{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'2.3.6'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc as pm\n",
    "\n",
    "np.random.seed(20090425)\n",
    "\n",
    "pm.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Treatment Meta-analysis for ASD Interventions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As many of the interventions involve combinations of paradigms we identified categories that would best describe these combinations. Each intervention is described as involving the following elements, with some involving more than one.  \n",
    "\n",
    "- **High Intensity ABA** Greater than 10 hours of EIBI as part of intervention (inclusive of UCLA/Lovaas, ESDM, EIBI variants).\n",
    "- **Low Intensity ABA** Less than 10 hours of EIBI or variants.\n",
    "- **Low intensity parent training** Less than 5 hours per week of parent training.\n",
    "- **Defined school program** A formal, designated school enrollment as part of intervention.\n",
    "- **Eclectic school** Involvement in preschool with content unspecified.\n",
    "- **Community high intensity** Documented eclectic service provision in community of greater than 10 hours of service\n",
    "- **Adjunctive community service** Eclectic service provision unspecified or less than 10 hours of service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Import data\n",
    "cognition = pd.read_csv('data/Cognition.csv', \n",
    "                             na_values=['NR', 'nr', 'Varied see comment', 'Varied', 'see comment'],\n",
    "                             index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_outcomes = pd.read_csv('data/Cognition_outcomes.csv', index_col=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# replace some values\n",
    "cognition = cognition.replace({'immed': 0})\n",
    "cognition.time_to_last_fup = cognition.time_to_last_fup.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing diagnosis values should be zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diagnoses = ['diagnosis_autism', 'diagnosis_pdd-nos', 'diagnosis_asperger', 'diagnosis_other']\n",
    "cognition[diagnoses] = cognition[diagnoses].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.cognition_outcome.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace(\n",
    "                    ['IQ', 'Early learning composite', 'Intellectual functioning',\n",
    "                    'MDI', 'cognitive standard', 'Full scale IQ',\n",
    "                    'DQ', 'GMDS-ER GQ', 'Early Learning composite'], 'IQ Standard Score') \n",
    "\n",
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace(\n",
    "                    ['mental age', 'dev age', 'Developmental age'], 'IQ Age Scale')\n",
    "\n",
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace(\n",
    "                    ['Non-Verbal IQ', 'non-verbal IQ', 'Performance IQ',\n",
    "                    'Nonverbal IQ', 'Non-verbal', 'Non-verbal intelligence', \n",
    "                    'non-verbal cognition (VR)', 'Visual', 'Visual-spatial IQ', \n",
    "                    'Visual reception'], 'Non-verbal IQ')\n",
    "\n",
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace('Fine motor', \n",
    "                                                                                    'Motor IQ')\n",
    "\n",
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace(\n",
    "                    ['Receptive Language', 'Receptive language', 'Expressive language'], \n",
    "                    'Language IQ')\n",
    "\n",
    "cognition_outcomes.cognition_outcome = cognition_outcomes.cognition_outcome.replace(\n",
    "                    'Expressive language age', 'Language Age Scale')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop outcomes that cannot be categorized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_outcomes = cognition_outcomes[cognition_outcomes.cognition_outcome.isin(\n",
    "    ['Language and communication', 'Reciprocal social interaction']) ^ True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop null outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_outcomes = cognition_outcomes[cognition_outcomes.cognition_outcome.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm replacement of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IQ Standard Score', 'Language Age Scale', 'Language IQ',\n",
       "       'Non-verbal IQ', 'IQ Age Scale', 'Motor IQ'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.cognition_outcome.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obsid</th>\n",
       "      <th>cognition_outcome</th>\n",
       "      <th>tool</th>\n",
       "      <th>baseline_mean</th>\n",
       "      <th>baseline_sd</th>\n",
       "      <th>end_tx_mean</th>\n",
       "      <th>end_tx_sd</th>\n",
       "      <th>final_mean</th>\n",
       "      <th>final_sd</th>\n",
       "      <th>change_mean</th>\n",
       "      <th>change_sd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcomeid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Stanford-Binet and Bayley scale</td>\n",
       "      <td>61.43</td>\n",
       "      <td>16.43</td>\n",
       "      <td>73.48</td>\n",
       "      <td>27.28</td>\n",
       "      <td>64.65</td>\n",
       "      <td>33.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Stanford-Binet and Bayley scale</td>\n",
       "      <td>63.83</td>\n",
       "      <td>13.98</td>\n",
       "      <td>61.00</td>\n",
       "      <td>27.30</td>\n",
       "      <td>61.94</td>\n",
       "      <td>31.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>59.60</td>\n",
       "      <td>6.90</td>\n",
       "      <td>68.50</td>\n",
       "      <td>7.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>63.20</td>\n",
       "      <td>6.60</td>\n",
       "      <td>61.40</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>BSID, SB:FE, Weschler</td>\n",
       "      <td>51.60</td>\n",
       "      <td>16.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.60</td>\n",
       "      <td>24.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           obsid  cognition_outcome                             tool  \\\n",
       "outcomeid                                                              \n",
       "1              1  IQ Standard Score  Stanford-Binet and Bayley scale   \n",
       "2              2  IQ Standard Score  Stanford-Binet and Bayley scale   \n",
       "3              3  IQ Standard Score                           Mullen   \n",
       "4              4  IQ Standard Score                           Mullen   \n",
       "5              5  IQ Standard Score            BSID, SB:FE, Weschler   \n",
       "\n",
       "           baseline_mean  baseline_sd  end_tx_mean  end_tx_sd  final_mean  \\\n",
       "outcomeid                                                                   \n",
       "1                  61.43        16.43        73.48      27.28       64.65   \n",
       "2                  63.83        13.98        61.00      27.30       61.94   \n",
       "3                  59.60         6.90        68.50       7.50         NaN   \n",
       "4                  63.20         6.60        61.40       9.00         NaN   \n",
       "5                  51.60        16.90          NaN        NaN       66.60   \n",
       "\n",
       "           final_sd  change_mean  change_sd  \n",
       "outcomeid                                    \n",
       "1             33.04          NaN        NaN  \n",
       "2             31.09          NaN        NaN  \n",
       "3               NaN          8.9        NaN  \n",
       "4               NaN         -1.8        NaN  \n",
       "5             24.80          NaN        NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm numeric types throughout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "obsid                  int64\n",
       "cognition_outcome     object\n",
       "tool                  object\n",
       "baseline_mean        float64\n",
       "baseline_sd          float64\n",
       "end_tx_mean          float64\n",
       "end_tx_sd            float64\n",
       "final_mean           float64\n",
       "final_sd             float64\n",
       "change_mean          float64\n",
       "change_sd            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counts of unique treatments (treatment names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Eclectic                                        4\n",
       "TAU                                             3\n",
       "EIBI                                            3\n",
       "ABA                                             3\n",
       "control / TAU                                   2\n",
       "Parent training                                 2\n",
       "Clinic based\\nABA                               1\n",
       "Low intensity behavioral                        1\n",
       "control                                         1\n",
       "Intensive Eclectic                              1\n",
       "P-ESDM                                          1\n",
       "Intensive Rx-\\nUCLA                             1\n",
       "intervention manuals only                       1\n",
       "Parent directed                                 1\n",
       "Parent\\nmanaged\\nABA                            1\n",
       "Preschool Autism Communication\\nTrial [PACT]    1\n",
       "Local services                                  1\n",
       "UCLA                                            1\n",
       "behavioral                                      1\n",
       "non-intensive public early\\nintervention        1\n",
       "Local authority approach                        1\n",
       "intensive behavior\\nanalytic (IBT)              1\n",
       "low intensity behavioral                        1\n",
       "Non-IS                                          1\n",
       "Assess & Monitor group                          1\n",
       "LEAP                                            1\n",
       "Control                                         1\n",
       "HTMW                                            1\n",
       "EIBT-UCLA                                       1\n",
       "Clinic directed                                 1\n",
       "eclectic                                        1\n",
       "Special nursery                                 1\n",
       "Community based Rx                              1\n",
       "Interpersonal Synchrony (IS)                    1\n",
       "Portage                                         1\n",
       "ESDM                                            1\n",
       "Name: treatment, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition.treatment.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counts of unique tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSEL                                    24\n",
       "Merrill-Palmer                           8\n",
       "Mullen                                   8\n",
       "composite                                5\n",
       "PEP-R                                    4\n",
       "Bayley, Stanford-Binet                   2\n",
       "Griffith mental developmental scales     2\n",
       "Stanford-Binet and Bayley scale          2\n",
       "BSID, SB:FE, Weschler                    2\n",
       "Bayley-Dutch version                     2\n",
       "Bayley and WPPSI-R                       2\n",
       "Bayley and Merrill-Palmer                2\n",
       "Bayley, Merrill-Palmer                   2\n",
       "BSID-II                                  2\n",
       "WPPSI-R/WISC-R/BSID                      2\n",
       "Bayley                                   2\n",
       "GSID                                     2\n",
       "Stanford Binet and BSID-II               2\n",
       "MPSMT                                    2\n",
       "Merrill Palmer                           1\n",
       "Name: tool, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.tool.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counts of outcome measures used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IQ Standard Score     33\n",
       "Non-verbal IQ         21\n",
       "Language IQ           12\n",
       "IQ Age Scale           6\n",
       "Motor IQ               4\n",
       "Language Age Scale     2\n",
       "Name: cognition_outcome, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_outcomes.cognition_outcome.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build intervention network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of intervention classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "intervention_types = ['high_intensity_eibi', 'low_intensity_eibi',\n",
    "       'low_intensity_parent_training', 'defined_school_program',\n",
    "       'high_intensity_community', 'adjunctive_community_service',\n",
    "       'eclectic_school']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create edges for network using all pairwise combinations of interventions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "network_edges = list(itertools.combinations(intervention_types, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "network = dict.fromkeys(network_edges, 0)\n",
    "for i in intervention_types:\n",
    "    network[(i, i)] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create parallel network for sample size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sample_size = network.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Populate network dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loop over all studies\n",
    "for s in cognition.refid.unique():\n",
    "    \n",
    "    # Construct pairs of comparisons\n",
    "    study = cognition[cognition.refid==s]\n",
    "    study_interventions = study[intervention_types].sum()\n",
    "    # Get sample sizes\n",
    "    studies = cognition[cognition.refid==s]\n",
    "    n_analysis = studies.n_analysis\n",
    "    \n",
    "    # Generate pairs of interventions for this study\n",
    "    pairs = list(itertools.combinations(study_interventions.index[study_interventions==1].values, 2))\n",
    "    \n",
    "    # Loop over pairs within study, and increment comparison count\n",
    "    for pair in pairs:\n",
    "        \n",
    "        network[pair] += 1\n",
    "\n",
    "        sample_size[pair] += n_analysis[cognition[cognition.refid==s][pair[0]]==1].values[0]\n",
    "        sample_size[pair] += n_analysis[cognition[cognition.refid==s][pair[1]]==1].values[0]\n",
    "        \n",
    "    \n",
    "    # Account for comparisons within a class\n",
    "    within_comparisons = study_interventions[study_interventions==2].index.values\n",
    "    for c in within_comparisons:\n",
    "        network[(c, c)] += 1\n",
    "        sample_size[(c, c)] += n_analysis[cognition[cognition.refid==s][c]==1].values[0]\n",
    "        sample_size[(c, c)] += n_analysis[cognition[cognition.refid==s][c]==1].values[1]\n",
    "        \n",
    "nonzero_network = {tuple(ni.replace('_', ' ') for ni in n): network[n] for n in network if network[n]}\n",
    "nonzero_samples = {tuple(ni.replace('_', ' ') for ni in n): sample_size[n] for n in sample_size if sample_size[n]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of studies with direct comparisons between pairs of interventions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('adjunctive community service', 'adjunctive community service'): 1,\n",
       " ('adjunctive community service', 'eclectic school'): 1,\n",
       " ('defined school program', 'adjunctive community service'): 2,\n",
       " ('defined school program', 'defined school program'): 2,\n",
       " ('defined school program', 'eclectic school'): 3,\n",
       " ('eclectic school', 'eclectic school'): 1,\n",
       " ('high intensity community', 'eclectic school'): 1,\n",
       " ('high intensity eibi', 'adjunctive community service'): 3,\n",
       " ('high intensity eibi', 'defined school program'): 4,\n",
       " ('high intensity eibi', 'eclectic school'): 3,\n",
       " ('high intensity eibi', 'high intensity community'): 2,\n",
       " ('high intensity eibi', 'high intensity eibi'): 3,\n",
       " ('high intensity eibi', 'low intensity parent training'): 1,\n",
       " ('low intensity eibi', 'eclectic school'): 1,\n",
       " ('low intensity parent training', 'adjunctive community service'): 3,\n",
       " ('low intensity parent training', 'defined school program'): 1,\n",
       " ('low intensity parent training', 'eclectic school'): 1}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample sizes for each comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('adjunctive community service', 'adjunctive community service'): 152,\n",
       " ('adjunctive community service', 'eclectic school'): 31,\n",
       " ('defined school program', 'adjunctive community service'): 77,\n",
       " ('defined school program', 'defined school program'): 112,\n",
       " ('defined school program', 'eclectic school'): 112,\n",
       " ('eclectic school', 'eclectic school'): 41,\n",
       " ('high intensity community', 'eclectic school'): 32,\n",
       " ('high intensity eibi', 'adjunctive community service'): 111,\n",
       " ('high intensity eibi', 'defined school program'): 184,\n",
       " ('high intensity eibi', 'eclectic school'): 105,\n",
       " ('high intensity eibi', 'high intensity community'): 90,\n",
       " ('high intensity eibi', 'high intensity eibi'): 90,\n",
       " ('high intensity eibi', 'low intensity parent training'): 28,\n",
       " ('low intensity eibi', 'eclectic school'): 40,\n",
       " ('low intensity parent training', 'adjunctive community service'): 172,\n",
       " ('low intensity parent training', 'defined school program'): 48,\n",
       " ('low intensity parent training', 'eclectic school'): 26}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `nx.draw_networkx` not found.\n"
     ]
    }
   ],
   "source": [
    "nx.draw_networkx?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `nx.draw_networkx` not found.\n"
     ]
    }
   ],
   "source": [
    "nx.draw_networkx?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v2/ClKPGBuRjwGvGlHGXvnpsKDCd+NY8vgI7ADUdHKlauzKhRo5KcjZ81axbjx4+nbNmy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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b4ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "\n",
    "G = nx.Graph()\n",
    "\n",
    "for n in nonzero_network:\n",
    "    G.add_edge(n[0], n[1] ,weight=nonzero_network[n])\n",
    "    \n",
    "pos=nx.spring_layout(G)\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "nx.draw_networkx(G, pos=pos, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UbIjIVUfBhoiIiJUZCgqa5DpVR49yrvpZIiJNTcGGiIiItVVWNsllTuTl8cILL/Dll19y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\nIiIeobAhIiIiIiIe8f8AjxYQ1t3/8W8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cc0b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "nx.draw_networkx(G, pos, with_labels=False, node_size=700)\n",
    "\n",
    "nmax = float(max(nonzero_samples.values()))\n",
    "\n",
    "for i in range(max(nonzero_network.values())):\n",
    "    # Generate edge with appropriate weight\n",
    "    edge_i = [(u,v) for (u,v,d) in G.edges(data=True) if d['weight']==(i+1)]\n",
    "    # Generate alpha for edges\n",
    "    try:\n",
    "        alpha = nonzero_samples[edge_i[0]] / nmax\n",
    "    except KeyError:\n",
    "        # Sometimes the pairs are backwards\n",
    "        alpha = nonzero_samples[edge_i[0][::-1]] / nmax\n",
    "    nx.draw_networkx_edges(G, pos, edgelist=edge_i, width=(i+1)*3, alpha=alpha)\n",
    "    \n",
    "pos_shifted = {p: (pos[p][0], pos[p][1]+0.05) for p in pos}\n",
    "nx.draw_networkx_labels(G, pos=pos_shifted)\n",
    "plt.gca().xaxis.set_major_locator(plt.NullLocator())\n",
    "plt.gca().yaxis.set_major_locator(plt.NullLocator());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge study information and outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Columns from the cognition table that we are interested in\n",
    "cols_to_keep = ['refid', 'author', 'year', 'treatment', \n",
    "       'high_intensity_eibi', 'low_intensity_eibi',\n",
    "       'low_intensity_parent_training', 'defined_school_program',\n",
    "       'high_intensity_community', 'adjunctive_community_service',\n",
    "       'eclectic_school', 'intervention_duration', \n",
    "       'n_analysis', 'age_enroll_mean', 'age_enroll_sd',\n",
    "       'age_enroll_lo', 'age_enroll_hi', 'diagnosis_autism',\n",
    "       'diagnosis_pdd-nos', 'diagnosis_asperger', 'diagnosis_other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data = cognition[cols_to_keep].merge(cognition_outcomes, left_index=True, right_on='obsid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export and peek at merged dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>refid</th>\n",
       "      <th>author</th>\n",
       "      <th>year</th>\n",
       "      <th>treatment</th>\n",
       "      <th>high_intensity_eibi</th>\n",
       "      <th>low_intensity_eibi</th>\n",
       "      <th>low_intensity_parent_training</th>\n",
       "      <th>defined_school_program</th>\n",
       "      <th>high_intensity_community</th>\n",
       "      <th>adjunctive_community_service</th>\n",
       "      <th>...</th>\n",
       "      <th>cognition_outcome</th>\n",
       "      <th>tool</th>\n",
       "      <th>baseline_mean</th>\n",
       "      <th>baseline_sd</th>\n",
       "      <th>end_tx_mean</th>\n",
       "      <th>end_tx_sd</th>\n",
       "      <th>final_mean</th>\n",
       "      <th>final_sd</th>\n",
       "      <th>change_mean</th>\n",
       "      <th>change_sd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcomeid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6276_240</td>\n",
       "      <td>Kovshoff</td>\n",
       "      <td>2011</td>\n",
       "      <td>EIBI</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Stanford-Binet and Bayley scale</td>\n",
       "      <td>61.43</td>\n",
       "      <td>16.43</td>\n",
       "      <td>73.48</td>\n",
       "      <td>27.28</td>\n",
       "      <td>64.65</td>\n",
       "      <td>33.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>6276_240</td>\n",
       "      <td>Kovshoff</td>\n",
       "      <td>2011</td>\n",
       "      <td>EIBI</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>IQ Age Scale</td>\n",
       "      <td>Bayley</td>\n",
       "      <td>22.04</td>\n",
       "      <td>6.89</td>\n",
       "      <td>33.70</td>\n",
       "      <td>10.16</td>\n",
       "      <td>44.39</td>\n",
       "      <td>16.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6276_240</td>\n",
       "      <td>Kovshoff</td>\n",
       "      <td>2011</td>\n",
       "      <td>Control</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Stanford-Binet and Bayley scale</td>\n",
       "      <td>63.83</td>\n",
       "      <td>13.98</td>\n",
       "      <td>61.00</td>\n",
       "      <td>27.30</td>\n",
       "      <td>61.94</td>\n",
       "      <td>31.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>6276_240</td>\n",
       "      <td>Kovshoff</td>\n",
       "      <td>2011</td>\n",
       "      <td>Control</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>IQ Age Scale</td>\n",
       "      <td>Bayley</td>\n",
       "      <td>23.71</td>\n",
       "      <td>6.00</td>\n",
       "      <td>29.81</td>\n",
       "      <td>9.89</td>\n",
       "      <td>38.00</td>\n",
       "      <td>17.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6493</td>\n",
       "      <td>Strain</td>\n",
       "      <td>2011</td>\n",
       "      <td>LEAP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>IQ Standard Score</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>59.60</td>\n",
       "      <td>6.90</td>\n",
       "      <td>68.50</td>\n",
       "      <td>7.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              refid    author  year treatment  high_intensity_eibi  \\\n",
       "outcomeid                                                            \n",
       "1          6276_240  Kovshoff  2011      EIBI                    1   \n",
       "49         6276_240  Kovshoff  2011      EIBI                    1   \n",
       "2          6276_240  Kovshoff  2011   Control                    0   \n",
       "50         6276_240  Kovshoff  2011   Control                    0   \n",
       "3              6493    Strain  2011      LEAP                    0   \n",
       "\n",
       "           low_intensity_eibi  low_intensity_parent_training  \\\n",
       "outcomeid                                                      \n",
       "1                           0                              0   \n",
       "49                          0                              0   \n",
       "2                           0                              0   \n",
       "50                          0                              0   \n",
       "3                           0                              0   \n",
       "\n",
       "           defined_school_program  high_intensity_community  \\\n",
       "outcomeid                                                     \n",
       "1                               0                         0   \n",
       "49                              0                         0   \n",
       "2                               0                         0   \n",
       "50                              0                         0   \n",
       "3                               1                         0   \n",
       "\n",
       "           adjunctive_community_service    ...      cognition_outcome  \\\n",
       "outcomeid                                  ...                          \n",
       "1                                     0    ...      IQ Standard Score   \n",
       "49                                    0    ...           IQ Age Scale   \n",
       "2                                     1    ...      IQ Standard Score   \n",
       "50                                    1    ...           IQ Age Scale   \n",
       "3                                     0    ...      IQ Standard Score   \n",
       "\n",
       "                                      tool  baseline_mean  baseline_sd  \\\n",
       "outcomeid                                                                \n",
       "1          Stanford-Binet and Bayley scale          61.43        16.43   \n",
       "49                                  Bayley          22.04         6.89   \n",
       "2          Stanford-Binet and Bayley scale          63.83        13.98   \n",
       "50                                  Bayley          23.71         6.00   \n",
       "3                                   Mullen          59.60         6.90   \n",
       "\n",
       "           end_tx_mean  end_tx_sd  final_mean  final_sd  change_mean  \\\n",
       "outcomeid                                                              \n",
       "1                73.48      27.28       64.65     33.04          NaN   \n",
       "49               33.70      10.16       44.39     16.39          NaN   \n",
       "2                61.00      27.30       61.94     31.09          NaN   \n",
       "50               29.81       9.89       38.00     17.44          NaN   \n",
       "3                68.50       7.50         NaN       NaN          8.9   \n",
       "\n",
       "           change_sd  \n",
       "outcomeid             \n",
       "1                NaN  \n",
       "49               NaN  \n",
       "2                NaN  \n",
       "50               NaN  \n",
       "3                NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_data.to_csv('data/cognition_merged.csv')\n",
    "cognition_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build outcome network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another network to display the number of studies that contain multiple outcome measures, which allows us to relate outcomes to one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IQ Standard Score', 'Language Age Scale', 'Language IQ',\n",
       "       'Non-verbal IQ', 'IQ Age Scale', 'Motor IQ'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_types = cognition_outcomes.cognition_outcome.unique()\n",
    "outcome_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate set of edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outcome_network_edges = list(itertools.combinations(outcome_types, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outcome_network = dict.fromkeys(outcome_network_edges, 0)\n",
    "for i in outcome_types:\n",
    "    outcome_network[(i, i)] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The set of comparisons in each study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "refid\n",
       "1117                                           [Non-verbal IQ]\n",
       "1264                         [IQ Standard Score, IQ Age Scale]\n",
       "546                         [IQ Standard Score, Non-verbal IQ]\n",
       "5652                                       [IQ Standard Score]\n",
       "5680                        [IQ Standard Score, Non-verbal IQ]\n",
       "5715                                       [IQ Standard Score]\n",
       "588                         [IQ Standard Score, Non-verbal IQ]\n",
       "6245_6549                                  [IQ Standard Score]\n",
       "6276_240                     [IQ Standard Score, IQ Age Scale]\n",
       "6284                                       [IQ Standard Score]\n",
       "6288                         [Language Age Scale, Language IQ]\n",
       "6304_8035                         [Language IQ, Non-verbal IQ]\n",
       "6322                                       [IQ Standard Score]\n",
       "6427                                       [IQ Standard Score]\n",
       "647                         [IQ Standard Score, Non-verbal IQ]\n",
       "6493                                       [IQ Standard Score]\n",
       "6537_6592               [Non-verbal IQ, Motor IQ, Language IQ]\n",
       "6682                                       [IQ Standard Score]\n",
       "734                         [IQ Standard Score, Non-verbal IQ]\n",
       "9006         [IQ Age Scale, Non-verbal IQ, Motor IQ, Langua...\n",
       "997                                            [Non-verbal IQ]\n",
       "Name: cognition_outcome, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome_comparisons = cognition_data.groupby('refid')['cognition_outcome'].unique()\n",
    "outcome_comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loop over all studies\n",
    "for oc in outcome_comparisons:\n",
    "    \n",
    "    # Generate pairs of interventions for this study\n",
    "    pairs = list(itertools.combinations(oc, 2))\n",
    "    \n",
    "    # Loop over pairs within study, and increment comparison count\n",
    "    for pair in pairs:\n",
    "        \n",
    "        try:\n",
    "            outcome_network[pair] += 1\n",
    "        except KeyError:\n",
    "            outcome_network[pair[::-1]] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YNmwYo0aNUhdEenl5UaVKlSIcLaSmpnLs2DG1JOSff/6he/fu6mLGFi1alLmfkRDCeCRM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\nJiIiIiJS0f8BSd445pjNEi4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2c39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G2 = nx.Graph()\n",
    "\n",
    "for n in outcome_network:\n",
    "    G2.add_edge(n[0], n[1], weight=outcome_network[n])\n",
    "    \n",
    "pos2=nx.spring_layout(G2)\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "nx.draw_networkx(G2, pos=pos2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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POzu7Sv9aiKoKzmxUMpPffoO8iCUZte3a3Lno8J//aP28TZs2hZubG15//XV0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\nERERCYJhg4iIiIiIBPH/AGTwdS9bQeMOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109939b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "nx.draw_networkx(G2, pos2, node_size=700, with_labels=False)\n",
    "\n",
    "for i in range(max(outcome_network.values())):\n",
    "    # Generate edge with appropriate weight\n",
    "    edge_i = [(u,v) for (u,v,d) in G2.edges(data=True) if d['weight']==(i+1)]\n",
    "    \n",
    "    nx.draw_networkx_edges(G2, pos2, edgelist=edge_i, width=(i+1)*3)\n",
    "    \n",
    "pos_shifted2 = {p: (pos2[p][0]+0.02, pos2[p][1]-0.05) for p in pos2}\n",
    "nx.draw_networkx_labels(G2, pos_shifted2, font_size=10, font_family='sans-serif')\n",
    "plt.gca().xaxis.set_major_locator(plt.NullLocator())\n",
    "plt.gca().yaxis.set_major_locator(plt.NullLocator());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Histogram of outcome values across outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10a83f400>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tSO0ULERELkOJiYk0a9as0jZjTKXQcHZZdU+Ul5dX2ebpsReiXbt2fPvtt+7X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\nbKNgISIiIrZRsBARERHb/H8T6I10wM4fLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2f2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "plot_axes = cognition_data.groupby('cognition_outcome')['baseline_mean'].hist(bins=25, grid=False, alpha=0.4)\n",
    "plt.legend(plot_axes.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zgwJXUD2PUpa0AXAc4F58ZmZWuVaGOloEfECS74cyM7PKFXlg4QXA6sAYUk8+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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abc2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cognition_data['baseline_mean'].hist(by=cognition_data['cognition_outcome'], sharey=True, sharex=False, bins=10)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above, it is clear that there is little information in the age scale outcomes and in motor IQ, so we will restrict attention to the other three outcome measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "outcomes_MA = ['IQ Standard Score', 'Language IQ', 'Non-verbal IQ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA = cognition_data[cognition_data.cognition_outcome.isin(outcomes_MA)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop Zachor 2007 since there is no post-treatement value for the included outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA = cognition_data_MA[cognition_data_MA.refid!='5652']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eliminate `change_mean` values for 6493 since it has no corresponding variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA.change_mean[cognition_data_MA.refid=='6493'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop 546 because it has no variance for treatment mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA = cognition_data_MA[cognition_data_MA.refid!='546']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high_intensity_eibi              23\n",
       "low_intensity_eibi                4\n",
       "low_intensity_parent_training     6\n",
       "defined_school_program           15\n",
       "high_intensity_community          3\n",
       "adjunctive_community_service      6\n",
       "eclectic_school                  11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_data_MA[intervention_types].sum(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Treatment Comparison MA model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create indicators for study, intervention, outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unique_studies = cognition_data_MA.refid.unique()\n",
    "cognition_data_MA['study_id'] = np.squeeze([np.where(r==unique_studies) for r in cognition_data_MA.refid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "cognition_data_MA['outcome_id'] = 0\n",
    "\n",
    "for i,o in enumerate(outcomes_MA):\n",
    "    if i:\n",
    "        cognition_data_MA.outcome_id[cognition_data_MA.cognition_outcome==o] = i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulting indicators for all outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>study_id</th>\n",
       "      <th>outcome_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcomeid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           study_id  outcome_id\n",
       "outcomeid                      \n",
       "1                 0           0\n",
       "2                 0           0\n",
       "3                 1           0\n",
       "4                 1           0\n",
       "5                 2           0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cognition_data_MA[['study_id', 'outcome_id']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take final outcomes where available, end-of-treatment otherwise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA['after_mean'] = cognition_data_MA.apply(lambda x: \n",
    "                            (x.final_mean, x.end_tx_mean)[np.isnan(x.final_mean)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA['after_sd'] = cognition_data_MA.apply(lambda x: (x.final_sd, x.end_tx_sd)[np.isnan(x.final_sd)], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate difference of before and after treatment, assuming Gaussian outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "change_missing = cognition_data_MA.change_mean.isnull()\n",
    "cognition_data_MA.loc[change_missing, 'change_mean'] = (cognition_data_MA.after_mean[change_missing] - \n",
    "                                                 cognition_data_MA.baseline_mean[change_missing])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cognition_data_MA.loc[change_missing, 'change_sd'] = np.sqrt(cognition_data_MA.after_sd[change_missing]**2 \n",
    "                                    + cognition_data_MA.baseline_sd[change_missing]**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Export MA data for reference\n",
    "cognition_data_MA.to_csv('data/cognition_data_MA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate standard error of mean change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Local variables for data\n",
    "intervention_ind = cognition_data_MA[intervention_types]\n",
    "(high_intensity_eibi, low_intensity_eibi, \n",
    " low_intensity_parent_training, defined_school_program, \n",
    " high_intensity_community, adjunctive_community_service, \n",
    " eclectic_school) = intervention_ind.values.T.astype(int)\n",
    "\n",
    "intervention_duration = cognition_data_MA.intervention_duration\n",
    "n_analysis = cognition_data_MA.n_analysis.values\n",
    "\n",
    "(age_enroll_mean, age_enroll_sd, age_enroll_lo, age_enroll_hi) = cognition_data_MA[\n",
    "    ['age_enroll_mean','age_enroll_sd','age_enroll_lo','age_enroll_hi']].values.T.astype(float)\n",
    "\n",
    "(diagnosis_autism, diagnosis_pdd_nos, \n",
    " diagnosis_asperger, diagnosis_other) = cognition_data_MA[['diagnosis_autism',\n",
    "                                     'diagnosis_pdd-nos',\n",
    "                                     'diagnosis_asperger',\n",
    "                                     'diagnosis_other']]\n",
    "\n",
    "(baseline_mean, baseline_sd, change_mean, change_sd) =  cognition_data_MA[['baseline_mean',\n",
    "                                     'baseline_sd',\n",
    "                                     'change_mean',\n",
    "                                     'change_sd']].values.T.astype(float)\n",
    " \n",
    "study_id = cognition_data_MA.study_id.values\n",
    "outcome_id = cognition_data_MA.outcome_id.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x109aedef0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GWusiQDqKhcu4YwpvKVN4IDlZ8rlIeA9n2+nfASil2gJPA1nA7Oou0lrbgSnA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105556d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cognition_data_MA.groupby('cognition_outcome')['change_mean'].plot(kind='kde')\n",
    "plt.xlabel('Mean difference in outcome');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The three outcome measures are modeled as a multivariate normal random variable, to account for the likely correlation among the measures. Thus, we specify the mean vector and variance-covariance matrix with diffuse normal and Wishart priors, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "μ = pm.Normal('μ', 0, 0.001, value=[0]*3)\n",
    "σ = pm.HalfCauchy('σ', 0, 5, value=[3]*3)\n",
    "ρ = pm.Uniform('ρ', -1, 1, value=[0]*3)\n",
    "@pm.deterministic\n",
    "def Τ(σ=σ, ρ=ρ):\n",
    "    var = σ**2\n",
    "    S = np.eye(3)*var\n",
    "    S[0,1] = S[1,0] = σ[0]*σ[1]*ρ[0]\n",
    "    S[0,2] = S[2,0] = σ[0]*σ[2]*ρ[1]\n",
    "    S[2,1] = S[1,2] = σ[1]*σ[2]*ρ[2]\n",
    "    return np.linalg.inv(S)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, each study is a draw from a multivariate normal with the above parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = [pm.MvNormal('m_{}'.format(i), μ, Τ) for i in range(len(unique_studies))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters of interest are the indicators for each intervention category: \n",
    "\n",
    "- `high_intensity_eibi`\n",
    "- `low_intensity_eibi`\n",
    "- `low_intensity_parent_training`\n",
    "- `defined_school_program`\n",
    "- `high_intensity_community`\n",
    "- `adjunctive_community_service`\n",
    "- `eclectic_school`\n",
    "\n",
    "Each study arm will have one or more of these indicators contributing to the response. We might like to have interactions among these parameters, but we likely do not have enough studies to fit all the additional parameters precisely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "β = pm.Normal('β', 0, 0.001, value=[0]*7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, since it appears there is a slight negative relationship between the treatment effect and the baseline score (see plot below), we model the mean baseline score for each treatment arm as a confounder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AVYAT1B4cuCLHulWjgTYzmxfu2xZuU9IQKSGleHFtamxk4WmncfP69VQQzH6+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\nO5kSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1bc9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = cognition_data_MA[['after_mean', 'baseline_mean']].dropna()\n",
    "x = plot_data.baseline_mean.values\n",
    "y = (plot_data.after_mean - plot_data.baseline_mean).values\n",
    "fit = np.polyfit(x, y, 1)\n",
    "fit_fn = np.poly1d(fit)\n",
    "xvals = np.linspace(0, 100)\n",
    "plt.plot(x, y, 'ro', xvals, fit_fn(xvals), '--k')\n",
    "plt.xlabel('Baseline score'); plt.ylabel('Outcome difference');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "α_base = pm.Normal('α_base', 0, 0.0001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting this all together, the expected value for each result is the treatment effect mean for each outcome, along with the additive effects of treatment class and the baseline score effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@pm.deterministic\n",
    "def θ(m=m, β=β, α=α_base): \n",
    "    mi = [m[i][j] for i,j in zip(study_id, outcome_id)]\n",
    "    return(mi + np.dot(intervention_ind, β) \n",
    "     + α*(baseline_mean - baseline_mean.mean())/baseline_mean.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the likelihood is just a normal distribution, with the observed standard error of the treatment effect as the standard deviation of the estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "change_se = change_sd/np.sqrt(n_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = pm.Normal('d', θ, change_se**-2, observed=True, value=change_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to check our model, we simulate data from the posterior predictive distribution, to compare with observed values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d_sim = pm.Normal('d_sim', θ, change_se**-2, value=change_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indicator the best treatment, to be used to calculate the probability of best treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best = pm.Lambda('best', lambda b=β:  (b==b.max()).astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the model using Markov chain Monte Carlo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/Repositories/pymc/pymc/Node.py:403: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  self.__name__ = input['__name__']\n"
     ]
    }
   ],
   "source": [
    "M = pm.MCMC(locals())\n",
    "# M.use_step_method(pm.AdaptiveMetropolis, M.m)\n",
    "# M.use_step_method(pm.AdaptiveMetropolis, M.mu)\n",
    "# M.use_step_method(pm.AdaptiveMetropolis, M.beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_iterations, n_burn = 500000, 450000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 200000 of 200000 complete in 1140.9 sec"
     ]
    }
   ],
   "source": [
    "M.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 200000 of 200000 complete in 1241.2 sec"
     ]
    }
   ],
   "source": [
    "M.sample(n_iterations, n_burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sftLAsAJBGixWP5XJ6idJklQ8kxpJklQEkxpJklQEkxpJklQEkxpJasJnP0n9x+onDQwr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b277b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "pm.Matplot.summary_plot([M.β, M.α_base], custom_labels=[\n",
    "                                     'high intensity eibi',\n",
    "                                     'low intensity eibi',\n",
    "                                     'low intensity parent training',\n",
    "                                     'defined school program',\n",
    "                                     'high intensity community',\n",
    "                                     'adjunctive community service',\n",
    "                                     'eclectic school',\n",
    "                                     'baseline score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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+DY1x3NWVej8hRrqMMn36UABGjlzK5MnnonWMGKT632Yz675GuGfjDuCodO/F\nAOAIgDQysEzSKABlWtI6a/MfTl3UdRfwbyn9sYoVSNsAr0TE1cA3geHAg8A2kvZJZQakE3KlvgUw\nBzhI0hap3L+RjTJUyp/dxTZtBIxO6eOAO/PdTT8fJBvV2Sm9Pz619yCwo6QdUv5Y3ghWyvs+EHgy\npU8gu8xjGyBJTJlyHm1tQ2lrG7pegYaZNZ+6/4QZEQskTQYWASvITr4lHwN+KOksYGOyIf92spNj\n/nNRtXT+fbW6TgWukXQG2aWLSp+3hgHfkLSG7J6Qz0bEKkljgUmS+pONCBxaoW+l7XxS0n+R3Vch\nsssxpRszK+ZX6Qtkoxf7pm15iixg6LC9EfGKpE+SXTrqS7ZfL0n9/jxwi6SXyO7RWJNbN9/mD4Dr\nJZ0A3ALkpwVX61vF7bfGJ4mWlpauC3aDn41i1lz8bJQmJGllRAxYj/U3jYiXUvr7wEMRcVGPdfDN\nfBCamTU+PxtlA7O+J+8TJS2QtJTsUsmPeqBPZma2gfLIhtUDH4RmZo3PIxtmZmZWGw42zMzMrFAO\nNsys7vjZKGbNxfdsWD3wQWgdSFrvh7mZWa/zPRtmZmZWGw42zMzMrFAONszMzKxQDjbMzMysUA42\nzKzu+NkoZs3Fs1GsHvggNDNrfJ6NYmZmZrXhYMPMzMwK5WDDzMzMCuVgw8zMzArlYMPM6o6fjWLW\nXDwbxeqBD0LrwM9GMWtIno1iZmZmteFgw8zMzArlYMPMzMwK5WDDzMzMCuVgw8zqjp+NYtZcPBvF\n6oEPQjOzxufZKGZmZlYbDjbMzMysUA42zMzMrFAONszMzKxQDjbMrO742ShmzcWzUawe+CC0Dvxs\nFLOG5NkoZmZmVhsONszMzKxQDjbMzMysUA42zMzMrFAONsys7vjZKGbNxbNRrB74IDQza3yejWJm\nZma1UbNgQ9IQSYurLDtH0iFdrD9R0pe70c6vJQ3soswnJG3TVV1rS9Jd6ecOko7t6frriaQfS9o1\npc+sdX/MzKx+1OXIRkRMiIjbuyrWzboOj4gXuig2Dti2O/WtjYhoTcl3Acf1dP31JCJOjIj/S2//\nu6adsQ1ORNDe3k57e7u/DMysDtU62Ogj6VJJSyRNl9QPQNKVko5J6X+V9ICkuZIulnRzbv3dJc2U\n9IikL1ZqQNJySYPSSMoD5e1JGgXsA1wtaX7KGyFpVmrzFklbp7pmSbpA0n2SHpT0/pS/R8pbIGmR\npJ1S/osUkEuZAAAX0klEQVSpGxcAB6blp0maLWnPXB/bJA0r63cfSd+UtDjVeXLKPyT1s13S5ZLe\nktvO81MbcyXtLelWSQ9LOimVOTi1/cu0zy6QdLykOam+Hcv3f3470vqzJE1N+/LnuTKz0n67AOif\n+vHzNEp1aq7ceZJO6e4BYtaViGDMmPG0ti6htXUJY8ee5YDDrN5ERE1ewBBgFdCS3k8GPpbSVwBH\nA/2APwI7pPxrgJtSeiJwF7AxsAXwV6BPhXaWAYO6aG8msHdKbwzcDWyR3o8FLs+V+0ZKfwiYkdKT\ngONSui/QL6VXpp8HATfn+nQC8J2U3hm4v0K/PwdMATZK7zfP7Y93p7yrgFNz23lSSn8baAc2BbYE\nnkz5BwPPAlsBbwH+DExMy07J9ekK4JhcX1bm1n+ObBRIaT8dUGEfrsytuwMwL6U3Ah4GNi/bXmsA\n0JuvCb3cXu+8zJpc1XN+rUc2lkVEe0rPIwsISgTsCjwaEY+lvGt5427XAH4VEasi4hlgBdlJdH3a\nA9gF2AO4TdICYDywXa7ctPRzfm79u4EzJf0nMCQiXilrt/wO3euAIyT1BT5FdnIvdwjwo4hYAxAR\nz6a+LYuIh1OZq4AP5Na5Kf1cDNwTES9FxF+BV3P3rdwfEU9FxGtkJ/7pKX9J2f6oZk5EPBERASzs\nap30u3tG0l7AYcD8tC11SfKr2qt3ndPbDfaKWv8OG/llja1vjdt/NZdeDfQvW14+Flp+yL1Wtn5X\n21PeXr8KbQlYGhEHdFHH6+1FxLWS7gWOAH4j6aSImFmtExHxsqQZwFHAaGDvKkXLt7fS/sjnlfq2\nho77Zg1v7JtXy/JfrVDmH6RLbJI2IhsFKW8DurfPAS4DPkkWDP6kG+VrJsr3sNWE1P3fRUR2GWX6\n9KEAjBy5lMmTz0U+Q5nVjVqPbHQmgAeBHSXtkPLG0jEoWF+lOlYCpU/+DwKDJe0HIGljSbt3Wom0\nY0Qsi4hJwI3AsLIiK4EBZXmXAReTjRQ8X6HaGcBJkvqkNjYHHgKGlO4JAY4HZneyXetqOTAipT9C\ndmlpbaxKozYlNwAjye6NmV55FbN1I4kpU86jrW0obW1DHWiY1aFaBxvln106vE+XIz4P3CJpLvAC\n8HyubHc++0SVdP79lcAlkuaT7ZNRwIWSFgILgP27qHtMuul0AdklmJ+WLV8ErJa0sHSzZETMT9tS\n6RIKZMHIH4H21I9j0/74JDBVUjvZCMQlVbaz0nZ3ts/yy34MHJTa3Q94saxcVy5N/f4ZQESsAn4H\nTEmXX8x6lCRaWlpoaWlxoGFWh+r+G0QlbRoRL6X094GHIuKiGndrvUnaFpgZEbvUui9FS5di5gGj\nIuKRCkXq+yC0XieJev/fZGZvUjXSr/XIRnecmKZRLiW71PGjWndofUk6AbgXaPovv0qXoP4A3FYl\n0DB7Ez8bxay51P3Ihm0QfBCamTW+hh7ZMDMzswbmYMPMzMwK5WDDzMzMCuVgw8zMzArlYMPM6s7E\niRNr3QUz60GejWL1wAehdeDv2TBrSJ6NYmZmZrXhYMPMzMwK5WDDzMzMCuVgw8zMzArlYMPM6o6f\njWLWXDwbxeqBD0Izs8bn2ShmZmZWGw42zMzMrFAONszMzKxQDjbMzMysUA42zKzu+NkoZs3Fs1Gs\nHvggtA78bBSzhuTZKGZmZlYbDjbMzMysUA42zMzMrFAONszMzKxQDjbMrO742ShmzcWzUawe+CA0\nM2t8no1iZmZmteFgw8zMzArlYMPMzMwK5WDDzMzMCuVgw8zqjp+NYtZcPBvF6oEPQuvAz0Yxa0ie\njWJmZma14WDDzMzMCuVgw8zMzArlYMPMzMwK5WDDzOqOn41i1lx6ZDaKpHHAiIj4oqSTgJcj4mfr\nXfEb9R8EvBYR96T3Pd5GI5B0V0S0StoBOCAirq2DPv0aODYiXliPajztwMys8VWdjdK3p1uKiB/1\ndJ3AB4GVwD0FtlH3IqI1Jd8FHAf0SrAhaaOIWFOlT4f3Rh+s90QEixcvBmDYsGFIVf9/mJl1S5eX\nUSTdIGmupCWSTszlf1LSg5LuAw7I5U+U9OWUniVpREpvKWlZSo+TNE3SbyU9JOnC3PojJc2TtFDS\njPQp/iTgS5IWSHp/qQ1Ju6T2S+sOkdSe0iNS+3Ml3SJp6wrbtlXavoXptV/K/w9Ji9Pr1Fzd/yfp\nirTdV0s6TNJdaRvem9v+qyTdIWm5pKMlfVNSe9revqncckmDUnofSTNz6/9E0kxJj0j6Yq6/L6bk\nBcCBaX+cJmm2pD1z5dokDSvb1j0k3ZfWWSRpp5T/8Vz+JZI2KrWV+r0Q+G9JU3J1HSzp5grbcUKq\ne6Gkn6a8wZKukzQnvQ7A6lZEMGbMeFpbl9DauoSxY8/y912Y2fqLiE5fwObpZ39gMbA5sA3wGLAF\nsDHQBlycyk0A/iOlZwJ7p/SWwLKUHgc8AgwANgGWA9sBg4E/AjukcpuV11mhjQXAkJQ+AziTbMTm\nbmCLlD8WuLzCtk0GTklpAQOBEUB72t5NgSXAXsAQYBWwRyo7t1Qn8BHghpSeCNwB9AFagJeB/5eW\nTQOOTOllwKCU3geYmVu/Le3XLYC/An3SspXp50HAzbntOAH4TkrvDNxfYVsvBo5L6b5AP2A34KZc\n/T8Ajk/pNcColO6Tft/90/sf5upaBgxK++XB3DaVfnfXAK0pvT3w+wrHma0jaMyXmTWlqrFEdy6j\nnCrpqJR+RzqZbQPMiohnACRNTvlr4/aIWJnW/z3ZyXwQcEdEPAYQEc/lypeP5ZbeTyELJi4ExqTX\nrmQnv9vSEHAf4IkKffgg8PHUVgAvSHo/MC0i/p76Ng04kOykvCwilqb8pcBtqZ4lqf+Q3X/w24hY\nLWkJsFFETE/LFgM7dLFfAvh1RKwCnpG0AtiqrP/l++I64GxJpwOfAq6oUO89wHhJ70jb97CkQ8iC\nq7lpP/UHnkzlVwPXp32zWtItwEckXQ/8K/CVsv78MzAlIv6W1in97v4F2C03FD9A0lsj4uUu9kOv\n89WC3lOv+9qDOGbF6DTYkHQwcAiwX0S8kob6+/HmG/qq/ev4B29cqulXtuzVXHp16su6/KlPBqam\noCAi4pF0CWFpRHRnyL6871GWp1y/8n1eA7yWS+f35WtknVkjaVXZOqVyne2b13Lp0r6pKiJeljQD\nOAoYDexdocy1ku4FjgB+o+wmW4CrIuLMCtW+kgKwkl8AJwN/A+ZGxEvlTVD5OBDwvoh4rcKyuuIT\nzRuXUaZPHwrAyJFLmTz53F6/b2PixIl+PopZE+nqno2BwLMp0NgV2I/spHIfcJCkQZI2JjvB5f9V\nl/4zLSe7RAAwqou2ArgX+ICkIQClewHIbg4dUHGliEfJTshnk50QIRvOH5y7B2NjSbtXWP124HOp\nTB9JA4E7gaMk9Ze0KdkJ/E6qB1TluipXad8csxbrQ+X9cRnZpZI5EfH8mxqV3hURyyJiEnAjMIxs\n+0dJGpzKDJK0fZU2Z5MFMSfy5htTA/gdMDp3/8bmadmtwCm5fuzVje2zGpHElCnn0dY2lLa2oTUJ\nNADOOeecXm/TzIrTVbBxC9A3Xeb4Om/MBnmS7N6Ce8juL1hatl4p8Pgm8DlJ88nuP4jc8jd9joyI\nvwKfAaalGxNLJ7WbgY9Kmp8uc1C2/mTgY2SXVEifokcBF6Z6FgD7V9i+U4EPKrupdC6wW0QsAK4E\n5pAFPz+OiEUV2ix/X23bqq1zDnCRpPvJRjk63Tdl6y4CVqcbMU9N2zwfeJ7Kl1AAxii7yXcB2SWm\nn0bEA8BZwK2SFpEFBqUbaTv0IbLZKL8CRqafHfoUEb8HzgNmp33+rbT8FGCfdOPoUrLfr9UxSbS0\ntNDS0uKZKGbWI3r8qa+SJpENs1/VoxVbpyRtS3aT6S617ss68AUM60B+6qtZI+qdp75K+hrwXrKb\nKa2XSDqBbBSm0r0XZmZmNdXjIxtm68AHoXXgkQ2zhtQ7IxtmZj3Bz0Yxay4e2bB64IPQzKzxeWTD\nzMzMasPBhpmZmRXKwYaZmZkVysGGmZmZFcrBhpnVHT8Xxay5eDaK1QMfhNaBv2fDrCF5NoqZmZnV\nhoMNMzMzK5SDDTMzMyuUgw0zMzMrlIMNM6s7fjaKWXPxbBSrBz4Izcwan2ejmJmZWW042DAzM7NC\nOdgwMzOzQjnYMDMzs0I52DCzuuNno5g1F89GsXrgg9A68LNRzBqSZ6OYmZlZbTjYMDMzs0I52DAz\nM7NCOdgwMzOzQjnYMLO642ejmDUXz0axeuCD0Mys8Xk2ipmZmdWGgw0zMzMrlIMNMzMzK5SDDTMz\nMyuUgw0zqzt+NopZc/FsFKsHPgitAz8bxawheTaKmZmZ1YaDDTMzMyuUgw0zMzMrlIONOiBpnKRJ\n67jumWXv7+qZXr2pneWSBvVAPeu8rWZm1pgcbNSH9bkT7r87VBTRup59qSbo5OaftazH1lNE0N7e\nTnt7e1PeSOlno5g1FwcbBZH0cUn3SVog6RJJG6X8kZLmSVooaUapeG69wZKukzQnvQ5I+W+TdIWk\ndkmLJB0t6etA/9TGz1K5F3N1nZHKL0xly/s4WtLitHx2yusj6Zspf5GkL+RW+WLqe7ukXVL5QZJ+\nmcreI2lYZ/m2/iKCMWPG09q6hNbWJYwde1bTBRye+mrWXPrWugPNSNJuwBjggIhYLekHwMck3QJc\nChwYEY9J2iytkj9TXAR8JyLukrQ9cAuwO3A28GxEtKQ2NouIaZJOjojhufUjLf8Q8BFg34h4RdLm\nFbp6NnBYRPxF0sCU9xlge2DPiFhTtt7TETFC0ueArwAnAucA8yLiKEkfBH4KDO8kvydGRxqSemzL\nBZz/+rupU7NXT2myuMXM6oCDjWIcAowA5io7w/QDngTeB9wREY8BRMRzFdb9F2A3vXFmGiBp01Tn\n2FJmlXXL6/lJRLySyj9bocxdwFWSpgDTcn3/YUSsqbBeqcx84OiUbi2lI2KmpC0kDegkvxA9dyK3\nWu5LBzpmzcnBRnGuiojymzeP6MZ6At4XEa+VrVta1l1d3mMREZ+TtC9wODBP0ohcHyp5Nf1cTcdj\np1r58vzCTiUb0kmqdBll+vShAIwcuZTJk89FjrjMrE75no1i3A6MkjQYXr9/YXvgXuADkoaU8lP5\n/FniVuCU0htJe6bkDOALufzSJZhVkioFjTOAT0rqn8q/6TKKpJ0iYk5ETACeBt6Z1jtJUp9q65W5\nE/hYKnsw2aWWlVXyX6xSh60FSUyZch5tbUNpaxvqQMPM6p6DjQJExAPAWcCtkhaRBRBbR8Rfye6J\nmCZpIXBtaRXe+NR/CrBPurFyKXBSyj8X2Lx0QydwcMq/FGgv3SBaqicipgM3kV3KWQB8uUJX/zfd\n7LkYuCsiFgGXAX9MdS4Ejq20ibn+TgRGpO08H/hEF/n5dW0dSaKlpYWWlpamDDR8g6hZc/GzUawe\n+CC0DvxsFLOG5GejmJmZWW042DAzM7NCOdgwMzOzQjnYMDMzs0I52DCzuuNno5g1F89GsXrgg9DM\nrPF5NoqZmZnVhoMNMzMzK5SDDTMzMyuUgw0zMzMrlIMNM6s7fjaKWXPxbBSrBz4IrQM/G8WsIXk2\nipmZmdWGgw0zMzMrlIMNMzMzK5SDDTMzMyuUgw0zqzt+NopZc/FsFKsHPgjNzBqfZ6OYmZlZbTjY\nMDMzs0I52DAzM7NCOdgwMzOzQjnYMLO642ejmDUXz0axeuCD0Drws1HMGpJno5iZmVltONgwMzOz\nQjnYMDMzs0I52DAzM7NCOdgws7rjZ6OYNRfPRrF64IPQzKzxeTaKmZmZ1YaDDTMzMyuUgw0zMzMr\nlIMNMzMzK5SDDTOrO342illz8WwUqwc+CK0DPxvFrCF5NkpvkzRE0uIC6z9Y0s0p/WFJZxTVlpmZ\n2froW+sO2PqLiJuBm2vdj3KS+kbEP2rdDzMzqy2PbBSrr6SfS/q9pKmS+gNIOlvSHEmLJf2oVFjS\nKZKWSlok6dqUt6mkn0i6T9J8SR8pb0TSOEmTUvpKSRdJukvSI5KOyZU7PbW7SNLECvX0SesvltQu\n6bSU/25Jt0laKGmepHel/G/kyo5JeQdLulPSjcASSRulcqV2P9OTO7jZRQTt7e20t7f7soKZNSwH\nG8XaBfh+ROwOvAB8PuV/LyL2jYhhQH9JR6T8M4C9ImJP4KSUNx64PSLeB/wz8A1Jb+2i3a0johU4\nArgAQNJhwLsjYl9gODBC0oFl6+0FbBsRwyKiBfhJyr8amBQRewH7A0+mIGZPoAX4l9SvrVP54cAp\nEbEr8O/Ac6ndfYETJQ3pov9GFmiMGTOe1tYltLYuYezYsxxwmFlD8mWUYj0eEfek9M+BU4BvAf8s\n6XTgrcAgYAnwK6AduEbSL4FfpvUOAz4s6Svp/SbAOztpM0rrRsQDkrbK1XOYpAXp/abAu4E7c+s+\nAuwo6WLg18CtkgaQBSA3pjpfA5DUClwT2dlvhaTZwHvJgqo5EfFYrt1hkkal9wNTu8s72YYNiqre\nUiXg/NffTZ2avTrTLLGIn41i1lwcbBQr/69fQEjaBPgBsHdE/FnSBKB/KnM48AHgw8B4ScNS/tER\n8Yd8xZK26aTd18raLfl6RFxatbMRz0lqAUYCnwXGAKd20k75abK0vS+V5Z8cETM6qafhVQ8Yetf6\n9KOeAhVPfTVrLr6MUqztJe2X0seRjSL0IzspPyPpbcBosiBEwPYRMQv4L+DtwNuA6WQjIgBIGr6O\nfZkOfErSpqme7SQNzheQtAXQNyKmAWcDwyPiReBPko5MZTZJ957cCYxN92QMJguS5vDmAGQ68HlJ\nfdP6O3fjMlDDiej515o1wahRZzJgwDUMGHANo0ePZ82aKKStego0zKz5eGSjOAE8CHxB0k+ApcAP\nI+IVST8mu3TyJHBfKt8H+Jmkt5OdsC+KiOclfQ34rqR2suDwUeAjuTZKP/OnizelI2KGpN2Ae7K4\nhpXAx4Gnc2W3A66QVApC/yv9PB74kaT/AVYBoyLiBkn7A4tSG6dHxIrURr79y4AhwPwUUK0APtr1\n7jNJTJlyHosXZzOohw07FtXLEIqZ2Vrwl3pZPfBBaGbW+PylXmZmZlYbDjbMrO74BlGz5uLLKFYP\nfBBaB342illD8mUUMzMzqw0HG2ZmZlYoBxtmZmZWKAcbZmZmVigHG2ZWd/xsFLPm4tkoVg98EJqZ\nNT7PRjEzM7PacLBhZmZmhXKwYWZmZoVysGFmZmaFcrBhZnXHz0Yxay6ejWL1wAehdeBno5g1JM9G\nMVtbs2bNqnUXzHqdj/vaaeZ972DDrIpm/sM3q8bHfe008753sGFmZmaFcrBhZmZmhfINolZzknwQ\nmpk1gYioeJOogw0zMzMrlC+jmJmZWaEcbJiZmVmhHGyYVSFpoqQ/SVqQXiNr3admJ2mkpP+T9AdJ\nZ9S6PxsSScsltadjfU6t+9PMJP1E0lOSFufyBkmaIekhSbdK2qyWfexpDjbMqgvg2xExPL1uqXWH\nmpmkPsD3gJHA7sCxknarba82KAEcnI71fWvdmSZ3BdlxnvdfwIyI2Bm4Pb1vGg42zDpX9et3rcft\nCzwcEcsjYhXwC+DIGvdpQ+PjvRdExJ3As2XZHwGuSumrgKN6tVMFc7Bh1rkvSlok6fJmG9asQ9sB\nj+fe/ynlWe8I4DZJcyWdWOvObIC2ioinUvopYKtadqanOdiwDVq6Rrq4wusjwA+BdwF7AX8BvlXT\nzjY/z8OvrdaIGA58CPiCpANr3aENVWTfSdFUfw99a90Bs1qKiEO7U07SZcDNBXdnQ/dn4J259+8k\nG92wXhARf0k/n5Z0A9llrTtr26sNylOSto6IJyVtA6yodYd6kkc2zKpIf/AlHwUWVytrPWIu8B5J\nQyS9BRgL3FTjPm0QJL1V0oCU3hQ4DB/vve0m4BMp/QnglzXsS4/zyIZZdRdK2otsOHMZcFKN+9PU\nIuIfkk4GpgN9gMsj4oEad2tDsRVwgyTIzgtXR8Stte1S85J0LXAQsKWkx4GvAhcAUyR9GlgOjKld\nD3uev67czMzMCuXLKGZmZlYoBxtmZmZWKAcbZmZmVigHG2ZmZlYoBxtmZmZWKAcbZmZmVigHG2Zm\nZlYoBxtmZmZWqP8Pvkb0R8EN4lgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1134dd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "pm.Matplot.summary_plot([M.beta, M.alpha_base], custom_labels=[\n",
    "                                     'high intensity eibi',\n",
    "                                     'low intensity eibi',\n",
    "                                     'low intensity parent training',\n",
    "                                     'defined school program',\n",
    "                                     'high intensity community',\n",
    "                                     'adjunctive community service',\n",
    "                                     'eclectic school',\n",
    "                                     'baseline score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
     ]
    },
    {
     "data": {
      "image/png": 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+DY1x3NWVej8hRrqMMn36UABGjlzK5MnnonWMGKT632Yz675GuGfjDuCodO/F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\nZlYoBxtmZmZWqP8Pvkb0R8EN4lgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113645410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "pm.Matplot.summary_plot([M.beta, M.alpha_base], custom_labels=[\n",
    "                                     'high intensity eibi',\n",
    "                                     'low intensity eibi',\n",
    "                                     'low intensity parent training',\n",
    "                                     'defined school program',\n",
    "                                     'high intensity community',\n",
    "                                     'adjunctive community service',\n",
    "                                     'eclectic school',\n",
    "                                     'baseline score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "β:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t5.958            2.183            0.18           [  2.053  10.541]\n",
      "\t2.05             2.967            0.247            [-4.518  7.394]\n",
      "\t-0.655           2.266            0.179            [-4.998  3.674]\n",
      "\t2.697            1.912            0.147            [-0.752  6.636]\n",
      "\t-3.506           4.536            0.408          [-11.78    6.385]\n",
      "\t-0.54            2.647            0.214            [-5.41   4.785]\n",
      "\t-5.635           2.187            0.179            [-9.755 -1.405]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t1.821            4.5             5.851          7.474         10.342\n",
      "\t-4.515           0.176           2.317          4.016         7.448\n",
      "\t-5.156           -2.17           -0.671         0.921         3.618\n",
      "\t-0.727           1.398           2.572          3.893         6.673\n",
      "\t-12.745          -6.515          -3.48          -0.53         5.726\n",
      "\t-5.718           -2.362          -0.419         1.188         4.598\n",
      "\t-10.176          -7.118          -5.519         -4.038        -1.711\n",
      "\t\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "M.β.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate posterior probability of best intervention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "best:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.779            0.415            0.026                  [ 0.  1.]\n",
      "\t0.104            0.305            0.018                  [ 0.  1.]\n",
      "\t0.0              0.017            0.0                    [ 0.  0.]\n",
      "\t0.108            0.31             0.022                  [ 0.  1.]\n",
      "\t0.006            0.078            0.003                  [ 0.  0.]\n",
      "\t0.004            0.06             0.002                  [ 0.  0.]\n",
      "\t0.0              0.0              0.0                    [ 0.  0.]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.0              1.0             1.0            1.0           1.0\n",
      "\t0.0              0.0             0.0            0.0           1.0\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t0.0              0.0             0.0            0.0           1.0\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/numpy/core/fromnumeric.py:225: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return reshape(newshape, order=order)\n"
     ]
    }
   ],
   "source": [
    "M.best.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting β_0\n",
      "Plotting β_1\n",
      "Plotting β_2\n",
      "Plotting β_3\n",
      "Plotting β_4\n",
      "Plotting β_5\n",
      "Plotting β_6\n"
     ]
    },
    {
     "data": {
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ga+BxjEm8nZXfW86lBLgO+Nmbz+MkhyzwxDmtUEotAPyAz0+v9EKcefZmlVBc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MS0adPo1KkTt99+O126pF8SJMxGfe3UXB0LqIwxxpiTfA+q3PyDZSLSz50+z1hNvakKCwuZPHly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\njDHGGOMDC6qMMcYYY3zw/+fnv0dyo9hyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afd2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NERju62WgbtnKzh9CiM8AW6S/lFJWznHiAdQ05ZYzJk2a5FK58ePHs2TJEmbN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\nGBMHFlQZY4wxxsTB/wfh+8isR+zCEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b435518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.Matplot.plot(M.β)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UFINJklQUg0mSVJT/D356Gzw49woJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e1e00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.Matplot.summary_plot([M.μ], custom_labels=outcomes_MA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These posterior predictive plots illustrate the fit of the model. Each frame represents a study outcome, where the red line is the value of the observed mean outcome. The blue histograms are draws from the posterior predictive distribution of the model, so we look for cases where the red line is far outside the support of the simulated data as evidence for lack of fit. Here, the fit is reasonable across all study outcomes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py:516: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d8261d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aec0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e09ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd47ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd7e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b2682b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d543198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6245c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e99d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e9db240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eddb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f129128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4c72e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110293c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104deba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1105b3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ba1b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11104cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1113cdb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111611c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119801d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119ecbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d0f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fd2390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112733240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112759a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bb0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113016978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135d2d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1136c35c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113853b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d67a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114061828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142c7198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149715f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cf2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114d1e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150a9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11541fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11587b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115acdb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115fd8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11625dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11705e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171177b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117757c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117826048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117cac320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117e46908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184d65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184fbef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118bc2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e1d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119003f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194e0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1195cbd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11989df60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ddda20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a246b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a26ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.Matplot.gof_plot(M.d_sim.trace(), change_mean, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def post_pred_checks(simdata, trueval, round=3):\n",
    "    \"\"\"\n",
    "    Performs posterior predictive checks, returning the quantile of the observed data relative \n",
    "    to simulated.\n",
    "    \"\"\"\n",
    "\n",
    "    if isinstance(simdata, pm.Variable):\n",
    "        simdata = simdata.trace()\n",
    "\n",
    "    if np.ndim(trueval) == 1 and np.ndim(simdata == 2):\n",
    "        # Iterate over more than one set of data\n",
    "        return [post_pred_checks(simdata[:,i], trueval[i]) for i in range(len(trueval))]\n",
    "      \n",
    "    return (simdata > trueval).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ppp = post_pred_checks(M.d_sim.trace(), change_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a60bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(ppp, bins=8)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}