{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOWNAG6IZoh+ik4rqgeMAZj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"YMifluhW2rZp","executionInfo":{"status":"ok","timestamp":1781100698726,"user_tz":-120,"elapsed":1341,"user":{"displayName":"Andreas van Cranenburgh","userId":"13143063654677287265"}}},"outputs":[],"source":["import numpy as np\n","from scipy.stats import chi2_contingency\n","from statsmodels.stats.multitest import multipletests\n","import pandas as pd\n","\n","def corpus_stats(word, counts_recent, counts_reference, total_recent, total_reference):\n","    \"\"\"\n","    word             : the word being tested\n","    counts_recent    : raw count in week 5\n","    counts_reference : raw count in weeks 1-4\n","    total_recent     : total tokens in week 5\n","    total_reference  : total tokens in weeks 1-4\n","    \"\"\"\n","    a = counts_recent      # word in recent\n","    b = counts_reference   # word in reference\n","    c = total_recent - a   # non-word in recent\n","    d = total_reference - b  # non-word in reference\n","\n","    contingency = np.array([[a, b],\n","                             [c, d]])\n","\n","    # --- Chi-Squared ---\n","    chi2_stat, p_chi2, _, _ = chi2_contingency(contingency, correction=False)\n","\n","    # --- Log-Likelihood (G²) ---\n","    # G² = 2 * sum(observed * log(observed / expected))\n","    # scipy's chi2_contingency with lambda_=\"log-likelihood\" computes this\n","    g2_stat, p_g2, _, _ = chi2_contingency(contingency, lambda_=\"log-likelihood\")\n","\n","    # --- Effect sizes ---\n","    freq_recent    = a / total_recent\n","    freq_reference = b / total_reference\n","\n","    pct_diff = (freq_recent - freq_reference) / freq_reference * 100\n","\n","    # Avoid log(0) with a small epsilon\n","    eps = 1e-9\n","    log_ratio = np.log2((freq_recent + eps) / (freq_reference + eps))\n","\n","    return {\n","        \"word\":           word,\n","        \"freq_recent\":    freq_recent,\n","        \"freq_reference\": freq_reference,\n","        \"pct_diff\":       pct_diff,\n","        \"log_ratio\":      log_ratio,\n","        \"chi2\":           chi2_stat,\n","        \"p_chi2\":         p_chi2,\n","        \"g2\":             g2_stat,\n","        \"p_g2\":           p_g2,\n","    }\n"]},{"cell_type":"code","source":["# Example data\n","counts_recent = {'eend': 150, 'tafel': 101, 'fiets': 102}\n","counts_reference = {'eend': 77, 'tafel': 100, 'fiets': 142}\n","total_recent = sum(counts_recent.values())\n","total_reference = sum(counts_reference.values())"],"metadata":{"id":"mHH718-222BM","executionInfo":{"status":"ok","timestamp":1781100880331,"user_tz":-120,"elapsed":38,"user":{"displayName":"Andreas van Cranenburgh","userId":"13143063654677287265"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["# Run tests on whole vocabulary, including correction for multiple tests\n","# (false discovery rate).\n","\n","results = [\n","    corpus_stats(word, counts_recent[word], counts_reference.get(word, 0),\n","                 total_recent, total_reference)\n","    for word in counts_recent]\n","\n","# FDR correction across all words\n","p_values = [r[\"p_g2\"] for r in results]\n","_, p_adjusted, _, _ = multipletests(p_values, method=\"fdr_bh\")\n","\n","for r, p_adj in zip(results, p_adjusted):\n","    r[\"p_g2_adjusted\"] = p_adj"],"metadata":{"id":"urBml1212wxb","executionInfo":{"status":"ok","timestamp":1781100881153,"user_tz":-120,"elapsed":7,"user":{"displayName":"Andreas van Cranenburgh","userId":"13143063654677287265"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["results = pd.DataFrame(results)\n","results"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":163},"id":"-y3MUOgI3PFn","executionInfo":{"status":"ok","timestamp":1781100882491,"user_tz":-120,"elapsed":12,"user":{"displayName":"Andreas van Cranenburgh","userId":"13143063654677287265"}},"outputId":"f9a90951-c8f2-45a7-8d8d-fb0e279dd5b3"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    word  freq_recent  freq_reference   pct_diff  log_ratio       chi2  \\\n","0   eend     0.424929        0.241379  76.042088   0.815920  25.238117   \n","1  tafel     0.286119        0.313480  -8.728045  -0.131756   0.598371   \n","2  fiets     0.288952        0.445141 -35.087579  -0.623434  17.676782   \n","\n","         p_chi2         g2          p_g2  p_g2_adjusted  \n","0  5.067080e-07  24.764140  6.479173e-07       0.000002  \n","1  4.392004e-01   0.474701  4.908322e-01       0.490832  \n","2  2.618028e-05  17.051468  3.638025e-05       0.000055  "],"text/html":["\n","  <div id=\"df-c9b2768a-e9e3-4204-a57c-b02ed4ef374e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>word</th>\n","      <th>freq_recent</th>\n","      <th>freq_reference</th>\n","      <th>pct_diff</th>\n","      <th>log_ratio</th>\n","      <th>chi2</th>\n","      <th>p_chi2</th>\n","      <th>g2</th>\n","      <th>p_g2</th>\n","      <th>p_g2_adjusted</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>eend</td>\n","      <td>0.424929</td>\n","      <td>0.241379</td>\n","      <td>76.042088</td>\n","      <td>0.815920</td>\n","      <td>25.238117</td>\n","      <td>5.067080e-07</td>\n","      <td>24.764140</td>\n","      <td>6.479173e-07</td>\n","      <td>0.000002</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>tafel</td>\n","      <td>0.286119</td>\n","      <td>0.313480</td>\n","      <td>-8.728045</td>\n","      <td>-0.131756</td>\n","      <td>0.598371</td>\n","      <td>4.392004e-01</td>\n","      <td>0.474701</td>\n","      <td>4.908322e-01</td>\n","      <td>0.490832</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>fiets</td>\n","      <td>0.288952</td>\n","      <td>0.445141</td>\n","      <td>-35.087579</td>\n","      <td>-0.623434</td>\n","      <td>17.676782</td>\n","      <td>2.618028e-05</td>\n","      <td>17.051468</td>\n","      <td>3.638025e-05</td>\n","      <td>0.000055</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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0.42492917847025496,\n          0.28611898016997167,\n          0.28895184135977336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq_reference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1033215397950522,\n        \"min\": 0.2413793103448276,\n        \"max\": 0.445141065830721,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.2413793103448276,\n          0.31347962382445144,\n          0.445141065830721\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pct_diff\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58.066920556065845,\n        \"min\": -35.08757930016359,\n        \"max\": 76.04208822339133,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          76.04208822339133,\n          -8.728045325779044,\n          -35.08757930016359\n 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0.05]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":132},"id":"nTpOtOka3ViF","executionInfo":{"status":"ok","timestamp":1781100883685,"user_tz":-120,"elapsed":65,"user":{"displayName":"Andreas van Cranenburgh","userId":"13143063654677287265"}},"outputId":"2430f959-eeb9-4670-da76-613406cbf473"},"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    word  freq_recent  freq_reference   pct_diff  log_ratio       chi2  \\\n","0   eend     0.424929        0.241379  76.042088   0.815920  25.238117   \n","2  fiets     0.288952        0.445141 -35.087579  -0.623434  17.676782   \n","\n","         p_chi2         g2          p_g2  p_g2_adjusted  \n","0  5.067080e-07  24.764140  6.479173e-07       0.000002  \n","2  2.618028e-05  17.051468  3.638025e-05       0.000055  "],"text/html":["\n","  <div id=\"df-07f90926-62ad-46b3-8cc2-b45a8005b40c\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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