code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1cabeae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: trying URL 'https://cloud.r-project.org/bin/macosx/big-sur-arm64/contrib/4.4/lme4_1.1-35.5.tgz'\n",
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      "\n",
      "R[write to console]: downloaded 6.8 MB\n",
      "\n",
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      "R[write to console]: trying URL 'https://cloud.r-project.org/bin/macosx/big-sur-arm64/contrib/4.4/lmerTest_3.1-3.tgz'\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded binary packages are in\n",
      "\t/var/folders/rj/l30_wb7d3w7_tbx4gbz6lzzh0000gn/T//RtmpiJeFvb/downloaded_packages\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def load_csv_data(folder_name, file_name):\n",
    "    csv_path = os.path.join(folder_name, file_name+\".csv\")\n",
    "    return pd.read_csv(csv_path, encoding='latin-1')\n",
    "\n",
    "import rpy2 \n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects.vectors import StrVector\n",
    "\n",
    "packnames = ('lme4', 'lmerTest','ggpubr')\n",
    "\n",
    "utils = importr(\"utils\")\n",
    "utils.chooseCRANmirror(ind=1)\n",
    "utils.install_packages(StrVector(packnames))\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94c0b5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loading required package: Matrix\n",
       "\n",
       "Attaching package: ‘lmerTest’\n",
       "\n",
       "The following object is masked from ‘package:lme4’:\n",
       "\n",
       "    lmer\n",
       "\n",
       "The following object is masked from ‘package:stats’:\n",
       "\n",
       "    step\n",
       "\n",
       "Loading required package: ggplot2\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R \n",
    "library(lme4) \n",
    "library(lmerTest)\n",
    "library(ggpubr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae23706",
   "metadata": {},
   "source": [
    "### Marginal distinct idea counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e10d54f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_marginal_distinct = load_csv_data(\"data\",\"marginal_distinct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3852a007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.95, 3.2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.barplot(x=\"condition\", y=\"marginal_distinct_cnt\", data=df_marginal_distinct, errorbar=('ci', 95))\n",
    "ax.set(xlabel='Condition', ylabel='Marginal distinct idea count')\n",
    "plt.ylim(0.95,3.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7132da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: \n",
      "marginal_distinct_cnt ~ C(condition) + (1 | round) + (1 | network_size) +  \n",
      "    (1 | id)\n",
      "   Data: df_marginal_distinct\n",
      "\n",
      "REML criterion at convergence: 4773.8\n",
      "\n",
      "Scaled residuals: \n",
      "     Min       1Q   Median       3Q      Max \n",
      "-3.07143 -0.62879 -0.03928  0.61774  2.86515 \n",
      "\n",
      "Random effects:\n",
      " Groups       Name        Variance Std.Dev.\n",
      " id           (Intercept) 0.61991  0.7873  \n",
      " network_size (Intercept) 0.58177  0.7627  \n",
      " round        (Intercept) 0.03242  0.1800  \n",
      " Residual                 1.16502  1.0794  \n",
      "Number of obs: 1440, groups:  id, 360; network_size, 18; round, 4\n",
      "\n",
      "Fixed effects:\n",
      "                      Estimate Std. Error       df t value Pr(>|t|)    \n",
      "(Intercept)             2.4944     0.2133  21.1276  11.696 1.08e-10 ***\n",
      "C(condition)treatment   0.4569     0.1006 340.9978   4.541 7.76e-06 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "C(cndtn)trt -0.236\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_marginal_distinct \n",
    "\n",
    "df_marginal_distinct$condition = factor(df_marginal_distinct$condition) \n",
    "df_marginal_distinct$id = factor(df_marginal_distinct$id) \n",
    "\n",
    "mart = lmer(marginal_distinct_cnt ~ C(condition) + (1|round)+ (1|network_size) + (1|id), data=df_marginal_distinct) \n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fd67518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: marginal_distinct_cnt ~ C(condition) + round + network_size +  \n",
      "    (1 | id)\n",
      "   Data: df_marginal_distinct\n",
      "\n",
      "REML criterion at convergence: 4786.5\n",
      "\n",
      "Scaled residuals: \n",
      "     Min       1Q   Median       3Q      Max \n",
      "-2.83429 -0.64293 -0.04809  0.64518  2.93777 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance Std.Dev.\n",
      " id       (Intercept) 0.6801   0.8247  \n",
      " Residual             1.1956   1.0934  \n",
      "Number of obs: 1440, groups:  id, 360\n",
      "\n",
      "Fixed effects:\n",
      "                        Estimate Std. Error         df t value Pr(>|t|)    \n",
      "(Intercept)              3.62057    0.15064  786.33287  24.034  < 2e-16 ***\n",
      "C(condition)treatment    0.45694    0.10430  356.99998   4.381 1.55e-05 ***\n",
      "round                    0.04250    0.02577 1079.00002   1.649   0.0994 .  \n",
      "network_size            -0.13420    0.01005  356.99998 -13.351  < 2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr) C(cnd) round \n",
      "C(cndtn)trt -0.346              \n",
      "round       -0.599  0.000       \n",
      "network_siz -0.634  0.000  0.000\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_marginal_distinct \n",
    "\n",
    "df_marginal_distinct$condition = factor(df_marginal_distinct$condition)\n",
    "df_marginal_distinct$id = factor(df_marginal_distinct$id) \n",
    "\n",
    "mart = lmer(marginal_distinct_cnt ~ C(condition) + round+ network_size + (1|id), data=df_marginal_distinct) \n",
    "print(summary(mart)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b237ba",
   "metadata": {},
   "source": [
    "### Non-redundant Idea Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a223203d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_nonredun = load_csv_data(\"data\",\"nonredun\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53723edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Condition'), Text(0, 0.5, 'Non-redundant idea count')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.barplot(x=\"condition\", y=\"nonredun_cnt\", data=df_nonredun, errorbar=('ci', 95))\n",
    "ax.set(xlabel='Condition', ylabel='Non-redundant idea count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1ed1a08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: nonredun_cnt ~ condition + (1 | round) + (1 | id)\n",
      "   Data: df_nonredun\n",
      "\n",
      "REML criterion at convergence: 4706.3\n",
      "\n",
      "Scaled residuals: \n",
      "     Min       1Q   Median       3Q      Max \n",
      "-2.34580 -0.65198 -0.05188  0.62559  3.10335 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance Std.Dev.\n",
      " id       (Intercept) 0.63233  0.7952  \n",
      " round    (Intercept) 0.03694  0.1922  \n",
      " Residual             1.13782  1.0667  \n",
      "Number of obs: 1440, groups:  id, 360; round, 4\n",
      "\n",
      "Fixed effects:\n",
      "                   Estimate Std. Error       df t value Pr(>|t|)    \n",
      "(Intercept)          1.7000     0.1197   6.1142  14.201 6.53e-06 ***\n",
      "conditiontreatment   0.3958     0.1009 358.0001   3.922 0.000105 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtntrtmnt -0.422\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_nonredun \n",
    "\n",
    "df_nonredun$condition = factor(df_nonredun$condition) \n",
    "df_nonredun$id = factor(df_nonredun$id) \n",
    "df_nonredun$round = factor(df_nonredun$round) \n",
    "\n",
    "mart = lmer(nonredun_cnt ~ condition + (1|round) + (1|id), data=df_nonredun) \n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e38f60",
   "metadata": {},
   "source": [
    "### Collective distinct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b58aeb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groupwise_distinct = load_csv_data(\"data\",\"groupwise_distinct\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2aa517ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: groupwise_distinct_cnt ~ condition + (1 | round) + (1 | trial_id)\n",
      "   Data: df_groupwise_distinct\n",
      "\n",
      "REML criterion at convergence: 565.3\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-2.5644 -0.6140  0.1415  0.6062  2.2398 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance Std.Dev.\n",
      " trial_id (Intercept) 16.808   4.100   \n",
      " round    (Intercept)  5.813   2.411   \n",
      " Residual             62.984   7.936   \n",
      "Number of obs: 80, groups:  trial_id, 10; round, 4\n",
      "\n",
      "Fixed effects:\n",
      "                   Estimate Std. Error     df t value Pr(>|t|)    \n",
      "(Intercept)          55.225      2.170  9.434  25.450 5.16e-10 ***\n",
      "conditiontreatment    8.700      1.775 66.000   4.903 6.47e-06 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtntrtmnt -0.409\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_groupwise_distinct \n",
    "\n",
    "df_groupwise_distinct$condition = factor(df_groupwise_distinct$condition) # convert to nominal factor\n",
    "df_groupwise_distinct$trial_id = factor(df_groupwise_distinct$trial_id)\n",
    "\n",
    "mart = lmer(groupwise_distinct_cnt ~ condition + (1|round) + (1|trial_id), data=df_groupwise_distinct) # uses LMM\n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c316c023",
   "metadata": {},
   "source": [
    "### CQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68a36f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cq = load_csv_data(\"data\",\"cq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9107ead0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0, 8.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.barplot(x=\"condition\", y=\"cq\", data=df_cq, errorbar=('ci', 95))\n",
    "ax.set(xlabel='Condition', ylabel='Creativity Quotient')\n",
    "plt.ylim(3,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bfba8f15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: cq ~ condition + (1 | round) + (1 | id)\n",
      "   Data: df_cq\n",
      "\n",
      "REML criterion at convergence: 6373.7\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-5.3557 -0.5556 -0.0608  0.5005  4.2520 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance Std.Dev.\n",
      " id       (Intercept) 4.20140  2.050   \n",
      " round    (Intercept) 0.09485  0.308   \n",
      " Residual             3.04456  1.745   \n",
      "Number of obs: 1440, groups:  id, 360; round, 4\n",
      "\n",
      "Fixed effects:\n",
      "                   Estimate Std. Error       df t value Pr(>|t|)    \n",
      "(Intercept)          6.5205     0.2265  11.7161   28.79 3.08e-12 ***\n",
      "conditiontreatment   0.6998     0.2348 357.9999    2.98  0.00308 ** \n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtntrtmnt -0.518\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_cq \n",
    "\n",
    "df_cq$condition = factor(df_cq$condition) # convert to nominal factor\n",
    "df_cq$id = factor(df_cq$id) \n",
    "df_cq$round = factor(df_cq$round) \n",
    "\n",
    "mart = lmer(cq ~ condition + (1|round) + (1|id), data=df_cq) # uses LMM\n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2088b444",
   "metadata": {},
   "source": [
    "### Max(SemDis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0171b9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_semdis = load_csv_data(\"data\",\"semdis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66dbef4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.98, 1.08)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.barplot(x=\"condition\", y=\"semdis\", data=df_semdis, errorbar=('ci', 95))\n",
    "ax.set(xlabel='Condition', ylabel='Maximum SemDis score')\n",
    "plt.ylim(0.98,1.08)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76896fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: semdis ~ condition + (1 | round) + (1 | id)\n",
      "   Data: df_semdis\n",
      "\n",
      "REML criterion at convergence: -3035.7\n",
      "\n",
      "Scaled residuals: \n",
      "     Min       1Q   Median       3Q      Max \n",
      "-12.2542  -0.4743  -0.0179   0.4930   3.1156 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance  Std.Dev.\n",
      " id       (Intercept) 0.0008605 0.02933 \n",
      " round    (Intercept) 0.0002492 0.01579 \n",
      " Residual             0.0062648 0.07915 \n",
      "Number of obs: 1440, groups:  id, 360; round, 4\n",
      "\n",
      "Fixed effects:\n",
      "                    Estimate Std. Error        df t value Pr(>|t|)    \n",
      "(Intercept)        1.052e+00  8.705e-03 3.877e+00 120.824 4.45e-08 ***\n",
      "conditiontreatment 1.220e-02  5.193e-03 3.580e+02   2.349   0.0193 *  \n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtntrtmnt -0.298\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_semdis \n",
    "\n",
    "df_semdis$condition = factor(df_semdis$condition)\n",
    "\n",
    "mart = lmer(semdis ~ condition + (1|round) + (1|id), data=df_semdis)\n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd84e01",
   "metadata": {},
   "source": [
    "### Gini analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ef48ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gini = load_csv_data(\"data\",\"gini\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a8528651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Condition'), Text(0, 0.5, 'Gini Coefficient')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.barplot(x=\"condition\", y=\"gini\", data=df_gini, errorbar=('ci', 95))\n",
    "ax.set(xlabel='Condition', ylabel='Gini Coefficient')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "086f7099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: gini ~ condition + (1 | round) + (1 | trial_id)\n",
      "   Data: df_gini\n",
      "\n",
      "REML criterion at convergence: -257.9\n",
      "\n",
      "Scaled residuals: \n",
      "     Min       1Q   Median       3Q      Max \n",
      "-2.91861 -0.52278 -0.04951  0.55039  3.14754 \n",
      "\n",
      "Random effects:\n",
      " Groups   Name        Variance Std.Dev.\n",
      " trial_id (Intercept) 0.001969 0.04437 \n",
      " round    (Intercept) 0.000000 0.00000 \n",
      " Residual             0.003252 0.05703 \n",
      "Number of obs: 100, groups:  trial_id, 10; round, 5\n",
      "\n",
      "Fixed effects:\n",
      "                   Estimate Std. Error       df t value Pr(>|t|)    \n",
      "(Intercept)         0.32474    0.01618 11.70923  20.066 1.98e-10 ***\n",
      "conditiontreatment -0.07270    0.01141 89.00000  -6.374 7.95e-09 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtntrtmnt -0.352\n",
      "optimizer (nloptwrap) convergence code: 0 (OK)\n",
      "boundary (singular) fit: see help('isSingular')\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "boundary (singular) fit: see help('isSingular')\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df_gini \n",
    "\n",
    "df_gini$condition = factor(df_gini$condition) # convert to nominal factor\n",
    "df_gini$trial_id = factor(df_gini$trial_id)\n",
    "df_gini$round = factor(df_gini$round)\n",
    "\n",
    "mart = lmer(gini ~ condition + (1|round)+ (1|trial_id), data=df_gini) # uses LMM\n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bf775a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gini_networksize = load_csv_data(\"data\",\"gini_networksize\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "636fe5e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Network Size'), Text(0, 0.5, 'Gini Coefficient')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = sns.lineplot(data=df_gini_networksize, x=\"networksize\", y=\"gini\", hue=\"condition\")\n",
    "ax.set(xlabel='Network Size', ylabel='Gini Coefficient')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6320325b",
   "metadata": {},
   "source": [
    "### Percentage of recommendations dominated by semantic features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54b8886b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_explanation = load_csv_data(\"data\",\"frac_sem\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0718469c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`geom_smooth()` using formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i df_explanation \n",
    "\n",
    "ggplot(data=df_explanation, aes(x=network_size, y=frac_semantic))+\n",
    "geom_point(alpha = 0.25)+\n",
    "geom_smooth(method=loess, se=TRUE) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25c3b7c",
   "metadata": {},
   "source": [
    "### Recommendation affects rating behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "742c459e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = load_csv_data(\"data\",\"ratings\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab7b59c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear mixed model fit by REML. t-tests use Satterthwaite's method [\n",
      "lmerModLmerTest]\n",
      "Formula: rating ~ condition + (1 | idea_id) + (1 | from_ego_id) + (1 |  \n",
      "    for_alter_id)\n",
      "   Data: df_ratings\n",
      "\n",
      "REML criterion at convergence: 93402.8\n",
      "\n",
      "Scaled residuals: \n",
      "    Min      1Q  Median      3Q     Max \n",
      "-3.4797 -0.6559 -0.0021  0.6851  3.4690 \n",
      "\n",
      "Random effects:\n",
      " Groups       Name        Variance Std.Dev.\n",
      " idea_id      (Intercept) 0.06206  0.2491  \n",
      " from_ego_id  (Intercept) 0.48538  0.6967  \n",
      " for_alter_id (Intercept) 0.02787  0.1670  \n",
      " Residual                 1.02711  1.0135  \n",
      "Number of obs: 31836, groups:  \n",
      "idea_id, 1759; from_ego_id, 180; for_alter_id, 60\n",
      "\n",
      "Fixed effects:\n",
      "                      Estimate Std. Error        df t value Pr(>|t|)    \n",
      "(Intercept)          3.181e+00  5.698e-02 2.221e+02  55.829  < 2e-16 ***\n",
      "conditionrecommended 4.168e-02  1.266e-02 3.162e+04   3.294 0.000989 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Correlation of Fixed Effects:\n",
      "            (Intr)\n",
      "cndtnrcmmnd -0.074\n"
     ]
    }
   ],
   "source": [
    "%%R -i df_ratings \n",
    "\n",
    "df_ratings$condition = factor(df_ratings$condition)\n",
    "df_ratings$idea_id = factor(df_ratings$idea_id)\n",
    "df_ratings$for_alter_id = factor(df_ratings$for_alter_id)\n",
    "df_ratings$from_ego_id = factor(df_ratings$from_ego_id)\n",
    "\n",
    "mart = lmer(rating ~ condition+(1|idea_id)+(1|from_ego_id)+(1|for_alter_id), data=df_ratings) \n",
    "print(summary(mart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "657fc8b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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