiBfPTxeULliukfKw/ZWlVaT\nVo+Fn1V8nOTeKH2s+18suefJ36FPSk8Uhf6/tQOHQaAkgaw6SC7M/0pnS/tJl0iRg6Ro/y/O\nf0HhqdIkCYMABCCQBQIeInyRdL70+yxkmDz2E8hSe9TWHdp31zyjo/V0uKA39E3SAgy/ox4h\nkAECdmiiOVPlnPl2nWPnabw0rhBOULittIZkB6pNihyyxxSP61Ft27HCeRKEVrYsO0hesWUH\nyeNWu0pUosfJutE6XfIbBwwCEIBA2gmcpgx6SN23055R8rcQgUy0R3KM1DvZNkVPhxvp+e/Y\nl0L3sXqS9IMiBoG8EOhRQdxbZJWy0do5XlpTWqsgr9Tn58nxkp+L/fxoR+mRmP5biPs3prAW\nIJBlBymqntlRpEx4R5n97IYABCCQJgJ+t1N66gAAP4FJREFUobOztIX0RpoyRl4SE0hle/Sm\n5nW0h46j5RjtptfiU3tC906LDMzbSFwwToRATgjYwXm4oOIi+ZnYvUzrFrSOwo9JX5eWl3ol\nO152lh6UPK/qP5IdqfkSliMCeXCQclQdFAUCEGhRAvur3FMk9xzd1aIMKHaNCTwZwuhVQ8cB\nSvZgOUZ6KOzdqiPMv6XGtyE5COSFgJ0c9xxZVxQValltry9tUAjVGxv2ljxCyaOY7DDdJ9lh\n8vA/y3PgsYwSwEHKaMWRbQhAIDcEvMiMH2Ldg+S5RxgEhk2gK3R8sS20Ha+ExvSF3m8fFXrO\nmzzwBnzYaZMABFqQgIfR3lRQvPgracOrjW5c0F4Kj5NGSHa0/MLr3zExpFUwsmA4SFmoJfII\nAQjkkYAnE58jeez756TrJAwCwyKwhK6+PXTsqaezleQYndYVeo7WeM03NEZojMcJYRCAQE0J\nvK7U/llQf8Jn6H/tYjlMs0J4n7qWNtWk0r01Ns8vKxaMVC+THrxv1xDX23W83ZOdsHQSwEFK\nZ70slKtpIXSODx3f0xCJiZeF7s0mDkziXugcNiAAgUwRGKvc/kHyW8etpXskDALDJnBQGKnZ\n523jnJB6kL7bGTolDAIQaBSBH+tGVglzr9L7C/qGj38h9PzyitD7EUX/Lt0oPS1hKSCAg5SC\nShgsCxom8Xk1cr/UOYtpdMSBOEeD0eIYBDJBwA+vf5QWlbaQpksYBGpC4FwtgrhvGDln8dDW\n1Rf6TpWurEnCJAIBCNSUwEfC/MvvCX03KFG3BcdKy0teBML7/iL9TXIPFQaBzBJQ72k4rJa5\nl2O0YU/o/FtP6OjW0qwnvBqCR05gEIDA8Ag8pss9RrwZ5reH35Y8cdcN4DJSI+xA3eTORtyI\ne6SCQO/iIRyptuOn0hvdoePOnjDqw6nIGZmAAATiBOLtkRaZ7F8A4psKL5PcTnRLbit+IL1H\nyoNlpj1yg42liIBeFSzTHTr/TxVzr36nomtBaNtQv25+wNIhvJaibJIVCECgMgIb6HSvHna4\n5MbuE9JMCYNAzQmoHVnQHrqP0HLe6ypxTRQfcbMcpQs032HVmt+MBCEAgVoQ6FMiXj78/yTP\nS/ULtO0kPQv2r5b3oELNuAhHShtJWJ0J4CDVGXDS5DXwdJR6ir41JnToH6DvEwtC7/Zq4D43\nOnR5FRQMAhDIJoEOZXuydLf0smRH6VeSG0MMAnUlsEgIz3WE7t00PHtL3Wgd/RbSI+5ZekaT\nyOt6YxKHAASGS8C9Rx5i931pPWkt6SzpM9J90kPSZCkvPUsqCpZHAsMaYjcvjPqU3u79Vw3X\nLPUefdfOUh4hUSYIpIBAfEhDPbPjl0+7SP5BwhclTR9smmVmSEPTCOXrxuXaoza9hJukodvP\nq52ZrmHcO+Wr2JQGApkjUG17tKZKeojk3iW/bLtN2lPKwouPzLRH9CDpE9Usm6c3AnKK/jwy\njLha6w39c27oXrsjdJ20Db/I3Kwq4b4QGC4Bf6fuKnmoxNnSFdL60lQJg0AzCfRpuPZ5s0LX\nOlq44WJ9UM+Xo3STnKb3NTNT3BsCEKiYwOO64ijpvdKG0h3SSdJzhZBeJYEYruEgDZdgFddr\n4sHient37Ej1GskxWkyv+zaVY/R1TaydUUVyXAIBCDSfwEhlIXKMzlTcK4dNkPy27FUJg0Aq\nCCyv30TqDN0HLwjd67eFvplaJfXfcpTO1IxwHcIgAIGMEfDLuO9IK0sHSJtLD0rXS1tLWJUE\ncJCqBFfNZZM1U1Zv6766eOh8VI3Sl+QY7doeuj6uxsrjSTEIQCB7BFZSln8iPSHZMXKPUeQY\nvaw4BoFUEhitz6zmue44P/Ruq1E6m48OHY9q+N0B/9aPV6Yyw2QKAhAYjMBcHTxXsoPkXmEP\n7b5BulHCURIErDkE5OsMvsy3lln9iOYZ3aUhdXOkQ7QSA7/d15y64q6tTaDaMd9xal6O9VPS\nH6Qe6VHJK9MtI6XR3IvFMt9prJn65GnI9qj4thr/OVIv776htmmG2qlH5oXOzxWfwzYEIFBz\nArVojwbL1Ho6eKG0QEqLo5SZ9ogepME+WjU49lYIq6nBuVidR/9Qcv/t1rKremt31NohdNUg\neZKAAAQaR2Bj3ernksd/ewidG53PSF5K+XiJZbsFAcsegYn6LGt+0mlvah6shn1fOzL0Xabe\npGs0FJy5DNmrTnIMgYiAFwnaTfLqqc9L7lG6TPJwPGwIAjhIQwCq9rA+iYuocZnsZVWVxgRN\niv2wllvdY9GBD2m1yXIdBCDQWALr63aHSW5o/iN9WjpdWk3Sc2X/OO8+hRgEMk9gyRBmaT7s\nt9UFtYkK06YHhPvUs3TSrBB0CIMABDJKIHKU3qv8ryh5jtLXJI+GwMoQwEEqA2Y4u+UYfXnZ\n0PnwiND2NTlG+8ox2kJv524fTppcCwEINIRAh+7iH3E9QXKj4gmwn5d+I/l3KD4gHSe9JGEQ\nyCUBzYt9UPNjP60fKv+CnqG2Gxs6p8lR+rqH4uWywBQKAq1B4H4V88PSZOlE6XrJS4ZjJQjg\nIJWAUu0uNSDv12pANwvquXKMzp+p5VTlGJ2v9HjDXC1UroNA/QlM0C32lv4oeSXJv0ibSudI\nHj7nt26/kB6XMAi0DAH9UPkV00L3hirwMVpY6JgdQsfdc8OobVoGAAWFQP4IeI7iSZL/r+dL\ndpoOkDAI1J6AXjn33hja79YE1wXSpVpKZHzt70KKEIBADQh4Uqwnie4hnStNl/wCw+Ozfy19\nSdKK+7myzEyKzRX15hXGD0CH1fr2c7QMuF4Anl1o5/6gds4vFjAIQKB6AvVepCFJzibppNel\n30rtUr0tM+3RqHqTaIX0D9Kogy1Dm98yz9ZwhI+OCp23e2krDAIQaC4BLWEc7gl9o24Jfe23\n96u385kQjlWuXpH+Lh1TCB9SiEEAAmUILBbCy1pbaG+NlDhNiw5NGaVVWaeG7uW8wEOZS9gN\nAQikn8B5yuK9kkdOePGhL0pvSC1vOEg1+Ahcp4ewg8PI+SND22saWneekuRhqwZcSQIClRL4\nc5i//HUhrPFA6F1juuYM6YluTT29jR6r34RYJYRpy4e2jd8Mfafol1t/prQZ+lopYM5veQIa\nNn63IHz0LS1UgnPU8h8HAOSDgB2kLaRrpH9I20ktP88WB0mfguHabXrOen/oPvne0LGs5h/9\nTE9dl+pXyn84JoSnhps210MAAiUJ+LtrHcmrbVnvL2gpherJDX6I89uw2yy9DnvZy0nKJ/KQ\nhumOSBgEIFAlgUVCUGcsBgEI5ITAdJVjSylqNz+t+DSpZa3cIg1+4Pi+1NayZCos+IMhvKnV\n6iapB+lDunRVDT94WKvZHa6fMtbK3hgEIDAMAqvpWv/ekL+TzpXs/HgIgFeYO1n6gHSPtJ+0\nlrS09AnpIOnPkjqSsAwToD3KcOWRdQhAIDME/Ft+bjsfkG6V3La2rJXrQfJELS9z+z/SntLT\nEpaAgIYf/FunbSnnaDct8330MqHzq12h74daNvVi7eetdQKGnNKSBPyds6a0bkH+BfD1C9L0\nh/4fVnYnkL+4fy/5N4nuk7y4ApZvArRH+a5fSgcBCKSHgEbPhh0lv4y8TPILKjtOWIzA7oob\nymuSV3zCyhMouWqQntz8Y7GHa9Wft7pDx62a3LpZ+SQ4AoHcE/ALGTtB7rrfXzpRulJ6VOqR\n/AJBi2WFu6QLpR9L/qL2ULqRUi0sDasG1aIclaSRmVWDBikU7dEgcIoOlWyPis5hEwIQaD6B\nNLdHncLjl5B/qjGmPLRH/UhW0N8/SH5wcbishL2bwKAN0twQxslJmurlUeUonftmCCu9Own2\nQCAXBJZXKfwiYGfJQ9zOlK6T/BtCkRM0T/GHpCskO0keGvdxaRWp3pbmBqleZc9Lg0R7lOwT\nMmh7lCwJzoIABBpAIO3tkUdxuEfp6zVkkZn2qNwQu4jFS4p4yT9rinS/9B3JDztxm6WNJ+I7\niL9DYEz/Yg3dE3vCqK20POpJHaHjUT0p/nx66D5x7YGhQ++cTAwC6SXQoazZiVld8rwgh9Z4\naVxB+rj32wz9fVJ6QvKw06mFuBuEZ6ReCYNAJQRojyqhxbkQgAAEhkdA0+v7f0T2lwpvkrzd\nMjaUgxSBcO/RnQX9PtoZCy9RfGJsm2gJAu1h/k2TNentkNC+V1sYceS40Pk1zU86QPOTPM4T\ng0AzCWgl7LByQasqtCPkMC6/wffCLXZutP5I/9xEz090N/zlkldttKZLb0gYBOpBgPaoHlRJ\nEwIQgMC7CZyuXR4Wf7H0QalLaglL6iD9r2gcIy0mHS75rXDcpsc3iJcnMFkPl5NDz1mv6o36\n2ND+My3koKF3nf/sDX3fkaPkCegYBGpJYEkltqLkYZ1R6HgkO0J2jPy/bYucn2cVf05yb89t\nkrcdt7xfnaAYBJpCgPaoKdi5KQQg0KIE9lK5/SL0WMmjyFrChnKQxovCWdInpdslN0yeO4AN\nk8DS/Ytf9BwwL3T+amTo++UI/ZJxt+LzQtfPFmfFkGHSzfXl+qj0L2O9vEL36BSH3mfZGXLY\nKdns+LwsvVCQe4BulZ4vkvcvkDAIpI3AeGWI9ihttUJ+IACBvBOYqQJ+RfqrdK10tZR7K+cg\n+SHMq0z9QvI5B0nHSzw4CUItbXToelTpbT8vjPrMiDDixDGhY5fu0HfoLaHn9G1CmF/Le5FW\nKgnYgVlG8gIo1nIlQu+zIxQd8/+nzU6P5/rY8fH8DIePS7cUtu3sRHpFcf5/BQHLHAHao8xV\nGRmGAARyRuAGlec46TzpPZKdplxbOQfpAyr1ydKd0iTpQSmtNloZ20TykCG/MfeKe140wt2B\ndj68nXobHeZfc2MIf9sydO7fpmGMW4aOr88Lvd/TfnvsWPoJqNrCEpI6B/tlp8dxh6UUOUSe\n+xOZHRiNvgx2Ziw7Pw4fkez8OB4PfdxOEgaBPBOgPcpz7VI2CEAgKwR+pozuJu0ueeG2XFs5\nB6lbpT5EOlZK61tn5/0IaR/JD6Kl7E7t9NjJ+0sdTNu+gR6jrimv6zdgRofOI0aGEVdrafCr\nF4S2A9TTNC1t+c1pfjwXx/N2loqFjkfyZ81xh/G4rxkpReb/ITs7fssS1zOFbTs3lo9FcTv2\nmXDolU8MAo0iQHvUKNLcBwIQgEB5Aj06dLFkJ2lK+dPycaScg3Svimel2c5U5r4onSFdJXmI\nkR9IPWTJD67rSpOku6SPSp5DlQnTHCQ9MHftpx+ZPV2LOJykOUoPKH7yG6H7CHVFvJ6JQjQn\nkx6KI3z9PTnuzbHsuBSH3lcsOz3eF3dy7KyYtx2XYj2rff68eb/DuOz0vClhEIDA8AncqySs\nNFtu26M0QydvEIBAwwlcoDv+UFpHerThd2/gDcs5SA3MQlW38gPvntJ2kieMFZsfXu+TLpGm\nSrtImXGQlNd+06p2LsPH5Rx9oS20Hb9Y6Nx5auiaMDG9vXqFnFcVLKqr7NyUk+vcxxwWx6N9\nY3XMQ93i9oY2ZkuvFULHLQ9V8z+347PKhL4mrT2oyhoGAQikgIC/f3LfHqWAM1mAAASaT8Aj\nsiz3Ih3a/OzULwdZdZAmCInf7l+fAM11Ome/BOel9hQ5Sn+cpl6y8WHUB1PkHLm3xg7JYglk\nxyY6L4oXh/GeG53e75i49yYuOyzetlPzVCHufYMJB0eAMAhAoG4EWqo9qhtFEoYABLJCwL1I\nnt6Cg5TCGnPPiudt7CBdOkj+7ADKp+if5D7Iaek/tHb/j3PN/2eVObUzs4hkhyaJ7MxE50Xx\neOi40ys2LxjgHps5BdmZieIOPQzyMSna7zCSj0dxhwxREwQMAhBIPYH7lMMZUsu0R6mvETII\nAQjUk4DnIR0tbS79q543ambaWe1B8oP4adJFkrv5Lpe8nPFMqUNaWvIcJK+0sZa0hZQF8/wp\nDzUrJTss8f3x7SheKvS+Us6Mdvf/IrIdEzs1xfJ+83RPTXSOw8HiODUChEEAAi1FIK/tUUtV\nIoWFAAQSE3hGZ94k+fkbBykxtsadeLhudYd0iuQ3d8Xm3xC6RNpD8hu+auyzumhcwgvbE573\nG52nDqG3nR07L5HjUzzMLEpyriJ2Piw7MqXiM4qORw5PdE20HV3v0IwwCEAAAhAYHoGstkfD\nKzVXQwACrUrAw+yOkr4n5fJZMqs9SNEH8hpF7GysJtmR8bwW93A8X5Adi+HYXrp4g4QJeKJu\nErtVJz0svSVFjs5goc/zG0oMAhCAAATSSyCL7VF6aZIzCEAgzQQuVeb+T/qUdHWaM1pt3rLu\nIEXldnefVWv7UsIE7cC4ByeJ/SrJSZwDAQhAAAKZJJCl9iiTgMk0BCDQdAJeLMuOkYfZ5dJB\n8uR9DAIQgAAEIAABCEAAAhCAQFICHmbnKS6eJpI7y2oPkhceWLOC2rCn68UGMAhAAAIQgEAt\nCdAe1ZImaUEAAlkhcJUy2i3tKNlZypVl1UHaWLVwSwU14cUaJlZwPqdCAAIQgAAEkhCgPUpC\niXMgAIG8EehSgTwXycPscJBSUrte6OCr0hnSTdJx0mDmJcAxCEAAAhCAQK0J0B7VmijpQQAC\nWSFwoTL6N2kFyb91mRvLag+SK+BcqU06WzpKulHCIAABCEAAAo0mQHvUaOLcDwIQSAOBfygT\nL0g7SyenIUO1ykPWF2n4tUDYcz26VkBIBwIQgAAEIFAFAdqjKqBxCQQgkGkCfcq9e5E+n+lS\nlMh8lnuQouJ4btEEyWVp5o9VfUz3P1zCIACB9BJYSlnzF/m49Gax5jnbQilm/WVYzaHUKcG0\ntEd1Kh7JQgACEHgXgSO0Z+V37c34jjw4SF6h7p4U1IN/rHbLFOSDLEAAAuUJjNahtaRcLkta\nptira/8bZY6xu7YE0tIe1bZUpAYBCECgPIE3dWha+cPZPJIHBykt5M9XRg5NS2bIBwQgUJLA\nY9p7gnROyaP53HmgisUqnvmsW0oFAQhAAAJ1IMCwizpAJUkIQAACEIAABCAAAQhAIJsEcJCy\nWW/kGgIQgAAEIAABCEAAAhCoAwEcpDpAJUkIQAACEIAABCAAAQhAIJsEcJCyWW/kGgIQgAAE\nIAABCEAAAhCoAwEcpDpAJUkIQAACEIAABCAAAQhAIJsEcJCyWW/kGgIQgAAEIAABCEAAAhCo\nAwGW+a4D1CGSfK+OLyZ53XjrrVi8S3EMAhCAAAQgAAEIQAACEGgSARykxoP/nW65bpnbLtD+\nyHEqF/oHH6NjpeLeF+2Ph04bgwAEIAABCEAAAhCAAAQGIYCDNAicOh1aT+mOlBaRFi0oHo/2\nFYdjC+cuoXBlKdr2eVHc4WiplM3Tzsh5ihynOUX7ouPF+6Nth1Hc5+J0CQIGAQhAAAIQgAAE\nIJAfAjhIzalLOxaRs1HrHNj5Knaa4g6U46W0lPavJnn4X3Q8iju9UvPVPDwwcpii8kTh64Vj\n0bbDaJ/D4ni39mEQgAAEIAABCEAAAhBoKgEcpKbir+zmz6vXSV1HdkoGMztfkQMy2HmVHGvT\nye7lihymeOh4Oa1YOLZ4Uei0is3zryInyvl/TYrKEYXRPofF8WgfvVqCg0EAAhCAAAQgAAEI\nVEcAB6k6bg29am4I40aFjuN0009ODd3LTWz80LY+3Tua91SLsruXy05V3HFyvJQ8pHBZaY3C\ncW9bPtc9XXbe4uZ8Rs7S7EI8HjpeTrN0jIUyBAGDAAQgAAEIQAACrUoABynFNe8eo2VCx480\ntu2H8lDuCaHvU01wjupByL08kZMynPQ97M+OUuQ0FYdLxo45Pk5yGFfxnC3P1bKjNLsQOl6s\nVwv74qHjDBMUBAwCEIAABCAAAQhkmQAOUkprryt07DoitB2j7LX1hr6vdYbuCxV3Tw72DoFe\nRe3IWNVapy60w7RUidD7Iq2q+Eax7aUVdw9W3Nx7NVOysxTJ26U0Q/st5516FQQMAhCAAAQg\nAAEIpIEADlIaaiGWh+7Q/gH5RFM0bmxTOUbHzwrdR2sijx+8sfoQ8JC6lwqq9A7tusAOlJ2l\nSMvE4t7n7TULoeOWF72IzL1pdqYih+mVQjweOv6y5NCip0oQMAhAAAIQgAAEIFAPAjhI9aBa\nRZpaM3vFjtBxlByjPXX5H+eH7l3HhPBUFUlxSeMI9OhWdlysSsy9Vp5XZdlhiuIOlyts26mK\ntr3PzlhkrykS3dfOneNR+GIhHoVvaBuDAAQgAAEIQAACEEhIAAcpIah6nTYthM7xoeO7Sv/H\nGmf1eAi927SH+TfV636kmwoC7rV6rqCkGfIwQDtKy8fCFQrbDt8jObTcqxWZex/tLFkvxORt\nTXN7Wx4GiEEAAhCAAAQgAIGWJ4CD1MSPgOYZfb4ttJ2gLCzeF3p/cFToOXuyPKQmZolbp5eA\n5ypZ8qmHtA6dYUfJy6xH4UqKW55LtVkh7uPuzbLZabMDFTluzxbiDiPZoZovYRCAAAQgAAEI\nQCC3BHCQmlC1cow21AIMJ2lu/lZyjE6ZE3oO12QVD5vCIFALAp6j9ExBg6XnJdI9xE8/r7WQ\n7EStI31cctxD/Wx23u1EPS05fYeWh4JOL4R8jgUCgwAEIAABCEAguwRwkBpYd6/rYXR06Dys\nLfR9Xc7RtQtC24ajQ/ejDcwCt4JAnIBXz4sWh7gvfqAo7l4mO0qrSasXQsc3kD4rjZPGSjY7\nSNOlJwt6oiicp20MAhCAAAQgAAEIpJYADlIDqubGEEZtGdr303C6w/pC30sLQu/2o8P8axpw\na24BgVoQ8PA7zY/rV7n03BNlR2l8IVxDoXuhPiONl8ZIfZKH8D0Wk4cM+iWB9+E8CQIGAQhA\nAAIQgEBzCeAg1Zn/vDBq2xFhxEkay7Rynxyk+0LXqR8IwaufYRDIE4Hot57uLlEoffz750N5\nZb61YtqpEPcP/Hr43lPSIzE9qPh/JfdyYRCAAAQgAAEIQKAhBHCQ6oRZr8LXGhk6vADD5+QY\nnT03dP1kcR706kSbZFNOwD1Hnrtk3Vwir16Zb90ibadt90KNlPzbT5Gz9IDiHg5ozZEwCEAA\nAhCAAAQgUFMCOEg1xRmCXqMvPjZ0/GRECN/RD77eqh973bQzdA02v6PGOSA5CGSOQPSbTv8s\nyvlobdtxWl/yfCeHn5LcE2WbLv2nIPdc3SV5CB8GAQhAAAIQgAAEqiaAg1Q1uoUvlEOkCRTt\nX20LI47S6/K35BjtKsfoDwufxRYEIFABAc9Jihyg+GVeEGJDaRNpY+mT0g+kRSX/vpMdJetO\n6TZppoRBAAIQgAAEIACBRARwkBJhGvwk/9DMnaF9Xy3CsJjmoZ+8IHSfqQkVXfqFTi+fjEEA\nArUn8LSStK5w0nPVXXtQCGtrHN7GsyRtf7Y7hAP1smKMvuQe1woRd+mXdu9S99JoyXOiMAhA\nAAIQgAAEIFCSAA5SSSyV7fyupkmsFEbYT7IdNCp06lkNgwAEGkWgQzc6vfzNPCTPmuhTvhh6\nTr489H5O0RsL8rwmz5PCIAABCEAAAhCAQMBBqsGH4NSwIEwKI19dVst5K7mT1Xt0oRZmWFCD\npEkCAhCoIYGNQ9cNWlP8aiXpXqRvSVMkr5L3d+kvBb2gEIMABCAAAQhAoEUJ4CDVoOKfVRrj\nQvepb4TOGfoR2Ml68vpSX1jwPX7rqAZwSQICtSXg33S6QzqnkKx/AHcb6RPSL6SzpXskO1HW\n7ZKXIMcgAAEIQAACEGgRAl5bAKsBAc136OsIXSfPDd1r6+X0DSPDiCt7QseV80KnfywTgwAE\n0knA7zfOlyZJK0ofki6XvFrezZJXxTtF+qjE3CVBwCAAAQhAAAJ5J4CDVOMa1m8dzZSjtL9e\nOb9Xz1OjR4a+B7pD+/GvhrBEjW9FchCAQG0JeB6SV747TLKjtJJ0qOTlxT1f6RnpRGlzCYMA\nBCAAAQhAIKcEcJDqVLGdofuB9tD1STlKO4cwYsfFQuc0OUpfm6yNOt2SZCEAgdoSeFnJnSl5\n+N0q0lHSptIt0oPStyUtjodBAAIQgAAEIJAnAjys17k25Sj96anQpTfQfb/UMuC/PCR03NUT\nRm1V59uSPAQgUFsCLym506StpQnSpdKPpOelcyX3OGEQgAAEIAABCOSAAA5SAypRk5K62kP3\n0V3985P8w5cjbuwOHb+f67UdMAhAIGsEnlaGfyb5/3c3aWXJP0j7b+n/SRgEIAABCEAAAhkm\nkIdV7EaL/yaS5wusIPVJs6T7JK3om57fNxkbwoshdE/SULtTNT9pyqjQ8bCW1DpuppwnPWG9\npbxiEIBAdgjMV1b/VNAaCn8gXSL5d5U8j8mLPWCtRSAz7VFrVQulhQAEINA6BOzceVnemZKd\nolLycr4bSfU2TTXqfyCq6D5doWO3ntD5jKX4rrq4raIEOBkCEKiUwGO6YK9KL6rg/NV0rofi\neTnxu6Q09CgdqHx48QmsfgQy3x7VDw0pQwACZQjUuz0qc9um7s5Me5TlIXZnqoq/Ifl3S7aW\n1pOWl/yA4h6lidIrkh9SUjk/QPOTLnwldK3bG/rOVUWco2F3t6h3aTPlF4MABLJJwCvd+Xtp\nLcm/oeQepRukNSUsvwQy3x7lt2ooGQQgAIHKCWR1iN0SKuqe0nbStSWK/az2eYidH06mSrtI\nflip1I7TBesmvMh5qtgGhtZ1/0zzkc7RkDvdr+1fcpR+2x26Dx4YkldxklwAAQg0n0DkKPk7\nxA/P90uet3SitEDC8kMgN+1RfqqEkkAAAhAYHoGsOkheRcpD6q5PUPzrdM5+Cc4rdcqT2pm0\nl62nVAJJ940J4SnNT5o4sMLdiCmdoWOaEvz59NB9ohd5SJoO50EAAqki4O+QbaX/lU6QdpY8\nxM8vcLB8EMhde5SPaqEUEIAABFqPgJ0WLXgQvjRE0e0A2kG6eIjzhnu4qjlI5W46WU6ZfzNJ\nc5Ne7g6dj2t+0g7lzmU/BCBQEYFmjvleUTl1r3a3dLiU9OWLTh2WZWbM97BK2byLc90eNQ8r\nd4ZA7gk0sz1qFtzMtEdZ7UGyQ+KJ0BdJu0leLcoO00ypQ1pa8tC43aW1pC2kzNjkEHonh56z\nXtXwwLGh/WcjQttUOUs3aa7Sd/0DtJkpCBmFAATiBPwdtZO0o3SWtJG0q6QRtliGCeS6Pcpw\nvZB1CEAAAi1L4DMq+TTJw+2K1aN9dqA2keptNe1BKs7svNC5Tk/ouFKapQlVI4uPsw0BCCQm\nkJY3dn6B4+F3N0t+oVNPy8wbu3pCaEDaLdEeNYAjt4BAqxBIS3vUSN6ZaY+y2oMUVeY1imiK\nTv/KdeMULi7Nkfzr9lYu3syODl2Pqizbzw5hKS3NxwRvwcAgkHECjyj/H5aulm6R/HCteYhY\nhgm0RHuU4foh6xCAAAQSE8i6gxQV1CtGWbm2JQd+ADfXZaRwEGghAi+orFtJf5Julbwq538k\nLNsEWqI9ynYVkXsIQAACgxNo1CThwXPBUQhAAAKtScA93naM/i7dJNlhwiAAAQhAAAIQaCIB\nHKQmwufWEIAABESgW/KCMhdIl0pe7Q6DAAQgAAEIQKBJBHCQmgSe20IAAhCIEfAiM9+VvHDD\nb6Q2CYMABCAAAQhAoAkEcJCaAJ1bQgACEChBwCtvetlv/yzB90scZxcEIAABCEAAAg0ggIPU\nAMjcAgIQgEBCAo/rvG9KR0mbJryG0yAAAQhAAAIQqCEBHKQawiQpCEAAAjUg8Ful4blIF0uL\n1iA9koAABCAAAQhAoAICOEgVwOJUCEAAAg0isJ/u0y5NadD9uA0EIAABCEAAAgUCOEh8FCAA\nAQikj8DrytJu0p7STunLHjmCAAQgAAEI5JcADlJ+65aSQQAC2SZwm7J/uHSmtEK2i0LuIQAB\nCEAAAtkhgIOUnboipxCAQOsR+LmKPEv6SusVnRJDAAIQgAAEmkMAB6k53LkrBCAAgSQEenXS\nhZJ/SBaDAAQgAAEIQKABBHCQGgCZW0AAAhAYBgE7SJtIGwwjDS6FAAQgAAEIQCAhARykhKA4\nDQIQgECTCDys+94l0YvUpArgthCAAAQg0FoEcJBaq74pLQQgkE0C7kXaRWrLZvbJNQQgAAEI\nQCA7BHCQslNX5BQCEGhdAr9T0VeVPtK6CCg5BCAAAQhAoDEEcJAaw5m7QAACEBgOgRd08Q0S\nw+yGQ5FrIQABCEAAAgkI4CAlgMQpEIAABFJAwMPs/KOxHSnIC1mAAAQgAAEI5JYADlJuq5aC\nQQACOSPwR5VntPTZnJWL4kAAAhCAAARSRQAHKVXVQWYgAAEIlCUwR0culxhmVxYRByAAAQhA\nAALDJ4CDNHyGpAABCECgUQQ8zG57afFG3ZD7QAACEIAABFqNAA5Sq9U45YUABLJM4Bpl/k3p\nS1kuBHmHAAQgAAEIpJkADlKaa4e8QQACEFiYQI82p0q7LbybLQhAAAIQgAAEakUAB6lWJEkH\nAhCAQGMIeJjdx6RVGnM77gIBCEAAAhBoLQI4SK1V35QWAhDIPoFbVISnpF2yXxRKAAEIQAAC\nEEgfARyk9NUJOYIABCAwFIELdALLfQ9FieMQgAAEIACBKgiMquIaLoEABCAAgeYS+Lluv2xz\ns8DdIQABCEAAAvkkQA9SPuuVUkEAAvkm0KXiPZfvIlI6CEAAAhCAQHMI4CA1hzt3hQAEIAAB\nCEAAAhCAAARSSAAHKYWVQpYgAAEIQAACEIAABCAAgeYQwEFqDnfuCgEIQAACEIAABCAAAQik\nkAAOUgorhSxBAAIQgAAEIAABCEAAAs0hkIdV7EYL3SbSStIKUp80S7pPerSwrQCDAAQgAAEI\n1JUA7VFd8ZI4BCAAgcYQyLKD5LwfIe0jLV0G153av5d0f5nj7IYABCAAAQgMlwDt0XAJcj0E\nIACBFBHI8hC7M8XxG9LZ0tbSetLy0mqSe5QmSq9Id0kfkjAIQAACEIBAPQjQHtWDKmlCAAIQ\naBKBrPYgLSFee0rbSdeWYPes9nmI3SXSVGkX6XYJgwAEIAABCNSSAO1RLWmSFgQgAIEUEMhq\nD9IEsfNco+sTMLxO52yV4DxOgQAEIAABCFRKgPaoUmKcDwEIQCDlBLLqILl3aIa0wxB83UPm\noXaPDHEehyEAAQhAAALVEKA9qoYa10AAAhBIMYGsDrHrFdPTpIuk3aTLpRelmVKHtLS0rrS7\ntJa0hYRBAAIQgAAEak2A9qjWREkPAhCAQJMJZNVBMrbDpTukU6RSPUnztd9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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m1 <- mean(df02$Y[df02$A == \"SPOT\"])\n", "m2 <- mean(df02$Y[df02$A == \"DEoptim\"])\n", "#m3 <- mean(df02$Y[df02$A == \"RS\"])\n", "m4 <- mean(df02$Y[df02$A == \"GenSA\"])\n", "\n", "sYbar.. <- sqrt(max(0, MSAB/(b*n)) )\n", "\n", "plotConfInt <- function(ybar, sYbar..,dfree=(a-1), alg=\"\", val=\"\"){\n", " muLo95 <- ybar - qt(0.95, dfree) * sYbar..\n", " muHi95 <- ybar + qt(0.95, dfree) * sYbar..\n", "alpha <- seq(0.01,0.5,0.01)\n", "conf <- 1- alpha/2\n", "muLo <- ybar - qt(conf, dfree) * sYbar..\n", "muHi <- ybar + qt(conf, dfree) * sYbar..\n", "# plot(muLo~conf, type = \"l\", ylim=c(min(muHi,muLo), max(muHi,muLo) ), ylab=\"Y\")\n", "plot(muLo~conf, type = \"l\", ylim=c(-10, 35 ), xlab = paste(alg, \" CI\", val), ylab=\"Y\")\n", "abline(ybar,0)\n", "abline(v=0.95)\n", " arrows(0.74, muHi95, 0.95, muHi95, col= 2, code =1)\n", "arrows(0.74, muLo95, 0.95, muLo95, col= 2, code = 1)\n", "lines(conf,muHi)\n", "return( round( c(ybar - qt(0.95, dfree) * sYbar.., ybar + qt(0.95, dfree) * sYbar..),2))\n", " }\n", "#pdf(\"confInt.pdf\")\n", "par(mfrow=c(2,2))\n", "plotConfInt(ybar=m1, sYbar..=sYbar..,dfree=(a-1),alg=\"SPOT\")\n", "plotConfInt(ybar=m2, sYbar..=sYbar..,dfree=(a-1), alg=\"DEoptim\")\n", "plotConfInt(ybar=m3, sYbar..=sYbar..,dfree=(a-1), alg=\"RS\")\n", "plotConfInt(ybar=m4, sYbar..=sYbar..,dfree=(a-1), alg=\"GenSA\")\n", "par(mfrow=c(1,1))\n", "#dev.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-6] Estimators of the marginal mean" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:29.601301Z", "start_time": "2019-04-15T18:55:29.582Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 0.18
    2. \n", "\t
  2. 3.35
    3. \n", "\t
  3. 8.17
    4. \n", "\t
  4. 22.94
    5. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0.18\n", "\\item 3.35\n", "\\item 8.17\n", "\\item 22.94\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0.18\n", "2. 3.35\n", "3. 8.17\n", "4. 22.94\n", "\n", "\n" ], "text/plain": [ "[1] 0.18 3.35 8.17 22.94" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y1. <- mean(df02$Y[df02$A == \"SPOT\"])\n", "y2. <- mean(df02$Y[df02$A == \"DEoptim\"])\n", "y3. <- mean(df02$Y[df02$A == \"RS\"])\n", "y4. <- mean(df02$Y[df02$A == \"GenSA\"])\n", "round( (c(y1., y2., \n", " y3., \n", " y4.)),2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-7] Confidence intervals for Marginal Means" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:31.231237Z", "start_time": "2019-04-15T18:55:31.212Z" } }, "outputs": [], "source": [ "# The Satterthwaite procedure can be used to estimate the magnitude \n", "# of the variance component $\\sigma_\\beta^2$, when the random factor has significant main effects. \n", "# It is included in the R package SPOT \n", "# https://cran.r-project.org/web/packages/SPOT/index.html\n", "# source(\"../../../spot2/sourceDevelopment/misc/satter.R\")\n", "c1 <- (a-1)/(n*a*b)\n", "c2 <- 1/(n*a*b)\n", "satres <- satter(MScoeff=c(c1, c2),\n", " MSi=c(MSAB,MSB),\n", " dfi =c((a-1)*(b-1),(b-1)),\n", " alpha=0.1)\n", "dfsat <- satres[,\"df\"]\n", "\n", "smuei <- sqrt( max(0, c1*MSAB + c2*MSB)) # (25.64)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:31.833331Z", "start_time": "2019-04-15T18:55:31.741Z" }, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. -5.25
    2. \n", "\t
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TN0+XpdCgIINJ/AjFqldZWNFX9f9m27fbtuN5L+reQVSoFAlhtIXpWrlEWV+RU3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\ngAACCCCAAAIIIIAAAggggAACCCCAA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hn1vuUBBAAAEEEKgk0NT1UXcYvXlvaD9VrZJVdHb7sdNC8ZTVQuiqhJEY78Zj3DhJ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\nfUqgG0zl8rLGl8YNL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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# pdf(\"confInt.pdf\")\n", "par(mfrow=c(2,2))\n", "plotConfInt(ybar=y1., sYbar..=smuei,dfree=dfsat, alg=\"SPOT\", val=\"Marginal Means\")\n", "plotConfInt(ybar=y2., sYbar..=smuei,dfree=dfsat, alg=\"DEOptim\", val=\"Marginal Means\")\n", "plotConfInt(ybar=y3., sYbar..=smuei,dfree=dfsat, alg=\"RS\", val=\"Marginal Means\")\n", "plotConfInt(ybar=y4., sYbar..=smuei,dfree=dfsat, alg=\"GenSA\", val=\"Marginal Means\")\n", "par(mfrow=c(1,1))\n", "#dev.off()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## [MAMP-8] Estimation of the variance components" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:37.553722Z", "start_time": "2019-04-15T18:55:37.535Z" } }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 16.22
    2. \n", "\t
  2. 22.66
    3. \n", "\t
  3. 56.93
    4. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 16.22\n", "\\item 22.66\n", "\\item 56.93\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 16.22\n", "2. 22.66\n", "3. 56.93\n", "\n", "\n" ], "text/plain": [ "[1] 16.22 22.66 56.93" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "var.beta <- (MSB-MSE)/(a*n)\n", "var.tau.beta <- (MSAB-MSE)/n\n", "var <- MSE\n", "round( (c(var.beta, var.tau.beta, var)),2)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:46.403012Z", "start_time": "2019-04-15T18:55:46.388Z" } }, "outputs": [ { "data": { "text/html": [ "16.218815217878" ], "text/latex": [ "16.218815217878" ], "text/markdown": [ "16.218815217878" ], "text/plain": [ "[1] 16.21882" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Check if the estimated of the interaction variance component $\\sigma_{\\tau\\beta}^2$ is negative,\n", "## fit a reduced model, without interactions A*B\n", "if(var.tau.beta < 0) {\n", " h <- lm(Y ~ A + B, data = df02)\n", " (h.anova <- anova(h))\n", " xtable(h.anova)\n", " MSA <- h.anova$`Mean Sq`[1]\n", " MSB <- h.anova$`Mean Sq`[2]\n", " MSE <- h.anova$`Mean Sq`[3]\n", " (var.beta <- (MSB-MSE)/(a*n))\n", "}\n", "var.beta" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:50.252145Z", "start_time": "2019-04-15T18:55:50.235Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\n", "
estimatedflowerupper
16.22 3 6.23 138.29
\n" ], "text/latex": [ "\\begin{tabular}{llll}\n", " estimate & df & lower & upper\\\\\n", "\\hline\n", "\t 16.22 & 3 & 6.23 & 138.29\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| estimate | df | lower | upper |\n", "|---|---|---|---|\n", "| 16.22 | 3 | 6.23 | 138.29 |\n", "\n" ], "text/plain": [ " estimate df lower upper \n", "[1,] 16.22 3 6.23 138.29" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# The Satterthwaite procedure can be used to estimate the magnitude of the variance component $\\sigma_\\beta^2$, when the random factor has significant main effects. \n", "# source(\"../../../spot2/sourceDevelopment/misc/satter.R\")\n", "resS <- satter(MScoeff=c(1/(n*a), -1/(n*a)),\n", " MSi=c(MSB,MSE),\n", " dfi =c(b-1,(n-1)*a*b),\n", " alpha=0.1)\n", "# Note, the output from the \\texttt{satter()} procedure is $\\sigma_\\beta$, i.e., we have to consider: \n", "# <>=\n", "# sqrt(var.beta)\n", "# @\n", "round (resS,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using nlme" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:54.301934Z", "start_time": "2019-04-15T18:55:54.161Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
YAB
9.563410e-07SPOT 1
1.091241e-05SPOT 1
1.082825e-05SPOT 1
2.926531e-06SPOT 1
5.668585e-06SPOT 1
1.745022e-06SPOT 1
\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " Y & A & B\\\\\n", "\\hline\n", "\t 9.563410e-07 & SPOT & 1 \\\\\n", "\t 1.091241e-05 & SPOT & 1 \\\\\n", "\t 1.082825e-05 & SPOT & 1 \\\\\n", "\t 2.926531e-06 & SPOT & 1 \\\\\n", "\t 5.668585e-06 & SPOT & 1 \\\\\n", "\t 1.745022e-06 & SPOT & 1 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "| Y | A | B |\n", "|---|---|---|\n", "| 9.563410e-07 | SPOT | 1 |\n", "| 1.091241e-05 | SPOT | 1 |\n", "| 1.082825e-05 | SPOT | 1 |\n", "| 2.926531e-06 | SPOT | 1 |\n", "| 5.668585e-06 | SPOT | 1 |\n", "| 1.745022e-06 | SPOT | 1 |\n", "\n" ], "text/plain": [ " Y A B\n", "1 9.563410e-07 SPOT 1\n", "2 1.091241e-05 SPOT 1\n", "3 1.082825e-05 SPOT 1\n", "4 2.926531e-06 SPOT 1\n", "5 5.668585e-06 SPOT 1\n", "6 1.745022e-06 SPOT 1" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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c8/tzoOS7NPteHe/JeL+Pi/vl2r1MOXqj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\nAABJRU5ErkJggg==", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "library(nlme)\n", "head(masprep01 <- groupedData( Y ~ A | B, data = df02) )\n", "#levels(masprep01$A)\n", "#levels(masprep01$A) <- c(\"x2\", \"x4\", \"x3\", \"x1\")\n", "#masprep01$A <- ordered(masprep01$A, levels = c(\"x1\", \"x2\", \"x3\", \"x4\"))\n", "masprep01$A <- ordered(masprep01$A, levels = c(\"SPOT\", \"DEoptim\", \n", " #\"RS\", \n", " \"GenSA\"))\n", "#masprep01$A <- factor(masprep01$A, ordered = FALSE)\n", "plot(masprep01)\n", "#factor(masprep01$A, ordered = FALSE)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "ExecuteTime": { "end_time": "2019-04-15T18:55:56.944965Z", "start_time": "2019-04-15T18:55:56.895Z" } }, "outputs": [ { "data": { "image/png": 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8tRxyTeMtHRDiLQEwFnkHoykcogQIAA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\nashn9wbId0K3YzZWuBbk//pKkO+QnEtcA2N9lnM+sCLkNdkHQ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