Software-Analytics-In-Action.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Software Analytics in Action\n",
    "## An Interactive Hands-on Tutorial on Mining, Analyzing, Modelling, and Explaining Software Data\n",
    "by Dr. Chakkrit (Kla) Tantithamthavorn, Monash University, Melbourne, Australia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this tutorial\n",
    "Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via RStudio."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outline\n",
    "\n",
    "* [Part I: Mining Software Data](#mining)\n",
    "* [Part II: Analyzing Software Data](#analyzing)\n",
    "* [Part III: Modelling Software Data](#modelling)\n",
    "* [Part IV: Explaining Software Data](#explaining)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I: Mining Software Data <a class=\"anchor\" id=\"mining\"></a>\n",
    "\n",
    "This part is in progress. The content will be available soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part II: Analyzing Software Data <a class=\"anchor\" id=\"mining\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's suppress all warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(warn=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Install necessary R packages for this tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered S3 methods overwritten by 'ggplot2':\n",
      "  method         from \n",
      "  [.quosures     rlang\n",
      "  c.quosures     rlang\n",
      "  print.quosures rlang\n",
      "Registered S3 method overwritten by 'xts':\n",
      "  method     from\n",
      "  as.zoo.xts zoo \n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "Loading required package: carData\n",
      "Registered S3 methods overwritten by 'lme4':\n",
      "  method                          from\n",
      "  cooks.distance.influence.merMod car \n",
      "  influence.merMod                car \n",
      "  dfbeta.influence.merMod         car \n",
      "  dfbetas.influence.merMod        car \n",
      "lattice theme set by effectsTheme()\n",
      "See ?effectsTheme for details.\n",
      "Loading required package: reshape2\n",
      "Loading required package: effsize\n",
      "Registered S3 methods overwritten by 'forecast':\n",
      "  method             from    \n",
      "  fitted.fracdiff    fracdiff\n",
      "  residuals.fracdiff fracdiff\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following object is masked from ‘package:gridExtra’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following object is masked from ‘package:car’:\n",
      "\n",
      "    recode\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "Loading required package: Hmisc\n",
      "Loading required package: lattice\n",
      "Loading required package: survival\n",
      "Loading required package: Formula\n",
      "\n",
      "Attaching package: ‘Hmisc’\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    src, summarize\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    format.pval, units\n",
      "\n",
      "Loading required package: SparseM\n",
      "\n",
      "Attaching package: ‘SparseM’\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    backsolve\n",
      "\n",
      "\n",
      "Attaching package: ‘rms’\n",
      "\n",
      "The following objects are masked from ‘package:car’:\n",
      "\n",
      "    Predict, vif\n",
      "\n",
      "randomForest 4.6-14\n",
      "Type rfNews() to see new features/changes/bug fixes.\n",
      "\n",
      "Attaching package: ‘randomForest’\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following object is masked from ‘package:gridExtra’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following object is masked from ‘package:ggplot2’:\n",
      "\n",
      "    margin\n",
      "\n",
      "\n",
      "   O      \n",
      "  / \\     \n",
      " F   O  \n",
      "    / \\   \n",
      "   F   Trees 1.4.0\n",
      "\n",
      "Nathaniel.D.Phillips.is@gmail.com\n",
      "FFTrees.guide() opens the guide.\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "The following object is masked from ‘package:survival’:\n",
      "\n",
      "    cluster\n",
      "\n",
      "Loading required package: grid\n"
     ]
    }
   ],
   "source": [
    "source(\"import.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Load an example defect dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "eclipse <- loadDefectDataset(\"eclipse-2.0\")\n",
    "data <- eclipse$data\n",
    "indep <- eclipse$indep\n",
    "dep <- eclipse$dep\n",
    "data[,dep] <- factor(data[,dep])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Understand your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data \n",
       "\n",
       " 33  Variables      6729  Observations\n",
       "--------------------------------------------------------------------------------\n",
       "pre \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       36    0.763    1.135    1.825        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        1        3        5 \n",
       "\n",
       "lowest :  0  1  2  3  4, highest: 34 36 40 49 69\n",
       "--------------------------------------------------------------------------------\n",
       "ACD \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       20     0.39   0.4235   0.7742        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        0        1        3 \n",
       "                                                                            \n",
       "Value          0     1     2     3     4     5     6     7     8     9    10\n",
       "Frequency   5705   443   205   115    94    52    26    26    13    17     7\n",
       "Proportion 0.848 0.066 0.030 0.017 0.014 0.008 0.004 0.004 0.002 0.003 0.001\n",
       "                                                                \n",
       "Value         11    12    13    14    15    16    17    20    24\n",
       "Frequency      6     7     4     3     2     1     1     1     1\n",
       "Proportion 0.001 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000\n",
       "--------------------------------------------------------------------------------\n",
       "FOUT_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0     1367    0.982    2.862     3.31    0.000    0.000 \n",
       "     .25      .50      .75      .90      .95 \n",
       "   0.000    2.000    4.250    6.898    9.000 \n",
       "\n",
       "lowest :  0.000000000  0.004000000  0.007194245  0.010723861  0.015037594\n",
       "highest: 31.833333333 32.000000000 33.000000000 35.000000000 51.619047619\n",
       "--------------------------------------------------------------------------------\n",
       "FOUT_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      108    0.982    10.53    13.31      0.0      0.0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "     0.0      6.0     14.0     27.0     36.6 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 220 222 231 249 310\n",
       "--------------------------------------------------------------------------------\n",
       "FOUT_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      378    0.982    40.71    59.71        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0       12       43      110      175 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest: 1033 1076 1564 1656 2168\n",
       "--------------------------------------------------------------------------------\n",
       "MLOC_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0     1717    0.992    5.601    6.175     0.00     0.00 \n",
       "     .25      .50      .75      .90      .95 \n",
       "    1.00     4.00     8.00    12.70    16.67 \n",
       "\n",
       "lowest :  0.000000000  0.002680965  0.016000000  0.019184652  0.042606516\n",
       "highest: 79.500000000 80.000000000 80.769230769 92.000000000 96.166666667\n",
       "--------------------------------------------------------------------------------\n",
       "MLOC_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      178    0.992    20.28    25.65        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       1       11       27       50       70 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 499 526 599 662 994\n",
       "--------------------------------------------------------------------------------\n",
       "MLOC_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      563    0.992    79.66    115.9      0.0      0.0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "     3.0     26.0     81.0    207.0    328.6 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest: 2393 2447 3526 3793 4559\n",
       "--------------------------------------------------------------------------------\n",
       "NBD_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      694    0.986    1.229   0.8579    0.000    0.000 \n",
       "     .25      .50      .75      .90      .95 \n",
       "   1.000    1.250    1.667    2.120    2.469 \n",
       "\n",
       "lowest : 0.000000000 0.002680965 0.004000000 0.004796163 0.005012531\n",
       "highest: 4.666666667 5.000000000 5.500000000 6.000000000 7.000000000\n",
       "--------------------------------------------------------------------------------\n",
       "NBD_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       13    0.972     2.33    2.073        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       1        2        4        5        6 \n",
       "                                                                            \n",
       "Value          0     1     2     3     4     5     6     7     8     9    10\n",
       "Frequency   1314  1277  1259  1166   866   443   240    99    32    20    11\n",
       "Proportion 0.195 0.190 0.187 0.173 0.129 0.066 0.036 0.015 0.005 0.003 0.002\n",
       "                      \n",
       "Value         11    17\n",
       "Frequency      1     1\n",
       "Proportion 0.000 0.000\n",
       "--------------------------------------------------------------------------------\n",
       "NBD_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      175    0.992    15.94    20.57        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2        8       18       39       60 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 304 332 420 560 646\n",
       "--------------------------------------------------------------------------------\n",
       "NOF_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      114    0.932     2.63    3.647        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        1        3        7       10 \n",
       "\n",
       "lowest :   0.0000000   0.3333333   0.4444444   0.5000000   0.6363636\n",
       "highest:  86.0000000  94.0000000  99.0000000 204.0000000 251.0000000\n",
       "--------------------------------------------------------------------------------\n",
       "NOF_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       54    0.932    2.927    4.097        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        1        4        7       11 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest:  86  94  99 204 251\n",
       "--------------------------------------------------------------------------------\n",
       "NOF_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       60    0.932     3.07    4.332        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        1        4        8       12 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest:  86  94  99 204 251\n",
       "--------------------------------------------------------------------------------\n",
       "NOI \n",
       "       n  missing distinct     Info      Sum     Mean      Gmd \n",
       "    6729        0        2    0.399     1064   0.1581   0.2663 \n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "NOM_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      186    0.996    8.781    10.01        0        1 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2        5       10       19       29 \n",
       "\n",
       "lowest :   0.0000000   0.5000000   0.6666667   1.0000000   1.3333333\n",
       "highest: 205.0000000 216.0000000 220.0000000 236.0000000 297.0000000\n",
       "--------------------------------------------------------------------------------\n",
       "NOM_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      103    0.996    9.515    11.06        0        1 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2        5       11       21       32 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 216 219 220 236 297\n",
       "--------------------------------------------------------------------------------\n",
       "NOM_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      110    0.996     9.96     11.7        0        1 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2        6       12       22       34 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 216 220 236 272 297\n",
       "--------------------------------------------------------------------------------\n",
       "NOT \n",
       "       n  missing distinct     Info     Mean      Gmd \n",
       "    6729        0        3      0.4   0.8423   0.2672 \n",
       "                            \n",
       "Value          0     1     2\n",
       "Frequency   1064  5662     3\n",
       "Proportion 0.158 0.841 0.000\n",
       "--------------------------------------------------------------------------------\n",
       "NSF_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      120      0.7    2.132    3.874      0.0      0.0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "     0.0      0.0      1.0      3.5      7.0 \n",
       "\n",
       "lowest :    0.0000000    0.1428571    0.2000000    0.2500000    0.2857143\n",
       "highest:  283.0000000  385.0000000  446.0000000  479.0000000 1049.0000000\n",
       "--------------------------------------------------------------------------------\n",
       "NSF_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       76      0.7    2.297    4.151        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        1        4        8 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest:  283  385  446  479 1049\n",
       "--------------------------------------------------------------------------------\n",
       "NSF_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       77      0.7    2.301    4.159        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        1        4        8 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest:  283  385  446  479 1049\n",
       "--------------------------------------------------------------------------------\n",
       "NSM_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       73    0.477     1.13    2.138        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        0        1        4 \n",
       "\n",
       "lowest :   0.0000000   0.1428571   0.1666667   0.2000000   0.2500000\n",
       "highest: 373.0000000 399.0000000 417.0000000 500.0000000 596.0000000\n",
       "--------------------------------------------------------------------------------\n",
       "NSM_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       48    0.477    1.188     2.24        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        0        2        4 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 373 399 417 500 596\n",
       "--------------------------------------------------------------------------------\n",
       "NSM_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       48    0.477     1.19    2.244        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       0        0        0        2        4 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 373 399 417 500 596\n",
       "--------------------------------------------------------------------------------\n",
       "PAR_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      732    0.996   0.9488    0.742   0.0000   0.0000 \n",
       "     .25      .50      .75      .90      .95 \n",
       "  0.5000   0.8462   1.2000   1.8412   2.2500 \n",
       "\n",
       "lowest : 0.00000000 0.05263158 0.05882353 0.06666667 0.07142857\n",
       "highest: 6.64705882 6.80000000 7.00000000 7.16666667 8.00000000\n",
       "--------------------------------------------------------------------------------\n",
       "PAR_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       20    0.956    2.319    1.829        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       1        2        3        4        5 \n",
       "                                                                            \n",
       "Value          0     1     2     3     4     5     6     7     8     9    10\n",
       "Frequency    711  1667  1814  1315   614   299   130    71    27    33    12\n",
       "Proportion 0.106 0.248 0.270 0.195 0.091 0.044 0.019 0.011 0.004 0.005 0.002\n",
       "                                                                \n",
       "Value         11    12    13    14    16    17    24    25    28\n",
       "Frequency      6     8    12     3     2     2     1     1     1\n",
       "Proportion 0.001 0.001 0.002 0.000 0.000 0.000 0.000 0.000 0.000\n",
       "--------------------------------------------------------------------------------\n",
       "PAR_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      138    0.995    11.77    15.53        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2        5       12       25       39 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest:  804  968 1060 1187 1753\n",
       "--------------------------------------------------------------------------------\n",
       "TLOC \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      685        1    118.4    152.3      6.0      7.0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "    18.0     51.0    129.0    291.2    437.6 \n",
       "\n",
       "lowest :    3    4    5    6    7, highest: 2632 2638 4183 4636 5207\n",
       "--------------------------------------------------------------------------------\n",
       "CC_avg \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0     1007     0.99    1.916    1.753    0.000    0.000 \n",
       "     .25      .50      .75      .90      .95 \n",
       "   1.000    1.605    2.625    3.800    4.922 \n",
       "\n",
       "lowest :  0.000000000  0.002680965  0.004000000  0.004796163  0.007518797\n",
       "highest: 19.538461538 20.533333333 21.952380952 25.047619048 41.166666667\n",
       "--------------------------------------------------------------------------------\n",
       "CC_max \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0       74    0.987     5.62     6.82        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       1        3        7       13       19 \n",
       "\n",
       "lowest :   0   1   2   3   4, highest: 191 207 221 234 310\n",
       "--------------------------------------------------------------------------------\n",
       "CC_sum \n",
       "       n  missing distinct     Info     Mean      Gmd      .05      .10 \n",
       "    6729        0      268    0.992     26.9    37.54        0        0 \n",
       "     .25      .50      .75      .90      .95 \n",
       "       2       10       28       67      106 \n",
       "\n",
       "lowest :    0    1    2    3    4, highest:  922 1013 1035 1052 1299\n",
       "--------------------------------------------------------------------------------\n",
       "post \n",
       "       n  missing distinct \n",
       "    6729        0        2 \n",
       "                      \n",
       "Value      FALSE  TRUE\n",
       "Frequency   5754   975\n",
       "Proportion 0.855 0.145\n",
       "--------------------------------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Understand the defective ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "    FALSE      TRUE \n",
       "0.8551048 0.1448952 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(data[,dep])/nrow(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INTRO: PRELIMINARY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Check data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "    0.0     2.0    10.0    26.9    28.0  1299.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(data$CC_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Hypothesis Testing (CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  data[data$post == TRUE, ]$CC_sum and data[data$post == FALSE, ]$CC_sum\n",
       "W = 4380955, p-value < 2.2e-16\n",
       "alternative hypothesis: true location shift is not equal to 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(data[data$post==TRUE,]$CC_sum, data[data$post==FALSE,]$CC_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's take a look at the data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KWzn5/RaJw2bRqPPiEicnAsduTQli5d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\nFVdccXmGxuuUB0gV/nMTfF9rcbN2Q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C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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data, aes(x=CC_sum, fill=post)) + geom_density(alpha=.3) + scale_x_log10() + theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Test Alternative Hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  data[data$post == TRUE, ]$CC_sum and data[data$post == FALSE, ]$CC_sum\n",
       "W = 4380955, p-value < 2.2e-16\n",
       "alternative hypothesis: true location shift is greater than 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tWilcoxon rank sum test with continuity correction\n",
       "\n",
       "data:  data[data$post == TRUE, ]$CC_sum and data[data$post == FALSE, ]$CC_sum\n",
       "W = 4380955, p-value = 1\n",
       "alternative hypothesis: true location shift is less than 0\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wilcox.test(data[data$post==TRUE,]$CC_sum, data[data$post==FALSE,]$CC_sum, alternative = \"greater\")\n",
    "wilcox.test(data[data$post==TRUE,]$CC_sum, data[data$post==FALSE,]$CC_sum, alternative = \"less\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cliff's delta effect size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Cliff's Delta\n",
       "\n",
       "delta estimate: 0.561796 (large)\n",
       "95 percent confidence interval:\n",
       "    lower     upper \n",
       "0.5304041 0.5916494 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cliff.delta(data[data$post==TRUE,]$CC_sum, data[data$post==FALSE,]$CC_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "source(\"import.R\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part III: Modelling Software Data <a class=\"anchor\" id=\"modelling\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INTRO: BASIC REGRESSION ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building a logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "m <- glm(post ~ CC_max, data = data, family=\"binomial\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's take a look how a model looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = post ~ CC_max, family = \"binomial\", data = data)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-6.6213  -0.5113  -0.4410  -0.3991   2.2671  \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -2.490129   0.051777  -48.09   <2e-16 ***\n",
       "CC_max       0.104319   0.004819   21.65   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 5568.3  on 6728  degrees of freedom\n",
       "Residual deviance: 4967.3  on 6727  degrees of freedom\n",
       "AIC: 4971.3\n",
       "\n",
       "Number of Fisher Scoring iterations: 5\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What about the model visualization?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EGwAwAASBEEOwAAgBRBsAMAAEgRBDsA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\nydwh/cRJwgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(allEffects(m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP1: INCLUDE CONTROL METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NULL</th><td>NA</td><td>       NA</td><td>6728</td><td>5568.263</td></tr>\n",
       "\t<tr><th scope=row>CC_max</th><td> 1</td><td>600.97611</td><td>6727</td><td>4967.287</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td> 1</td><td>131.45283</td><td>6726</td><td>4835.835</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 1</td><td> 60.20786</td><td>6725</td><td>4775.627</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 4 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & Deviance & Resid. Df & Resid. Dev\\\\\n",
       "  & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNULL & NA &        NA & 6728 & 5568.263\\\\\n",
       "\tCC\\_max &  1 & 600.97611 & 6727 & 4967.287\\\\\n",
       "\tPAR\\_max &  1 & 131.45283 & 6726 & 4835.835\\\\\n",
       "\tFOUT\\_max &  1 &  60.20786 & 6725 & 4775.627\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 4 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;int&gt; | Deviance &lt;dbl&gt; | Resid. Df &lt;int&gt; | Resid. Dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NULL | NA |        NA | 6728 | 5568.263 |\n",
       "| CC_max |  1 | 600.97611 | 6727 | 4967.287 |\n",
       "| PAR_max |  1 | 131.45283 | 6726 | 4835.835 |\n",
       "| FOUT_max |  1 |  60.20786 | 6725 | 4775.627 |\n",
       "\n"
      ],
      "text/plain": [
       "         Df Deviance  Resid. Df Resid. Dev\n",
       "NULL     NA        NA 6728      5568.263  \n",
       "CC_max    1 600.97611 6727      4967.287  \n",
       "PAR_max   1 131.45283 6726      4835.835  \n",
       "FOUT_max  1  60.20786 6725      4775.627  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m1 <- glm(post ~ CC_max + PAR_max + FOUT_max, data = data, family=\"binomial\")\n",
    "anova(m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NULL</th><td>NA</td><td>       NA</td><td>6728</td><td>5568.263</td></tr>\n",
       "\t<tr><th scope=row>TLOC</th><td> 1</td><td>709.19340</td><td>6727</td><td>4859.070</td></tr>\n",
       "\t<tr><th scope=row>CC_max</th><td> 1</td><td> 74.55689</td><td>6726</td><td>4784.513</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td> 1</td><td> 63.35217</td><td>6725</td><td>4721.161</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 1</td><td> 17.40582</td><td>6724</td><td>4703.755</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & Deviance & Resid. Df & Resid. Dev\\\\\n",
       "  & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNULL & NA &        NA & 6728 & 5568.263\\\\\n",
       "\tTLOC &  1 & 709.19340 & 6727 & 4859.070\\\\\n",
       "\tCC\\_max &  1 &  74.55689 & 6726 & 4784.513\\\\\n",
       "\tPAR\\_max &  1 &  63.35217 & 6725 & 4721.161\\\\\n",
       "\tFOUT\\_max &  1 &  17.40582 & 6724 & 4703.755\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 5 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;int&gt; | Deviance &lt;dbl&gt; | Resid. Df &lt;int&gt; | Resid. Dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NULL | NA |        NA | 6728 | 5568.263 |\n",
       "| TLOC |  1 | 709.19340 | 6727 | 4859.070 |\n",
       "| CC_max |  1 |  74.55689 | 6726 | 4784.513 |\n",
       "| PAR_max |  1 |  63.35217 | 6725 | 4721.161 |\n",
       "| FOUT_max |  1 |  17.40582 | 6724 | 4703.755 |\n",
       "\n"
      ],
      "text/plain": [
       "         Df Deviance  Resid. Df Resid. Dev\n",
       "NULL     NA        NA 6728      5568.263  \n",
       "TLOC      1 709.19340 6727      4859.070  \n",
       "CC_max    1  74.55689 6726      4784.513  \n",
       "PAR_max   1  63.35217 6725      4721.161  \n",
       "FOUT_max  1  17.40582 6724      4703.755  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m2 <- glm(post ~ TLOC + CC_max + PAR_max + FOUT_max, data = data, family=\"binomial\")\n",
    "anova(m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>m1</th><th scope=col>m2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TLOC</th><td> 0</td><td>82</td></tr>\n",
       "\t<tr><th scope=row>CC_max</th><td>76</td><td> 9</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td>17</td><td> 7</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 8</td><td> 2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & m1 & m2\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTLOC &  0 & 82\\\\\n",
       "\tCC\\_max & 76 &  9\\\\\n",
       "\tPAR\\_max & 17 &  7\\\\\n",
       "\tFOUT\\_max &  8 &  2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 2\n",
       "\n",
       "| <!--/--> | m1 &lt;dbl&gt; | m2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| TLOC |  0 | 82 |\n",
       "| CC_max | 76 |  9 |\n",
       "| PAR_max | 17 |  7 |\n",
       "| FOUT_max |  8 |  2 |\n",
       "\n"
      ],
      "text/plain": [
       "         m1 m2\n",
       "TLOC      0 82\n",
       "CC_max   76  9\n",
       "PAR_max  17  7\n",
       "FOUT_max  8  2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance <- data.frame(m1=c(0,anova(m1)$Deviance[-1]), m2=anova(m2)$Deviance[-1])\n",
    "importance <- data.frame(apply(importance, 2, function(x){x/sum(abs(x))}))\n",
    "rownames(importance) <- c(\"TLOC\",\"CC_max\",\"PAR_max\",\"FOUT_max\")\n",
    "round(importance,digit=2)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP2: REMOVE CORRELATED METRICS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THE RISKS OF NOT REMOVING CORRELATED METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NULL</th><td>NA</td><td>       NA</td><td>6728</td><td>5568.263</td></tr>\n",
       "\t<tr><th scope=row>CC_max</th><td> 1</td><td>600.97611</td><td>6727</td><td>4967.287</td></tr>\n",
       "\t<tr><th scope=row>CC_avg</th><td> 1</td><td> 18.67344</td><td>6726</td><td>4948.614</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td> 1</td><td>129.71197</td><td>6725</td><td>4818.902</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 1</td><td> 57.87581</td><td>6724</td><td>4761.026</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & Deviance & Resid. Df & Resid. Dev\\\\\n",
       "  & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNULL & NA &        NA & 6728 & 5568.263\\\\\n",
       "\tCC\\_max &  1 & 600.97611 & 6727 & 4967.287\\\\\n",
       "\tCC\\_avg &  1 &  18.67344 & 6726 & 4948.614\\\\\n",
       "\tPAR\\_max &  1 & 129.71197 & 6725 & 4818.902\\\\\n",
       "\tFOUT\\_max &  1 &  57.87581 & 6724 & 4761.026\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 5 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;int&gt; | Deviance &lt;dbl&gt; | Resid. Df &lt;int&gt; | Resid. Dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NULL | NA |        NA | 6728 | 5568.263 |\n",
       "| CC_max |  1 | 600.97611 | 6727 | 4967.287 |\n",
       "| CC_avg |  1 |  18.67344 | 6726 | 4948.614 |\n",
       "| PAR_max |  1 | 129.71197 | 6725 | 4818.902 |\n",
       "| FOUT_max |  1 |  57.87581 | 6724 | 4761.026 |\n",
       "\n"
      ],
      "text/plain": [
       "         Df Deviance  Resid. Df Resid. Dev\n",
       "NULL     NA        NA 6728      5568.263  \n",
       "CC_max    1 600.97611 6727      4967.287  \n",
       "CC_avg    1  18.67344 6726      4948.614  \n",
       "PAR_max   1 129.71197 6725      4818.902  \n",
       "FOUT_max  1  57.87581 6724      4761.026  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m1 <- glm(post ~ CC_max + + CC_avg + PAR_max + FOUT_max, data = data, family=\"binomial\")\n",
    "anova(m1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NULL</th><td>NA</td><td>       NA</td><td>6728</td><td>5568.263</td></tr>\n",
       "\t<tr><th scope=row>CC_avg</th><td> 1</td><td>467.41722</td><td>6727</td><td>5100.846</td></tr>\n",
       "\t<tr><th scope=row>CC_max</th><td> 1</td><td>152.23233</td><td>6726</td><td>4948.614</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td> 1</td><td>129.71197</td><td>6725</td><td>4818.902</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 1</td><td> 57.87581</td><td>6724</td><td>4761.026</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & Deviance & Resid. Df & Resid. Dev\\\\\n",
       "  & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNULL & NA &        NA & 6728 & 5568.263\\\\\n",
       "\tCC\\_avg &  1 & 467.41722 & 6727 & 5100.846\\\\\n",
       "\tCC\\_max &  1 & 152.23233 & 6726 & 4948.614\\\\\n",
       "\tPAR\\_max &  1 & 129.71197 & 6725 & 4818.902\\\\\n",
       "\tFOUT\\_max &  1 &  57.87581 & 6724 & 4761.026\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 5 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;int&gt; | Deviance &lt;dbl&gt; | Resid. Df &lt;int&gt; | Resid. Dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NULL | NA |        NA | 6728 | 5568.263 |\n",
       "| CC_avg |  1 | 467.41722 | 6727 | 5100.846 |\n",
       "| CC_max |  1 | 152.23233 | 6726 | 4948.614 |\n",
       "| PAR_max |  1 | 129.71197 | 6725 | 4818.902 |\n",
       "| FOUT_max |  1 |  57.87581 | 6724 | 4761.026 |\n",
       "\n"
      ],
      "text/plain": [
       "         Df Deviance  Resid. Df Resid. Dev\n",
       "NULL     NA        NA 6728      5568.263  \n",
       "CC_avg    1 467.41722 6727      5100.846  \n",
       "CC_max    1 152.23233 6726      4948.614  \n",
       "PAR_max   1 129.71197 6725      4818.902  \n",
       "FOUT_max  1  57.87581 6724      4761.026  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m2 <- glm(post ~ CC_avg + CC_max + PAR_max + FOUT_max, data = data, family=\"binomial\")\n",
    "anova(m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>m1</th><th scope=col>m2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CC_max</th><td>74</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>CC_avg</th><td> 2</td><td>58</td></tr>\n",
       "\t<tr><th scope=row>PAR_max</th><td>16</td><td>16</td></tr>\n",
       "\t<tr><th scope=row>FOUT_max</th><td> 7</td><td> 7</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & m1 & m2\\\\\n",
       "  & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCC\\_max & 74 & 19\\\\\n",
       "\tCC\\_avg &  2 & 58\\\\\n",
       "\tPAR\\_max & 16 & 16\\\\\n",
       "\tFOUT\\_max &  7 &  7\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 2\n",
       "\n",
       "| <!--/--> | m1 &lt;dbl&gt; | m2 &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| CC_max | 74 | 19 |\n",
       "| CC_avg |  2 | 58 |\n",
       "| PAR_max | 16 | 16 |\n",
       "| FOUT_max |  7 |  7 |\n",
       "\n"
      ],
      "text/plain": [
       "         m1 m2\n",
       "CC_max   74 19\n",
       "CC_avg    2 58\n",
       "PAR_max  16 16\n",
       "FOUT_max  7  7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance <- data.frame(m1=anova(m1)$Deviance[-1], m2=anova(m2)$Deviance[c(3,2,4,5)])\n",
    "importance <- data.frame(apply(importance, 2, function(x){x/sum(abs(x))}))\n",
    "rownames(importance) <- c(\"CC_max\",\"CC_avg\",\"PAR_max\",\"FOUT_max\")\n",
    "round(importance,digit=2)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SPEARMAN'S CORRELATION ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9cvX4tUCyGUFrYeXj6t23YIDg4OqltG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\ncnJyjy7Niru3RKJku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jrxDsvKFId2f7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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indep <- eclipse$indep\n",
    "plot(varclus(as.matrix(data[,indep]), similarity=\"spear\", trans=\"abs\"))\n",
    "abline(h=0.3, col=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoSpearman: AUTOMATICALLY REMOVE CORRELATED METRICS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9WBsUH95sY7vjV12frDJ87dir9xImrv4nEfuWTcmD+w/qj9d6QOCABmjRk7AHmw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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(Rnalytica)\n",
    "filterindep <- AutoSpearman(data, indep)\n",
    "plot(varclus(as.matrix(data[, filterindep]), similarity=\"spear\", trans=\"abs\"))\n",
    "abline(h=0.3, col=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP3: BUILD EXPLAINABLE MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "indep <- AutoSpearman(data, eclipse$indep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = data[, indep], y = data[, dep])\n",
       "\n",
       "\n",
       "C5.0 [Release 2.07 GPL Edition]  \tSat May 25 18:27:46 2019\n",
       "-------------------------------\n",
       "\n",
       "Class specified by attribute `outcome'\n",
       "\n",
       "Read 6729 cases (10 attributes) from undefined.data\n",
       "\n",
       "Decision tree:\n",
       "\n",
       "pre <= 1:\n",
       ":...NOM_avg <= 17.5: FALSE (4924/342)\n",
       ":   NOM_avg > 17.5:\n",
       ":   :...NBD_avg > 1.971831:\n",
       ":       :...ACD <= 2: TRUE (51/14)\n",
       ":       :   ACD > 2: FALSE (5)\n",
       ":       NBD_avg <= 1.971831:\n",
       ":       :...NBD_avg <= 1.029412: FALSE (64/2)\n",
       ":           NBD_avg > 1.029412:\n",
       ":           :...NOM_avg <= 64: FALSE (332/77)\n",
       ":               NOM_avg > 64: TRUE (21/7)\n",
       "pre > 1:\n",
       ":...pre > 6:\n",
       "    :...pre > 12: TRUE (77/13)\n",
       "    :   pre <= 12:\n",
       "    :   :...NSM_avg > 0.25: TRUE (56/17)\n",
       "    :       NSM_avg <= 0.25:\n",
       "    :       :...NOF_avg <= 10.5: FALSE (94/39)\n",
       "    :           NOF_avg > 10.5: TRUE (11/2)\n",
       "    pre <= 6:\n",
       "    :...NBD_avg <= 1.012195: FALSE (180/19)\n",
       "        NBD_avg > 1.012195:\n",
       "        :...NOM_avg > 23.5:\n",
       "            :...ACD <= 0:\n",
       "            :   :...NSF_avg <= 11: TRUE (73/18)\n",
       "            :   :   NSF_avg > 11: FALSE (12/4)\n",
       "            :   ACD > 0:\n",
       "            :   :...NOF_avg <= 12: FALSE (27/5)\n",
       "            :       NOF_avg > 12: TRUE (7/1)\n",
       "            NOM_avg <= 23.5:\n",
       "            :...PAR_avg <= 0.6774194:\n",
       "                :...PAR_avg <= 0.5789474:\n",
       "                :   :...NBD_avg <= 2.833333: FALSE (118/37)\n",
       "                :   :   NBD_avg > 2.833333: TRUE (7/1)\n",
       "                :   PAR_avg > 0.5789474:\n",
       "                :   :...NBD_avg <= 1.564103: TRUE (70/17)\n",
       "                :       NBD_avg > 1.564103: FALSE (29/7)\n",
       "                PAR_avg > 0.6774194:\n",
       "                :...pre > 2:\n",
       "                    :...PAR_avg <= 1.75: FALSE (288/89)\n",
       "                    :   PAR_avg > 1.75:\n",
       "                    :   :...NBD_avg <= 1.16129: FALSE (4)\n",
       "                    :       NBD_avg > 1.16129: TRUE (37/11)\n",
       "                    pre <= 2:\n",
       "                    :...NBD_avg <= 2.130435: FALSE (188/22)\n",
       "                        NBD_avg > 2.130435:\n",
       "                        :...NOF_avg <= 1.75: FALSE (29/8)\n",
       "                            NOF_avg > 1.75:\n",
       "                            :...NOM_avg <= 6.5: FALSE (10/3)\n",
       "                                NOM_avg > 6.5: TRUE (15/3)\n",
       "\n",
       "\n",
       "Evaluation on training data (6729 cases):\n",
       "\n",
       "\t    Decision Tree   \n",
       "\t  ----------------  \n",
       "\t  Size      Errors  \n",
       "\n",
       "\t    26  758(11.3%)   <<\n",
       "\n",
       "\n",
       "\t   (a)   (b)    <-classified as\n",
       "\t  ----  ----\n",
       "\t  5650   104    (a): class FALSE\n",
       "\t   654   321    (b): class TRUE\n",
       "\n",
       "\n",
       "\tAttribute usage:\n",
       "\n",
       "\t100.00%\tpre\n",
       "\t 93.79%\tNOM_avg\n",
       "\t 23.29%\tNBD_avg\n",
       "\t 11.81%\tPAR_avg\n",
       "\t  2.87%\tNOF_avg\n",
       "\t  2.60%\tACD\n",
       "\t  2.39%\tNSM_avg\n",
       "\t  1.26%\tNSF_avg\n",
       "\n",
       "\n",
       "Time: 0.0 secs\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.model <- C5.0(x = data[,indep], y = data[,dep])\n",
    "summary(tree.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RMZDEZERIS6ujrW4XAHBoNha2t76dKl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\nlzRAQnq83oJLQZ+Zux4aSuNwOLtPJezt60pDcDhcf7ndzP+lVqX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vR5epYig3N3fy5Mm5ubk3btyYP38+1uFwgYyMDF1d3by8vHv37pmb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Qx23WfFcne2kCRiApbbiWzYvMpjCZzKdPn40auMkAACAASURBVC5ZsgTvvBUVFZcv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\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5uEapN21PgHo/wc+lHXAAD6xBc8Vruq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\n19msjvb07bIDD7/w22kNms8bmfLHeG+1D0RK8GnH6NCo2HNjAJz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3/8+PGQkJCU\nlBTm2DabjRDSr18/5thcM0qWZVVVGVZbl8ioxtihoaFut5vHJ4LIqMYZ9fvvv1ut1vT0dObY\nVIWFhcyxuWYUAJvNNnXq1Jaf6I98zHWdOXAGQFRoUycdtaqZfcdvPGZa/q8Dc/O60MeqT/x3\nfGr/W8dmN336serEzisLugC4cP3PrZ5FNGrOnDnp6enGfRjb49zXoZps6fTnPXv25OTk5Ofn\nt3B8oOb6z5MnT46Pjzfuw8gKu6SkJD8/f8iQITywi4qKwsLCdu3axRy7uLi4sLBw9OjRPLBp\nojF01rFXrlxZWFho3IeRlbOmaVFRURERETywlyxZYjabBw0axAM7MTExJSWlhePbbL5w4cKc\nnBzjPoysnP1ZSqDN5lwzyuFwJCcnM8TWfw7qjDLuFcsKu7i42Gaz9enThwe2yCitUUYVFBQY\n94pliG2322NjY3lgc80ou90eHx/f6rP8FDV3N3xwTW4XAK8dOdP4+NOHnwMQ3X+tz+N7Xx+K\n5naeUEN7P7Z+pVUi71/3RgDTTj8UkPN1meE1Z3a1elibzfk5dyT2yZMnWTl3+mqfVQUR2G12\n5mp+VmHzc+5IbHqjAxPnTl/ts6ognQNba/6iSa7YAYmarzps2JDXXfnQ7hMme9b5EU2c7QYI\nAHeN7/fj7ko3AOnAvgY7++75eNUN11x94+0PbPg2Y15XR/nB59tGqbkr2+983qMXdbB50GHT\n262DDjt4nQW2wBbYgZqfPHkyGLGD1FlgG3+VJOnAgQNcsZmImt827a4zbs809JMHJm0rr+41\n+0mVAIBWd2rfvn0/7z9E/9fW5eLhLsvRHVc99NFe3eT0gc2XzNtKJDNiBqzXT+JtemC8fgQh\nJC8qhEjWgE4nzpkzJ61Xz2G9u4eoMhPnJRN70B/oV7H9+2XfPLE/K3NdkydPju/edUphKnNs\n+jXHOQP78cCml4BcXJTBHJt+FTtq5GAe2PRrDobOOvbKlSsHFOSkxIYxd9bo1xzhLh7YS5Ys\nMZvVuAgHD+zExMTknok8sBcuXNi3T0bfHl2YO9OvYntnpvLA5ppRDoejZ1JCMGbU4EH9gy6j\nbDZbdlaayCj9Z64Zld+/T3q3cB7Ydrs9NiaKU0aZTEqP6FAeGcX2q1hqDqBrv3Pn3XrzhecN\nJIQ44sb+WFFL/7fsl8UAVEeufvzvWx+PUWUAmYNHXXzZpRPHFIUoEiHyBQ98hivu+Dc9qOJY\nsUJI3LnXb/j0i7dfejzXZQYgWZICIrvyiuk2RQLQJSOfibO77jT9Yc+ePX37ZDpNLM11TTp/\ntFkmPLBLSkrOGdSfbU10DRnsuQuPOXZxcfGAgtwwVeaBnZGRwgn7+RWLQ5j2EN1Z07SoyHCT\nxKWTPPTAQolwKYimaYkJ3ax8+vZtt1zt4DPYS0tL09OS7XzMuWaUw2FjW21dXDOqf78+oXyi\nlWtG2WxWtp1El8goH/OHHljI9sPXiG2321Q+2LfdcjXh0448JnbuutMv3311blKsRVHContM\nuvKO705V6//beGKnaVrF79vvnDM5L7OnUzXH9kgfMeGyt/51UNO0+utDt17fG8AXJ6vor0d2\n3gxAsjZ7sWSTmpwXCSBxxMU1hosAmTjv2bOnV6wVQOFtq5mbD093AUi/8FbmziUlJVZV4oSd\nl+gAkDXrLubOxcXF2d3tnLB7drFwcr5zYgKA8NR+zJ01TQuzK5ywrx8VB8DZLZUHdleXygn7\n8sHRAKJzRjJ3Li0t7RHFq5NwzSgbt8HONaMy42zBmFEWE69qi4zyEc2oyIyBPLD5fUTSjHIl\n9eGRUcwndgxVv+LlijX7JCV0YIgKoK760MML1gKA/BsC0a5dJ4lkGdBVevuN+uWYn37hByLb\nft34/14H3LWlry55HgDkwwE519XVHTtaRSTznGy5BfM3Hn6hDdg//VROiHpej5ac24btdrtr\najRCTDywDx6sADAxhQv2b79VEsnKA/v48WoALTu3DfuTTw4T2Z4QQnh0vzMVdSASj4JsKTkC\nkGgbF+yyslpO3W/btlIiWTMiuQz2E6XVhKg8sLlmFL/BzjWjjvxeRSRL0GVUba2bUycRGeWj\nLSVHiGzv6eKCXVOjgcicMgog3Z1csAGcOXOGyc4T7DVjxowZM2ZcdOE4AijmhBkzZlxwXlFi\npAWA1aTKpqiA5okyAZFt/ryupIQG5Lxq1So/1y83K6ZAsSW/l9UOFNv/Vd3bhu2nAsW+5557\n+FWbtO7aRmyFQFKcrfsG7uz/8uhtKIhCQG9x4oHtZ7XbgC0TENmvtdcDxX777bc5Y/PKKP+o\nz7qM4hqtfqoNGeWns8io9hckSLFlbtHKducJ5mrmTcq2/pOuuys5XJKdDOwMIpK5Z/64GbGO\nQJ2/++47f8xTCv+wKDns7MHevHlzMGKvW7cuGLH5OdfWtr7o+VlYkCDFPnr0aDBii4zqSGyR\nUT4K0sEepNhHjx612+2RkZGvtah169bV1tYG5MxECp0vf77goju/PjL26TeuTwq1hMX1ye7l\nNEnnRz5tcjS98ntzClOkk5rjytnTp0yZQh9pxtkWqLMsyxaJVMthGze8qj/IyjxElspguffu\nv/bv35+tsyRJEgA5ZON7bzLHtsvkdJ32+OOPp6WlMce2SaRKiX5v/UvMsc0SqXI3+EONlXOc\nKh/SXIX5aYsWLWLrLMuyTOAm6vsb1zPHjlHlwzVaZkb6I488whzbREitZH//vfrdV1mZR5mk\nY1ro2DGD5s+fzxzbLJEaKWTju/WLS7Ey55pREqDJDh7V5ppRVolUyZEbN6xmjs01oyQAimvj\nu/XfhYmM4pdRv2uhRef0WbhwIXNsCdAky/sb32GOHWWSjtSgf/+8++67jzk22O08wV6aptWc\n3tHNrEiyc39l/dSy9LtHAcQMeCmgeeLIrnYA46+/i/7K0HnPnj2JDhOAB78+wtw8P8oK4PLH\nX2XuXFJSQu9l44Gd4TIDuGbVe8ydi4uLU0NVTtjd7Lza8er0MACZQ8cyd9Y0jd7LxgP7kuRQ\nAIl98nlgR1lkTtiTEpwABky5lLlzaWlpnE3hhM01oyzcBjvXjEoO4TXYuWYUv2gVGeUjmlF9\nx0zmgc2vHWlG9Ro4nLnzWX7zBF56YfmFuZEAug5ZqT+6f9PKAREWAH/999GA7C6dPpgAkuz8\n8x2L2Trv2bMnO7M7AWQ1hrn5hNE5BJCUMObOJSUlNpuZE/Y5BakAJFMEc+fi4uL83CSJcMFO\nT+kKPgVZvniWBBDJwtxZ07TIcCendrxn4QUACFF5YMd3i+CEfePV5xGASDbmzqWlpb2SYzhh\nc80oO7fBzjWj8vokSkGYUVaryqnaIqN8dM/CC/hh8/uIvPHq8wAQycwjo87qiR2VObTPZ8cq\nNE07/duLidGeayR7X7Dgiv4t7XvYWHPmzEmKiwpV6i+Zba9zXQX9ly5Q3LtnXJxZYWbu1eTJ\nk2MjQyNNMnPskpISu92eFBfFA5su/hnFAZsuUDwwO5kHNl38k6WzF3vlypV9UrpbJcLcWdO0\nqKgol8PGA3vJkiWqSVH5YCcmJsbHRPDAXos+0iEAABq+SURBVLhwYVpirF1mj00XKE7pHs0D\nm2tGORyO+JjwoMuo/Pz8vPSEoMsom83WKyFGZJT+I9eMykruZuODbbfbI11OThmlyLKFAzaX\niV1dxdr7ru2fHGczmcJjeky+8m/Gdexa1bEdy2yylLPoG41O7AhRhsx7rsatlX775uQh3Ykk\nEcN9JAGBzZlzZbcuLr2O7Xd+dKqn3HRil9w9yulNZIbYkydPinQ5bDJ7bDqx69bFxQObhqZd\nZuasY9OJXd/UbjywaWgydNaxV658ITW+i0LYt6OmaVFRUU6bmQf2kiWLTYqsT5DYYicmJsZG\nhvLAXrjwth5dI1QOg51O7OJjwnlgc80oh8MRExESdBmVn5+f0SMm6DLKZrMlxrLsJLpERvmY\nL1myOKV7FxMfbLvdHua0ccooWZZ4VJvHxO7RqckArNEp46ZNzU/rCsDetX7niZZVc/rbgS4z\nAM/EbuL5Y/qmuACMf/LDHIcKwBnVLTOrd3paRv7AokuvWxQQ2R9H9gRACOldUNR+59+/WqCX\ne8+ePRmJYWzN9Z9H53fjhF1SUmIzK5ywCzK6cMIuLi7OSY3khJ3SLZQT9uKrPcvcM3fWNC08\nxMwJ+7ZL+vLD7hZl54R99aQMTtilpaXJcSGcsLlmlM3Ca7Bzzag+yRHBmFFWsywyqhNkFPOP\nyI7JKLYTO2oeln7Jgao6+sjKuTkAsm7Y5Meza+8oiqXv1DOx0zRNc1c9d6lnB5VbXv1SP7Z4\nYEpERERAcNl2E4Bhs2/z/N4+55cHxhpDs4tJAnDL+t2szPWfU6wKgKl3Pcscu6SkhP6VwwO7\nm1kGcMnDrzPHLi4u7qrKnLAjWLejjj0m3AIgpXAkc2dN0+i3Jzywi0LNAOIyvHvFMMUOUXhV\nu8CpAug71rtiOzvn0tLScG6dhGtG8RvsXDMqhttg55pR/KotMkprKqPSi8bzwGbejj4Z1SNv\nCHNn5hM7av7wvlP6I+6aY8lWRXXmtXrKbvN95xKi3PDscDSY2GlaxbF1AByxc41Hv5URaay+\nP7JJRFJcS5cu1R9pj3M/p6pYPTu40dCUGq5P2E5z/WeLRIjsePfdd5ljl5SUEEBSXDywVUIA\nlJSUMMcuLi5WCZHNcTyw6Ylxhs46tkuRJFOXiRMnMnfWvCvE8sAOkSUQedCgQTywZQJJDuGB\n7ZCJZIq46qqrmDuXlpYqBJISxgOba0YRMK62/jPXjDIRIqsxPLC5ZhQBJCWcB7bIKK1RRkmm\nqAsvvJAHNr0Hiwe2QyaANGbMGObOzCd21NxnDvdCViSAdccqmn6OpmmaduSrh80SGbjww2O7\npusTO8/limbXcABqyCC0T2fcmqzYtm/fru+zoWlVAEBMxp03tpVVt2rlrjn8VVm1o+tY+mtd\nXV2NpslEMvo0NrdYLG4/OKm5/mulW5NM5k2bNpWVlbHFdrvdGkBAeGBXaxqAjz/+eP/+/cyx\nqzVNBpdq12oagJad24Z9otatWEN+/fVXHt3PzboddfNTdW4Q5ejRozyw6zRITEeNbl5ep8mK\n48cff+SBXcsNm2tGaQAhXAY714yq4TbYuWYUv2qLjGqcUYoaumPHjqAYNcaMApEOHz7MAxvs\nthTTzX2OSzunC3YeXX+8ckK4pckn1pRvP2/YgpCc+R/fM+L098/V/4c+7wMQkbbaOBNswxk7\nALIa1/J7aPzSTar61FYArqQl9Ff/t+tZlBDqp7kRWzJF8sD2f0uxtmH7r4Cw/d+u56zCBqBY\nU3g4+7/vjf8FMWITYgpGbMWSyAP77bffbtWwPdgioyAy6n+EDZFRHYsN4u8+rQE5s91SzMdc\n1/b7+gEY/81vTT/NXX3rgGiTPevLk1WapjVxxo6tZs6cOX78eP3XadOmOePmP/f3+tOB226c\nff8vp1o28RlbBQUFAGQ1es3LDWLIx9xisVQvuLxVwiYH7sKFC/v27csWOy8vD4CkhK9d/RQn\n7Pvuuy85OZktdnZ2NgDFnLD6pcWcsF977bUWnNuGTdW/f/+bb76ZrfOwYcMAEMny6toXW8Bu\nT0F69ep1991388CWFNfa1c9wwh41atTs2bPZYg8ePBiAZIpc+8oyTtj8MoprtflllKzGrXn5\n75yw+WUU104iMspH/DKKyPZX17zACbtv3776hhmssGlGRUZGLlu2rMnjqSwWC32DAZn7/m9t\n0///6aKRD209dvem7f1D1EbP8QrsztgZr19ps3Nd1UEAzrjr6K979uwBoFh6Nn7FNpsbTSRT\npPH6FVbYJSUltCacsNHw+hVW2MXFxQBM9mx+2AyddWwAijXFeP0KK2fqQ2QHJ2xCTMZr7Nhi\n+1w+xRBbsSQar7Fj5VxaWgpANnfnhM01o2Q1miG20YRrRplsaZywuWaUYk7gh83QWWRUy9g+\nl6EzxAaRjdfYsXJme41d45pQbbk6A8Cl3x9v/JRjOx8yETJw4Yf1jxjO2NWv0nm2SVJj+zrU\nytKN/Mz5OQvsxub8nH2wtQC/SfHfmZX4mQvsjjTv+FFDr+xh4tw5Bjtbc37OQdr9BHYHOBvN\nfT669pUcBTC2qQvsjn69rkbTNt83kngVkb4GwLZFuYQQEhMTQ487fPiwpIR2ibTqz6w8+vuJ\nWndAH5OEEEByOu3Gr5/b7LxqQOylWw/Tw3744YeUlBQQOSY6ynhMe8xnlRwyYoeFhZrNZrbY\nW7duHTBgACDFxHThg42IiAiTqf7qBybY69evHz9+PIgSE93gsh6G2HrHY4hNCAFRTAqJiIhg\n6+zFJjEx0VywAVVVw8PD+WCz7H4Nqy3brOaQkBC2zidOnAgLCwPkmBhmg71htXlmFMfBLoWE\nOGw2G1tsT0bxHOwcM4ppJ+mojJLNqhIWFsbWGcGdURyxzWYz82qfOHGiW7duERERP//8c8vP\n8lPU/IVD5ZfGeHPJXZkbGvofrVd52Q6V+B7/2+f33frsbuMj1Sc/W/OPnyL6ThjfJ9z36MYK\n6IxiQO+kVbffSm7QD6Nfc7A17wBs+jVH0GHTrzmCDpufs8DuSGz6VWzQYYuM6khskVFapxjs\nQYrNfLkTah4z+JbTdW76yMf3jgHQe/5n9Fd37cmffvpp388Hm3NocPPEww8/HNCbb1mFhYV7\n9+4tLCykO8m0U10Kli6ZWH9/XFxcnCzL1113Xfudqbn+c3Z29i+//DJ16tS0tDQmzkZsVVVd\nLteCBQva74yG2D179ty7d+/8+fMTEhKYOBuxExMTzWbz3Llz2++MhtgxMTGHDx9m1fGM2NOn\nT3/jjTdiYmKuv/56ts4AHA6HpmnGa4dZmf/hD3/YsGFD9+7dr732WrbOAMLCwhRFue2229rv\n7GM+cuTIXbt2JSQkTJkyha0zgLi4uDNnztx+++3td/YxLyws3Lp168CBAydPnszWGYDZbHY6\nncYrtdtprv+cnZ29c+fOqVOnDhgwgImzETs+Pl5RlHnz5rXfGR2YUaqqRkZG3nTTTe13Rgdm\n1JYtWxwOx+WXt34rQEDOCNqMUlXVYrEsWrSo/c5olFEff/xxamrqnDlz2DozFzW/6R8PpRRs\nmzyi75EdX7z23hZH3Nh1DwykB5w+9EyPHjerjtyqsq9bt2M136SaM2dOenq6z4XJ7ZG77jT9\nge4Vm5+fz8rZqMmTJ8fHxxcXF7My1LHpXrHp6emsnI2is2efC5PbIx2b7hU7evRoVs5G0X0Y\nGRrq2CtXrrRarf3792furGlaVFRUoBux+Gm+ZMkSs9nsc2EyE2dN0xITE5OTA9sB3U/zhQsX\n5uTk+Nw8wcSZxz6MuvmcOXNkWZ43bx5zZ7pXLNtq65o8ebIkSStWrGBlaMyo/Pz8IUOGtHx8\n28Q1o2w2W58+fVg5G8U1owoLC31unmDirAVtRtnt9tjY2BYObrP5woULTSaTz80TTJw5ZdTL\nd1+dmxRrUZSw6B6Trrzju1PV+v+W/bIYgOrIbe7pAdw8se9rf9eYoTp16lRNjT/rCPrrTKT6\nC0qqqqqqqv26djhQ7PLy8tpa96FDh1g5G7HdbjfbmuiqqKjw88g2YFdWVlZU1jI011Vd3fpq\nkAE569i//fabn2ff21CQuro6t9sv90DNjx49yg+bEFJWdpqHeWlpaXU1r8Hudrtra7mYnzrV\nyroGbXYGHexMsXWVl5f7eWTbBntlFZdo5ZpRmqax7YG6uGZUFdNSd4KMcrvddXVcsEtLS/lh\nMxeRbDP++uTXew9W1NQcP/zjW88s6uWsvzLV0e0mTdNaOF0XnrZa07Rv7sgB0PQ6dkd3l6xZ\ns2bN6tVbdh/VAvmieu3atbIaZ1zV3Udlh/Z8/vnnWzZ/8d2vpwJyrqur+/bbb2U1uoWFnssO\n7dlaUrLj6y0lAWK///77APnqq69CQ0PZYrvd7oqKih/3HeGB/eWXX6Lhqu4Msbdt26aYE3hg\n00uRWnZuG/att95KiHr8+HEe3e/48eNEsvAoyAMPPECIybjzBEPsn376ich2HtjLly9XLIlx\ncT/ywK6oqKisPskDe+3atYD0ww8/8MA+c+bMLwea7X7tweaaUTt27JDVuKDLqIqKiu9/OBR0\nGaVYU2RZExmlY1dUVFRWS5wyCkQ27jzBEBvsdp5gL+OpvPJfd76wZOGovB71WJEpAZ1LhN+r\nuquhvku7tSz/V3VvGzbQ6LYTFtj+r+reVmy/FCi2/6u6n1XYAAjxa82CQJ39Xx69bQXxc1X3\nsw3bz50nAsX2f+eJtmEDfi3zJDKKEbZf+r+TUX7uPHG2DfYgzSg/d55oQ0Yx3HmCuRQA1Sf3\nvfPq2tVr1qz7585aT8+LGT3ljxdddNHkUf39L7oufVX32jNHvt78xedffPHVt/vrPNM+V58B\ng4YNH37nwvkBeTbeeaJJ85xBQ+bftHDKmII2YM+dO3fEiBFssRvvPMEcW1/VnSF2450nmGPr\nq7ozxKbq2bPn/fffz9a58aruzAuir+rOHtuwqjtzbH3nCYbYdFV3IjtfXfMcJ+wxY8bQq9eZ\nZ5Rx54kgyijjzhNBlFHGnSeCJaMIkVNSUu699162ziKjmpO+8wTzjGK18wR7zRo/0CF7/n6V\nzVEAovMfPVZd17Z5IgBZjVv84J0bVz/B1lnfeaK28nfm5gAA8vTyh5k76ztPcMPG5k3vMXfW\nd57gh83JmRC1X78c5s4ec9nBDds0sDCfE7akuDhhK5bEuVfOYu5MlzuRTJHcup90zZ+u4JRR\nshodjBllsqUFY0Yp5gSRUQ3MRUY1dAaRR48aziOjmN88wVDKquLNksmVN3jw0KHDBg/qO/Hc\n4RpqdnzxGbwaOnQoApG79sjfFi6qqNMAsHUGUFd9INIZc6LGzcFcu37ejWf4YLtrj3DDxqhh\nY8truTjXVf7AD5uTs6bVbPt62+iL5vFg1twV3LBrvyz51+gtX/LAdteVccKuqz70wnMvPr1i\nFQ9srfYEt+7nfvbp559czmew1xwPxoyqrdwXlBlVc0hkVANzkVG+3O6PPvhk4/sf88A+a9X6\nRRtaIBcc0IWe/VRAzt5V3bmY88P27jzBxZwftndVdy7m/LD5OXM1F9g+8u48wcVcZJSPREY1\nVjCOGq7mAttHzHeeYCvlD1OmWeX6N//qq6/aYodOGBLdwnNalepwZWRmxXePZeDsroTUYKM0\ne1T3lKRENuYNZXaGp2dkBh22NSy6V2pq0GGzdG6ILSlqdl5BEBSkoblsMmfl5gcftmrJyukf\ndNgmqyMzOycIsBtKZJTIqJaduWKLjGqMzVLuylcfvGXp82/99+ffLRHdhk645L6lfzWueBKA\nJCVsxNSrnv/HpjLvXmnxY9p+EwcAQiSGzo9O9az2Sa9fYWtuxGZbEB2bXr8SdNj0+hUiycGF\nzc/Za06CEpsEGbZnS7Fgww7yjAqywe7JqGCrtsioprGDbbDzuMbu0anJAKzRKeOmTc1P6wrA\n3nXsjxW1bbBCSqRn+qmGJEyZc1v7x4Nsjrtk6oQ75l3Ufuffv1qAhvswKpaeu7esZ2VuxAbI\nvbf9mTm2fvMEN2y8/uzDzLH1myf4YTN01rEBEKJmpacyd9a8FyZzwzblZmdwwpYUFydsEHnI\ngDzmzvrNE7ywIV08hVdGyWp0MGaUyZYWjBmlmBNERhmxRUZ1WEaxndhR87D0Sw5Uee7nWDk3\nB0DWDZva4AatruLLd1+ef/F50RbPYsWyGj32kutXb/y6yh24HQBInu162u388sBYNJrYef6P\nhXlDbOLZrocptj6x44bt3a6HKbY+seOHzdDZJzQ9W4oxdda8ockN2+TZrocDtqS4OGGDyJ7t\nepg66xM7Xtg8M0pWoxli6z/zziiTLY0bNseMUswJ/LAZOouMahk7GDOK7cSOmj+875T+iLvm\nWLJVUZ15bThlV1/c2srfN65+fOa4ATbvLcHW6PSZ198bmJ0xNNvt3M+pKtYk+rM3NH13lGuP\neUNs4rMPIxNs34kde2zffRiZYPtO7DhgM3TWsRuEJlNnzSc02WObfPZhZIhdH5qssetDk6mz\n78SOOTbfjIr2OSYoMqp+Yscem2NG1U/sOGAzdBYZ1TJ2MGYU24kdNfeZw72QFQlg3bGKQN2I\n1uhOkOrSvW+vWf3KK68Ub9lND4LfyszM3PXdd6NHjaKLf/qo9vRvX37++abPN32z+1Crzu6a\nw7Ia6+g6r+zXxwF8//33WVlZqjnkheeXN3k8Nd/8xef/+v6gn+b6MT179vzpp31z5lxJF/9k\niL1ly5aioiKz1fX8s08yx46Lizt48KC++CdD7HfeeWfOnDluOJ947F7m2BEREcePH9cX/2SI\nPWLEiM82fZEQ340uUMzQua6uzm63a5BffmllC9gBFUQ3HzRo0NYvv0ru2YMu/skW22q1yor1\nxVXPMsfu16/ftn9vz87qTRf/ZIh97Nix2NhYxWRftfIZ5tgioxpn1KRJk2rdtuVPPsgcm2tG\nTZ482eaIevaZR5lji4wSGdWy87FjxxISEqxWa6sLFPuzpZiPua6SqzMLl387d0/pU8mulh18\n1cRkz6sTP321/J7rWzigsf7yl7/4uS3a/MsuaNmq+tRWAK6kJfTX4uJih8Phj/M/Xl7WKjY1\n13+dP3++JPm1y1Cg2G+++aafG4+0AZuu+M8D+5VXXhkyZAgn7OnTp3PCXrFihdVq5eH8wQcf\nqKpfm5X5XxDd/JFHHjGbzZywLRa/7uFqA/b9999vMvl1x1ag2O+//z7zUaObi4xqnFF0GX0e\n2Fwzymbza0d2kVFtLojIqOac/c8of7YU8zHXtf2+fgDGf/Nbqw4+UloACk3Mu+ovef6g67rz\nzjsLCgoqKytbPoxOY1s+xmfyPGbMmNWrV1dWVv789m03r9mbe/+KBT2b2AzbzwkyNQ/rubR0\n740Ali5dOmLECD/NA8I+//zz16xZwwn7qaee+sMf/sAD+8ILL3Q6nfPmzeOB/dJLL02fPp0H\n9mWXXRYdHc3DediwYW+88QbbdtQ1b968pKQkTtivvfYaJ+ybb745MzOTB/bw4cOZjxpdekYx\nxw7ejDKZTNdee20wZhSnaouMEhnVsrP/GeXPlmItfyuq1Qbwran3OUzVs2fPvFHrmFjVVR0E\n4Iy7zsd8y9UZAC79/jhDcx27/eYC+3+ILQoisAW2wD6bsUVBOg02FQ/s9pt7zu1vPDcnMTEx\n4FlhI+3du3ff/nLjI212ltTYvg61snSjPlml5vtKjgIYG25haK5j6+YCOxixRUEEtsAW2Gcz\ntihIp8GmMmK3WezN6fzurYxIsDh7ByAibbXxkfY4ryyIAfDCoXKD+aoch2qyZ9H7k1mZe7Dr\nKnRzgR2M2KIgAltgC+yzGVsUpNNga5pmdG6n2Jqf1RO730puABAz+JbTdZ6lokOi+gDoPf8z\ntuYU++N7x+jmAjsYsUVBBLbAFthnM7YoSKfB1jTN6NxOsTU/qyd2mqYtmdgDQNd+58679WYA\nAHHE1W+ywcocgGpPJaTeXGAHI7YoiMAW2AL7bMYWBek02BeeN9Do3H4xND/bJ3buutMv3311\nblKsRVEAqK4LvjtVzdwcAFGiJl15h24usIMRWxREYAtsgX02Y4uCdBrssOgeRuf2i6H52T6x\n6xhzgd2R5sHozNVcYHekucDuSHOB3WHOXM0FdnDJrxUvhYSEhISEhISEzn6RoqIiAEe/2vzf\n0zX0Zx99+umnAdgRYrKlD+zfRX+ElTNXc4HdkebB6MzVXGB3pLnA7khzgd1hzlzNBXZwibR6\nhBbIXrGEtG7YNmeu5gK7I82D0ZmrucDuSHOB3ZHmArvDnLmaC+zgkrJr1y6GdmzdOsxcYHek\neTA6czUX2B1pLrA70lxgd5gzV3OBHVwinWmWKiQkJCQkJCT0f1ni5gkhISEhISEhoU4iMbET\nEhISEhISEuokEhM7ISEhISEhIaFOIjGxExISEhISEhLqJBITOyEhISEhISGhTiIxsRMSEhIS\nEhIS6iQSEzshISEhISEhoU4iMbETEhISEhISEuokEhM7ISEhISEhIaFOIjGxExISEhISEhLq\nJBITOyEhISEhISGhTiIxsRMSEhISEhIS6iQSEzshISEhISEhoU4iMbETEhISEhISEuokEhM7\nISEhISEhIaFOIjGxExISEhISEhLqJBITOyEhISEhISGhTiIxsRMSEhISEhIS6iQSEzshISEh\nISEhoU4iMbETEhISEhISEuokEhM7ISEhISEhIaFOIjGxExISEhISEhLqJBITOyEhISEhISGh\nTiIxsRMSEhISEhIS6iQSEzshISEhISEhoU4iMbETEhISEhISEuokEhM7ISEhISEhIaFOIjGx\nExISEhISEhLqJBITOyEhISEhISGhTiIxsRMSEhISEhIS6iQSEzshISEhISEhoU4iMbETEhIS\nEhISEuokEhM7ISEhISEhIaFOIjGxExISEhISEhLqJBITOyEhISEhISGhTiIxsRMSEhISEhIS\n6iQSEzshISEhISEhoU4iMbETEhISEhISEuokEhM7ISEhISEhIaFOIjGxExISEhISEhLqJBIT\nOyEhISEhISGhTiIxsRMSEhISEhIS6iQSEzshISEhISEhoU4iMbETEhISEhISEuokEhM7ISEh\nISEhIaFOIjGxExISEhISEhLqJPr/rZO2h37nIc0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(tree.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a Rule-Based Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rule.model <- C5.0(x = data[, indep], y = data[,dep], rules = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print a model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "C5.0.default(x = data[, indep], y = data[, dep], rules = TRUE)\n",
       "\n",
       "\n",
       "C5.0 [Release 2.07 GPL Edition]  \tSat May 25 18:27:48 2019\n",
       "-------------------------------\n",
       "\n",
       "Class specified by attribute `outcome'\n",
       "\n",
       "Read 6729 cases (10 attributes) from undefined.data\n",
       "\n",
       "Rules:\n",
       "\n",
       "Rule 1: (2910/133, lift 1.1)\n",
       "\tpre <= 6\n",
       "\tNBD_avg <= 1.16129\n",
       "\t->  class FALSE  [0.954]\n",
       "\n",
       "Rule 2: (3680/217, lift 1.1)\n",
       "\tpre <= 2\n",
       "\tNOM_avg <= 6.5\n",
       "\t->  class FALSE  [0.941]\n",
       "\n",
       "Rule 3: (4676/316, lift 1.1)\n",
       "\tpre <= 1\n",
       "\tNBD_avg <= 1.971831\n",
       "\tNOM_avg <= 64\n",
       "\t->  class FALSE  [0.932]\n",
       "\n",
       "Rule 4: (4924/342, lift 1.1)\n",
       "\tpre <= 1\n",
       "\tNOM_avg <= 17.5\n",
       "\t->  class FALSE  [0.930]\n",
       "\n",
       "Rule 5: (5423/547, lift 1.1)\n",
       "\tpre <= 6\n",
       "\tNOM_avg <= 23.5\n",
       "\tPAR_avg <= 1.75\n",
       "\t->  class FALSE  [0.899]\n",
       "\n",
       "Rule 6: (5189/552, lift 1.0)\n",
       "\tpre <= 12\n",
       "\tNOF_avg <= 10.5\n",
       "\tNSM_avg <= 0.25\n",
       "\t->  class FALSE  [0.893]\n",
       "\n",
       "Rule 7: (77/13, lift 5.7)\n",
       "\tpre > 12\n",
       "\t->  class TRUE  [0.823]\n",
       "\n",
       "Rule 8: (54/11, lift 5.4)\n",
       "\tNBD_avg > 1.029412\n",
       "\tNOM_avg > 64\n",
       "\t->  class TRUE  [0.786]\n",
       "\n",
       "Rule 9: (11/2, lift 5.3)\n",
       "\tpre > 6\n",
       "\tpre <= 12\n",
       "\tNOF_avg > 10.5\n",
       "\tNSM_avg <= 0.25\n",
       "\t->  class TRUE  [0.769]\n",
       "\n",
       "Rule 10: (96/25, lift 5.1)\n",
       "\tpre > 6\n",
       "\tNSM_avg > 0.25\n",
       "\t->  class TRUE  [0.735]\n",
       "\n",
       "Rule 11: (51/14, lift 4.9)\n",
       "\tpre <= 1\n",
       "\tACD <= 2\n",
       "\tNBD_avg > 1.971831\n",
       "\tNOM_avg > 17.5\n",
       "\t->  class TRUE  [0.717]\n",
       "\n",
       "Rule 12: (41/12, lift 4.8)\n",
       "\tpre > 2\n",
       "\tpre <= 6\n",
       "\tNBD_avg > 1.16129\n",
       "\tPAR_avg > 1.75\n",
       "\t->  class TRUE  [0.698]\n",
       "\n",
       "Rule 13: (56/19, lift 4.5)\n",
       "\tpre <= 1\n",
       "\tNBD_avg > 1.971831\n",
       "\tNOM_avg > 17.5\n",
       "\t->  class TRUE  [0.655]\n",
       "\n",
       "Rule 14: (199/70, lift 4.5)\n",
       "\tpre > 1\n",
       "\tNBD_avg > 1.012195\n",
       "\tNOM_avg > 23.5\n",
       "\t->  class TRUE  [0.647]\n",
       "\n",
       "Rule 15: (45/16, lift 4.4)\n",
       "\tpre > 2\n",
       "\tpre <= 6\n",
       "\tNBD_avg > 1.012195\n",
       "\tPAR_avg > 1.75\n",
       "\t->  class TRUE  [0.638]\n",
       "\n",
       "Default class: FALSE\n",
       "\n",
       "\n",
       "Evaluation on training data (6729 cases):\n",
       "\n",
       "\t        Rules     \n",
       "\t  ----------------\n",
       "\t    No      Errors\n",
       "\n",
       "\t    15  825(12.3%)   <<\n",
       "\n",
       "\n",
       "\t   (a)   (b)    <-classified as\n",
       "\t  ----  ----\n",
       "\t  5672    82    (a): class FALSE\n",
       "\t   743   232    (b): class TRUE\n",
       "\n",
       "\n",
       "\tAttribute usage:\n",
       "\n",
       "\t 99.75%\tpre\n",
       "\t 96.34%\tNOM_avg\n",
       "\t 81.26%\tPAR_avg\n",
       "\t 78.70%\tNSM_avg\n",
       "\t 77.53%\tNBD_avg\n",
       "\t 77.28%\tNOF_avg\n",
       "\t  0.76%\tACD\n",
       "\n",
       "\n",
       "Time: 0.1 secs\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(rule.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build a Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "rf.model <- randomForest(f, data = data, importance = TRUE, keep.forest=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print a Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      " randomForest(formula = f, data = data, importance = TRUE, keep.forest = TRUE) \n",
      "               Type of random forest: classification\n",
      "                     Number of trees: 500\n",
      "No. of variables tried at each split: 3\n",
      "\n",
      "        OOB estimate of  error rate: 12.38%\n",
      "Confusion matrix:\n",
      "      FALSE TRUE class.error\n",
      "FALSE  5565  189  0.03284672\n",
      "TRUE    644  331  0.66051282\n"
     ]
    }
   ],
   "source": [
    "print(rf.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print a variable importance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D1l27Ho5oFDdkTu8XEkfAMAgCwhNDydT5+9ap+nT3t2lVv2DmnZ+2UcCQAAaBFn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z0VRVEUZdCJYCPDQk5uGt2/femiBW0tzZzyF61et8ln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\nr+kBr2SNInJ03XctvCoXdLa1sMtTtnK9KT/tSr6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      "text/plain": [
       "Plot with title “rf.model”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varImpPlot(rf.model, type=\"1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FAST-AND-FRUGAL TREE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "post is now logical.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FFT 1 (of 7) predicts post using 3 cues: {pre, NBD_avg, NOM_avg}\n",
      "\n",
      "[1] If pre <= 0, decide False.\n",
      "[2] If NBD_avg > 1.3, decide True.\n",
      "[3] If NOM_avg <= 7.4, decide False, otherwise, decide True.\n",
      "\n",
      "                     train test\n",
      "cases       .n    6729.000   --\n",
      "hits        .hi    636.000   --\n",
      "misses      .mi    339.000   --\n",
      "false al    .fa   1368.000   --\n",
      "corr rej    .cr   4386.000   --\n",
      "speed       .mcu     1.532   --\n",
      "frugality   .pci     0.847   --\n",
      "cost        .cost    0.254   --\n",
      "accuracy    .acc     0.746   --\n",
      "balanced    .bacc    0.707   --\n",
      "sensitivity .sens    0.652   --\n",
      "specificity .spec    0.762   --\n",
      "\n",
      "pars: algorithm = 'ifan', goal = 'wacc', goal.chase = 'wacc', sens.w = 0.5, max.levels = 4"
     ]
    }
   ],
   "source": [
    "f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "fft.model <- FFTrees(formula = f, data = data)\n",
    "print(fft.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize a FAST-AND-FRUGAL TREE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SwAAABAJYEZOwCQYgEBARcuXIiJibG0tOzTp4+ioqKkIwIAkCTM2AGAtFqzZk2d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\n+XdJgXt67v1oOemuQ3FfvPd26dIMASOn1vzhu4sNDErnY7Kq5u6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mMgqWefZNzDZSMWp7QXzYRp1nN+uF+PDzyRZcXnU+BttKAYmdFLjQ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OnGkux1Q0Wrq2x76lAzsq+yzqYqkZ//rW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\n5Dfxf0zzvCA5lQt2AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(fft.model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP4: Explore different parameter settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results <- list()\n",
    "# for(i in seq(1,10)){\n",
    "#   set.seed(i)\n",
    "#   indices <- sample(nrow(data), replace=TRUE)\n",
    "#   training <- data[indices,]\n",
    "#   testing <- data[-indices,]\n",
    "#   indep <- AutoSpearman(training, eclipse$indep)\n",
    "#   f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "#   \n",
    "#   glm.model <- glm(f, data = training, family=\"binomial\")\n",
    "#   defaulttree.model <- C5.0(x = training[, indep], y = training[,dep], rules=TRUE, trials=1)\n",
    "#   optimaltree.model <- C5.0(x = training[, indep], y = training[,dep], rules=TRUE, trials=100)\n",
    "#   rf10trees.model <- randomForest(f, data = training, importance = TRUE, ntree=10)\n",
    "#   rf100trees.model <- randomForest(f, data = training, importance = TRUE, ntree=100)\n",
    "# \n",
    "#   predictions <- data.frame(\n",
    "#     GLM = predict(glm.model, testing, type=\"response\"),\n",
    "#     C50.1trial = predict(defaulttree.model, testing, type=\"prob\")[,\"TRUE\"],\n",
    "#     C50.100trials = predict(optimaltree.model, testing, type=\"prob\")[,\"TRUE\"],\n",
    "#     RF.10trees = predict(rf10trees.model, testing, type=\"prob\")[,\"TRUE\"],\n",
    "#     RF.100trees = predict(rf100trees.model, testing, type=\"prob\")[,\"TRUE\"]\n",
    "#   )\n",
    "#   performance <- apply(predictions, 2, function(x) performance.calculation(testing[,dep], x))\n",
    "#   results[[\"AUC\"]] <- rbind(results[[\"AUC\"]], performance[\"AUC\",])\n",
    "#   results[[\"Fmeasure(0.5)\"]] <- rbind(results[[\"Fmeasure(0.5)\"]], performance[\"Fmeasure\",])\n",
    "#   \n",
    "#   performance <- apply(predictions, 2, function(x) performance.calculation(testing[,dep], x, threshold = 0.2))\n",
    "#   results[[\"Fmeasure(0.2)\"]] <- rbind(results[[\"Fmeasure(0.2)\"]], performance[\"Fmeasure\",])\n",
    "#   \n",
    "#   performance <- apply(predictions, 2, function(x) performance.calculation(testing[,dep], x, threshold = 0.8))\n",
    "#   results[[\"Fmeasure(0.8)\"]] <- rbind(results[[\"Fmeasure(0.8)\"]], performance[\"Fmeasure\",])\n",
    "# }\n",
    "# saveRDS(results, \"figures/parameter-settings.rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CoodgAAIEGdc845x87UNE3X9f79+8c/T+Kj2AEAgASVnZ19ySWXCCE0TYvMsdls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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results <- readRDS(\"figures/parameter-settings.rds\")\n",
    "ggplot(melt(results[[\"AUC\"]]), aes(x=reorder(Var2, -value, median), y=value)) + geom_boxplot()  + ylab(\"AUC\") + xlab(\"\") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous(breaks = 10:18*0.05, labels = 10:18*0.05, limit=c(0.5,0.9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP5: USE OUT-OF-SAMPLE BOOTSTRAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using method as id variables\n"
     ]
    },
    {
     "data": {
      "image/png": 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EAIOwCAQAg7AIBACDsAgEAIOwCAQAg7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\nBDsAAABBEOwAAAAEQbADAAAQxP8AC7BI9ARP5+EAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results <- NULL\n",
    "indep <- AutoSpearman(data, eclipse$indep)\n",
    "f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "\n",
    "for(i in seq(1,100)){\n",
    "  set.seed(1234+i)\n",
    "  indices <- sample(nrow(data), replace=TRUE)\n",
    "  training <- data[indices,]\n",
    "  testing <- data[-indices,]\n",
    "  \n",
    "  m <- glm(f, data = training, family=\"binomial\")\n",
    "  predictions <- predict(m, testing, type=\"response\")\n",
    "  performance <- performance.calculation(testing[,dep], predictions)\n",
    "  results <- rbind(results, c(method=\"100 Bootstrap\",performance[\"AUC\"]))\n",
    "}\n",
    "\n",
    "# 10x10-folds CV\n",
    "for(i in seq(1,10)){\n",
    "  set.seed(1234+i)\n",
    "  indices <- createFolds(data[, dep], k = 10, list = TRUE, returnTrain = TRUE)\n",
    "  for(i in seq(1,10)){\n",
    "    training <- data[indices[[i]],]\n",
    "    testing <- data[-indices[[i]],]\n",
    "    \n",
    "    m <- glm(f, data = training, family=\"binomial\")\n",
    "    predictions <- predict(m, testing, type=\"response\")\n",
    "    performance <- performance.calculation(testing[,dep], predictions)\n",
    "    results <- rbind(results, c(method=\"10X10-Fold CV\",performance[\"AUC\"]))\n",
    "  }\n",
    "}\n",
    "\n",
    "results <- data.frame(results)\n",
    "results$AUC <- as.numeric(as.character(results$AUC))\n",
    "\n",
    "ggplot(melt(results), aes(x=method, y=value)) + geom_boxplot() + ylab(\"value\") + xlab(\"\") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + facet_wrap(~variable, scales=\"free_y\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP6: SUMMARIZE BY A ScottKnott-ESD TEST "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No id variables; using all as measure variables\n"
     ]
    },
    {
     "data": {
      "image/png": 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4bNmzcnJydT7AAACB6cigUAAJAExQ4AAEASFDsAAABJUOwAAAAkQbEDAACQBMUO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\n7HLh84NOpVSzGoMyUHOvj8zm892Si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X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\nMAQaOwAAAACGQGMHAAAAwBD/AwcsTchVkMinAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance <- NULL\n",
    "indep <- AutoSpearman(data, eclipse$indep)\n",
    "f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "for(i in seq(1,100)){\n",
    "  indices <- sample(nrow(data), replace=TRUE)\n",
    "  training <- data[indices,]\n",
    "  m <- glm(f, data = training, family=\"binomial\")\n",
    "  importance <- rbind(importance, Anova(m,type=\"2\",test=\"LR\")$\"LR Chisq\")\n",
    "}\n",
    "\n",
    "importance <- data.frame(importance)\n",
    "colnames(importance) <- indep\n",
    "\n",
    "df <- melt(importance)\n",
    "df$rank <- sk_esd(importance)$groups[as.character(df$variable)]\n",
    "\n",
    "ggplot(df, aes(x=variable, y=value)) + geom_boxplot() + facet_grid(~rank, scales = \"free\", drop = TRUE) + theme(axis.text.x = element_text(angle = 45, hjust = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP7: VISUALIZE THE RELATIONSHIPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "indep <- AutoSpearman(data, eclipse$indep)\n",
    "f <- as.formula(paste( \"post\", '~', paste(indep, collapse = \"+\")))\n",
    "m <- glm(f, data = data, family=\"binomial\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VyHYgcAAAoqnlQgRKvLC4odAAAonK6k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\nAJIg7AAAACTxHysPpteEZqbjAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(effect(\"pre\",m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DON'T USE ANOVA TYPE-I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>NULL</th><td>NA</td><td>         NA</td><td>6728</td><td>5568.263</td></tr>\n",
       "\t<tr><th scope=row>TLOC</th><td> 1</td><td>709.1933977</td><td>6727</td><td>4859.070</td></tr>\n",
       "\t<tr><th scope=row>NSF_max</th><td> 1</td><td>  0.5941960</td><td>6726</td><td>4858.476</td></tr>\n",
       "\t<tr><th scope=row>NSM_max</th><td> 1</td><td>  0.1685477</td><td>6725</td><td>4858.307</td></tr>\n",
       "\t<tr><th scope=row>NOF_max</th><td> 1</td><td> 33.2212919</td><td>6724</td><td>4825.086</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Df & Deviance & Resid. Df & Resid. Dev\\\\\n",
       "  & <int> & <dbl> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNULL & NA &          NA & 6728 & 5568.263\\\\\n",
       "\tTLOC &  1 & 709.1933977 & 6727 & 4859.070\\\\\n",
       "\tNSF\\_max &  1 &   0.5941960 & 6726 & 4858.476\\\\\n",
       "\tNSM\\_max &  1 &   0.1685477 & 6725 & 4858.307\\\\\n",
       "\tNOF\\_max &  1 &  33.2212919 & 6724 & 4825.086\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 5 × 4\n",
       "\n",
       "| <!--/--> | Df &lt;int&gt; | Deviance &lt;dbl&gt; | Resid. Df &lt;int&gt; | Resid. Dev &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| NULL | NA |          NA | 6728 | 5568.263 |\n",
       "| TLOC |  1 | 709.1933977 | 6727 | 4859.070 |\n",
       "| NSF_max |  1 |   0.5941960 | 6726 | 4858.476 |\n",
       "| NSM_max |  1 |   0.1685477 | 6725 | 4858.307 |\n",
       "| NOF_max |  1 |  33.2212919 | 6724 | 4825.086 |\n",
       "\n"
      ],
      "text/plain": [
       "        Df Deviance    Resid. Df Resid. Dev\n",
       "NULL    NA          NA 6728      5568.263  \n",
       "TLOC     1 709.1933977 6727      4859.070  \n",
       "NSF_max  1   0.5941960 6726      4858.476  \n",
       "NSM_max  1   0.1685477 6725      4858.307  \n",
       "NOF_max  1  33.2212919 6724      4825.086  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indep1 <- c(\"TLOC\",\"NSF_max\",\"NSM_max\",\"NOF_max\")\n",
    "f <- as.formula(paste0(dep, \" ~ \", paste0(indep1,collapse = \"+\")))\n",
    "m <- glm(f, data=data, family=\"binomial\")\n",
    "anova(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A anova: 4 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>LR Chisq</th><th scope=col>Df</th><th scope=col>Pr(&gt;Chisq)</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>TLOC</th><td>630.30444430</td><td>1</td><td>4.291484e-139</td></tr>\n",
       "\t<tr><th scope=row>NSF_max</th><td>  0.09637628</td><td>1</td><td> 7.562224e-01</td></tr>\n",
       "\t<tr><th scope=row>NSM_max</th><td>  0.09127543</td><td>1</td><td> 7.625619e-01</td></tr>\n",
       "\t<tr><th scope=row>NOF_max</th><td> 33.22129193</td><td>1</td><td> 8.224503e-09</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A anova: 4 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & LR Chisq & Df & Pr(>Chisq)\\\\\n",
       "  & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTLOC & 630.30444430 & 1 & 4.291484e-139\\\\\n",
       "\tNSF\\_max &   0.09637628 & 1 &  7.562224e-01\\\\\n",
       "\tNSM\\_max &   0.09127543 & 1 &  7.625619e-01\\\\\n",
       "\tNOF\\_max &  33.22129193 & 1 &  8.224503e-09\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A anova: 4 × 3\n",
       "\n",
       "| <!--/--> | LR Chisq &lt;dbl&gt; | Df &lt;dbl&gt; | Pr(&gt;Chisq) &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| TLOC | 630.30444430 | 1 | 4.291484e-139 |\n",
       "| NSF_max |   0.09637628 | 1 |  7.562224e-01 |\n",
       "| NSM_max |   0.09127543 | 1 |  7.625619e-01 |\n",
       "| NOF_max |  33.22129193 | 1 |  8.224503e-09 |\n",
       "\n"
      ],
      "text/plain": [
       "        LR Chisq     Df Pr(>Chisq)   \n",
       "TLOC    630.30444430 1  4.291484e-139\n",
       "NSF_max   0.09637628 1   7.562224e-01\n",
       "NSM_max   0.09127543 1   7.625619e-01\n",
       "NOF_max  33.22129193 1   8.224503e-09"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Anova(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The risks of reordering model specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 4 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Type1.1</th><th scope=col>Type2.1</th><th scope=col>Type1.2</th><th scope=col>Type2.2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>ACD</th><td>28</td><td>47</td><td>49</td><td>47</td></tr>\n",
       "\t<tr><th scope=row>NOF_max</th><td>32</td><td>23</td><td>13</td><td>23</td></tr>\n",
       "\t<tr><th scope=row>NSM_max</th><td>11</td><td>19</td><td>31</td><td>19</td></tr>\n",
       "\t<tr><th scope=row>NSF_max</th><td>29</td><td>11</td><td> 7</td><td>11</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & Type1.1 & Type2.1 & Type1.2 & Type2.2\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tACD & 28 & 47 & 49 & 47\\\\\n",
       "\tNOF\\_max & 32 & 23 & 13 & 23\\\\\n",
       "\tNSM\\_max & 11 & 19 & 31 & 19\\\\\n",
       "\tNSF\\_max & 29 & 11 &  7 & 11\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 4\n",
       "\n",
       "| <!--/--> | Type1.1 &lt;dbl&gt; | Type2.1 &lt;dbl&gt; | Type1.2 &lt;dbl&gt; | Type2.2 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| ACD | 28 | 47 | 49 | 47 |\n",
       "| NOF_max | 32 | 23 | 13 | 23 |\n",
       "| NSM_max | 11 | 19 | 31 | 19 |\n",
       "| NSF_max | 29 | 11 |  7 | 11 |\n",
       "\n"
      ],
      "text/plain": [
       "        Type1.1 Type2.1 Type1.2 Type2.2\n",
       "ACD     28      47      49      47     \n",
       "NOF_max 32      23      13      23     \n",
       "NSM_max 11      19      31      19     \n",
       "NSF_max 29      11       7      11     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "indep1 <- c(\"NSF_max\",\"NSM_max\",\"NOF_max\",\"ACD\")\n",
    "f <- as.formula(paste0(dep, \" ~ \", paste0(indep1,collapse = \"+\")))\n",
    "m <- glm(f, data=data, family=\"binomial\")\n",
    "\n",
    "importance1 <- data.frame(Type1.1=anova(m)$Deviance[-1], Type2.1=Anova(m,type=\"2\",test=\"LR\")$\"LR Chisq\")\n",
    "rownames(importance1) <- indep1\n",
    "\n",
    "indep2 <- c(\"NSM_max\",\"ACD\",\"NSF_max\",\"NOF_max\")\n",
    "f <- as.formula(paste0(dep, \" ~ \", paste0(indep2,collapse = \"+\")))\n",
    "m <- glm(f, data=data, family=\"binomial\")\n",
    "importance2 <- data.frame(Type1.2=anova(m)$Deviance[-1], Type2.2=Anova(m,type=\"2\",test=\"LR\")$\"LR Chisq\")\n",
    "rownames(importance2) <- indep2\n",
    "\n",
    "importance <- data.frame(importance1[indep1,],importance2[indep1,])\n",
    "importance <- data.frame(apply(importance, 2, function(x){x/sum(abs(x))}))\n",
    "\n",
    "round(importance[order(-importance$Type2.1),], digit=2)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DON’T CHANGE PROBABILITY THRESHOLD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No id variables; using all as measure variables\n",
      "No id variables; using all as measure variables\n",
      "No id variables; using all as measure variables\n"
     ]
    },
    {
     "data": {
      "image/png": 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XG5XG63W891bTab1Wr1+XwlJSUqpwoE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EjvyOLx5j55Wd6zkO1TQGlf6XA0U9oHiN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\nOwAAAJ34f1a295Y6CxqgAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results <- readRDS(\"figures/parameter-settings.rds\")\n",
    "levels <- c(\"C50.100trials\",\"RF.100trees\",\"C50.1trial\",\"RF.10trees\",\"GLM\")\n",
    "g1<- ggplot(melt(data.frame(results[[\"Fmeasure(0.5)\"]])), aes(x=factor(variable, levels=levels), y=value)) + geom_boxplot()  + ylab(\"F-measure (0.5)\") + xlab(\"\") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous(labels=0:7*0.1, breaks=0:7*0.1, limit=c(0, 0.7))\n",
    "\n",
    "g2<- ggplot(melt(data.frame(results[[\"Fmeasure(0.8)\"]]),na.rm=TRUE), aes(x=factor(variable, levels=levels), y=value)) + geom_boxplot() + ylab(\"F-measure (0.8)\") + xlab(\"\") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous(labels=0:7*0.1, breaks=0:7*0.1, limit=c(0, 0.7))\n",
    "\n",
    "g3<- ggplot(melt(data.frame(results[[\"Fmeasure(0.2)\"]]),na.rm=TRUE), aes(x=factor(variable, levels=levels), y=value)) + geom_boxplot()  + ylab(\"F-measure (0.2)\") + xlab(\"\") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_y_continuous(labels=0:7*0.1, breaks=0:7*0.1, limit=c(0, 0.7))\n",
    "\n",
    "grid.arrange(g1,g2,g3,ncol=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DON’T REBALANCE THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No id variables; using all as measure variables\n",
      "No id variables; using all as measure variables\n"
     ]
    },
    {
     "data": {
      "image/png": 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laZ7dy5c86cOVJXUY6K73W3JlX7btq0qdh5AcB2nLh6X7Nnzw4ICJC6igqXnZ1d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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "indep <- c(\"TLOC\",\"PAR_max\",'NOI',\"NOF_max\",\"FOUT_max\",\"NSM_max\",\"NSF_max\",\"ACD\",\"NOM_max\")\n",
    "\n",
    "original.m <- fit(data, dep, indep, classifier=\"lr\", rebalance=\"no\", validation=\"boot\")\n",
    "down.m <- fit(data, dep, indep, classifier=\"lr\", rebalance=\"down\", validation=\"boot\")\n",
    "up.m <- fit(data, dep, indep, classifier=\"lr\", rebalance=\"up\", validation=\"boot\")\n",
    "\n",
    "auc <- data.frame(Original=original.m$performance$AUC, \n",
    "                  UnderSampling=down.m$performance$AUC,\n",
    "                  OverSampling=up.m$performance$AUC)\n",
    "g1 <- ggplot(melt(auc), aes(x=variable, y=value)) + geom_boxplot() + theme_bw() + ylab(\"AUC Performance\") + xlab(\"\") + scale_y_continuous(breaks=12:20*0.05, limits = c(0.6,0.9)) + theme(axis.text.x = element_text(angle = 45, hjust = 1))\n",
    "\n",
    "fmeasure <- data.frame(Original=original.m$performance$Fmeasure, \n",
    "                       UnderSampling=down.m$performance$Fmeasure,\n",
    "                       OverSampling=up.m$performance$Fmeasure)\n",
    "g2 <- ggplot(melt(fmeasure), aes(x=variable, y=value)) + geom_boxplot() + theme_bw() + ylab(\"F-Measure Performance\") + xlab(\"\") + scale_y_continuous(breaks=4:10*0.05, limits = c(0.2,0.5)) + theme(axis.text.x = element_text(angle = 45, hjust = 1))\n",
    "\n",
    "grid.arrange(g1,g2, ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part IV: Explaining Software Data <a class=\"anchor\" id=\"explaining\"></a>\n",
    "\n",
    "This part is in progress and the content will be available soon."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}