{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Zn2L_FmCov_j" }, "source": [ "# TIME GENERALIZATION" ] }, { "cell_type": "markdown", "metadata": { "id": "wh_PfWGeov_n" }, "source": [ "#### Explanation of the Code\n", "\n", "In the following code, we perform the following tasks:
\n", "\n", "- Import necessary libraries.
\n", "- Load the dataset using pandas.
\n", "- Clean the data by removing missing values and unnecesary variables.
\n", "- Conduct statistical analysis using pingouin library.
\n", "\n", "You run each individual cell by Ctrl+Enter.
\n", "Throughout the code, you will have to adjust names of variables to match your experiment.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e_5UCThRov_o", "outputId": "4faaec23-96cc-4db5-be18-94b464ad0bfc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Pingouin not found. Installing...\n", "Collecting pingouin\n", " Downloading pingouin-0.5.5-py3-none-any.whl.metadata (19 kB)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pingouin) (3.8.0)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pingouin) (1.26.4)\n", "Requirement already satisfied: pandas>=1.5 in /usr/local/lib/python3.10/dist-packages (from pingouin) (2.2.2)\n", "Collecting pandas-flavor (from pingouin)\n", " Downloading pandas_flavor-0.6.0-py3-none-any.whl.metadata (6.3 kB)\n", "Requirement already satisfied: scikit-learn>=1.2 in /usr/local/lib/python3.10/dist-packages (from pingouin) (1.5.2)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pingouin) (1.13.1)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from pingouin) (0.13.2)\n", "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from pingouin) (0.14.4)\n", "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (from pingouin) (0.9.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5->pingouin) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5->pingouin) (2024.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.5->pingouin) (2024.2)\n", "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2->pingouin) (1.4.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2->pingouin) (3.5.0)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (1.3.1)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (4.55.1)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (1.4.7)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (24.2)\n", "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (11.0.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->pingouin) (3.2.0)\n", "Requirement already satisfied: xarray in /usr/local/lib/python3.10/dist-packages (from pandas-flavor->pingouin) (2024.10.0)\n", "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels->pingouin) (1.0.1)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=1.5->pingouin) (1.16.0)\n", "Downloading pingouin-0.5.5-py3-none-any.whl (204 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m204.4/204.4 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading pandas_flavor-0.6.0-py3-none-any.whl (7.2 kB)\n", "Installing collected packages: pandas-flavor, pingouin\n", "Successfully installed pandas-flavor-0.6.0 pingouin-0.5.5\n" ] } ], "source": [ "# Install required libraries if not already installed\n", "# run this the first time you run the code, then you can use the second cell instead\n", "try:\n", " import pandas as pd\n", "except ImportError:\n", " print(\"Pandas not found. Installing...\")\n", " !pip install pandas\n", " import pandas as pd\n", "\n", "try:\n", " import glob\n", "except ImportError:\n", " print(\"Glob not found. Installing...\")\n", " !pip install glob2\n", " import glob\n", "\n", "try:\n", " import seaborn as sns\n", "except ImportError:\n", " print(\"Seaborn not found. Installing...\")\n", " !pip install seaborn\n", " import seaborn as sns\n", "\n", "try:\n", " import pingouin as pg\n", "except ImportError:\n", " print(\"Pingouin not found. Installing...\")\n", " !pip install pingouin\n", " import pingouin as pg\n", "\n", "try:\n", " import matplotlib.pyplot as plt\n", "except ImportError:\n", " print(\"Matplotlib.pyplot not found. Installing...\")\n", " !pip install matplotlib\n", " import matplotlib.pyplot as plt\n", "\n", "try:\n", " import numpy as np\n", "except ImportError:\n", " print(\"Numpy not found. Installing...\")\n", " !pip install numpy\n", " import numpy as np\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T6DpQcckov_r" }, "outputs": [], "source": [ "#import required libraries\n", "\n", "import pandas as pd\n", "import glob\n", "import seaborn as sns\n", "import pingouin as pg\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "id": "DaNJLBZRov_r" }, "source": [ "## 1. CREATE DATA FRAME" ] }, { "cell_type": "markdown", "metadata": { "id": "cf-TlzE7ov_r" }, "source": [ "#### Explanation of the Code\n", "In this section:
\n", " - data will be loaded into one dataframe
\n", " - unnecessary lines and columns will be removed
\n", " - data will be reformated to match the needs of our statistical analysis\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Tv-4a6Phov_s" }, "source": [ "### collect logs into one file" ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "import pandas as pd\n", "\n", "# Upload files manually\n", "uploaded = files.upload()\n", "\n", "# Create DataFrame (for .csv or .xlsx)\n", "import io\n", "import os\n", "\n", "dataframes = []\n", "for filename in uploaded.keys():\n", " if filename.endswith(\".csv\"):\n", " df = pd.read_csv(io.BytesIO(uploaded[filename]))\n", " elif filename.endswith(\".xlsx\"):\n", " df = pd.read_excel(io.BytesIO(uploaded[filename]))\n", " dataframes.append(df)\n", "\n", "# Combine all DataFrames\n", "combined_df = pd.concat(dataframes, ignore_index=True)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 385 }, "id": "3jMIWxbLo5e-", "outputId": "c1e473a6-bcd2-493c-99a5-63c0921eeb75" }, "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving 003105_time_gen00_A01_2024-10-17_20h59.26.821.csv to 003105_time_gen00_A01_2024-10-17_20h59.26.821.csv\n", "Saving 028980_time_generalization_00_2024-10-07_19h35.15.027.csv to 028980_time_generalization_00_2024-10-07_19h35.15.027.csv\n", "Saving 053758_time_generalization_00_2024-10-13_18h42.42.986.csv to 053758_time_generalization_00_2024-10-13_18h42.42.986.csv\n", "Saving 089680_time_gen00_A01_2024-10-16_13h37.12.186.csv to 089680_time_gen00_A01_2024-10-16_13h37.12.186.csv\n", "Saving 094062_time_generalization_00_2024-10-04_14h34.17.750.xlsx to 094062_time_generalization_00_2024-10-04_14h34.17.750.xlsx\n", "Saving 094204_time_gen00_A01_2024-10-16_16h15.55.874.csv to 094204_time_gen00_A01_2024-10-16_16h15.55.874.csv\n", "Saving 113546_time_generalization_00_2024-10-13_14h15.48.566.csv to 113546_time_generalization_00_2024-10-13_14h15.48.566.csv\n", "Saving 165627_time_generalization_00_2024-10-09_18h04.49.168.xlsx to 165627_time_generalization_00_2024-10-09_18h04.49.168.xlsx\n", "Saving 182377_time_generalization_00_2024-10-13_12h02.00.757.csv to 182377_time_generalization_00_2024-10-13_12h02.00.757.csv\n", "Saving 191786_time_gen00_A01_2024-10-15_18h36.10.761.csv to 191786_time_gen00_A01_2024-10-15_18h36.10.761.csv\n" ] } ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 338 }, "id": "HnwNd78xov_t", "outputId": "539fb4cb-f01c-49c4-b043-730d1388e4fa" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " stress accuracy participant session age frameRate \\\n", "count 10.000000 10.000000 1468.000000 1468.0 1176.000000 1176.000000 \n", "mean 0.300000 0.700000 101674.250681 1.0 22.127551 66.694673 \n", "std 0.483046 0.483046 60406.951014 0.0 1.906510 31.380543 \n", "min 0.000000 0.000000 3105.000000 1.0 21.000000 29.411765 \n", "25% 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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe" } }, "metadata": {}, "execution_count": 6 } ], "source": [ "# Set display options to show more rows and columns\n", "pd.set_option('display.max_rows', None) # Set to None to display all rows\n", "pd.set_option('display.max_columns', None) # Set to None to display all columns\n", "\n", "# function .describe() generates descriptive statistics for each column\n", "combined_df.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "d4b6_Sghov_u" }, "outputs": [], "source": [ "### YOU MAY SKIP THIS STEP IF YOU DO NOT USE RANDOMIZER TO ASSIGN SUBJETS TO GROUPS\n", "\n", "# Fill missing 'stress' and 'accuracy' values within each participant group\n", "# (replace 'stress' and 'accuracy' by the names of your between factors assigned via randomizer in psychopy, if relevant)\n", "\n", "combined_df['stress'] = combined_df.groupby('participant')['stress'].transform('first')\n", "combined_df['accuracy'] = combined_df.groupby('participant')['accuracy'].transform('first')\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "Vc8MIYSTov_t", "outputId": "f5ee8d16-e2d3-46b9-9da2-ed80b74da0ea" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_b90c793c-0691-47bb-aea3-088d03ef8f4c\", \"combined_df24.csv\", 672442)" ] }, "metadata": {} } ], "source": [ "# save as csv (to back up or manually open in excel if needed)\n", "\n", "combined_df.to_csv(\"combined_df24.csv\")\n", "\n", "# Download the file\n", "files.download(\"combined_df24.csv\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VXdU6C-eov_u", "outputId": "590f8654-d2d5-479a-c0a5-d5e017490e1d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " duration corrAns feedback noFeedback Blocks.thisTrialN \\\n", "4 1.0 b 1.0 1.0 NaN \n", "5 0.4 n 1.0 1.0 NaN \n", "6 0.8 n 1.0 1.0 NaN \n", "7 1.2 n 1.0 1.0 NaN \n", "8 1.0 b 1.0 1.0 NaN \n", "9 0.6 n 1.0 1.0 NaN \n", "10 1.0 b 1.0 1.0 NaN \n", "11 1.0 b 1.0 1.0 NaN \n", "12 1.6 n 1.0 1.0 NaN \n", "13 1.4 n 1.0 1.0 NaN \n", "15 1.4 n 0.0 1.0 NaN \n", "16 1.0 b 0.0 1.0 NaN \n", "17 1.2 n 0.0 1.0 NaN \n", "18 0.4 n 0.0 1.0 NaN \n", "19 1.0 b 0.0 1.0 NaN \n", "20 1.6 n 0.0 1.0 NaN \n", "21 1.0 b 0.0 1.0 NaN \n", "22 0.6 n 0.0 1.0 NaN \n", "23 0.8 n 0.0 1.0 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29.411765 \n", "1442 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1443 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1444 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1445 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1446 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1447 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1448 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1449 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1451 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1452 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1453 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1454 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1455 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1456 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1457 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1458 2024-10-15_18h36.10.761 time_gen00_A01 29.411765 \n", "1459 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list it is the feedback/noFeedback and stress/accuracy)\n", "# We want to keep the record of durations and correct responses\n", "# trial or block numbers are recorded here as Blocks.thisTrialN and BlockThisTrialN, this will have different names too\n", "# i would keep the response times for te trials (key_respBorN.keys)\n", "# make sure you keep the participant data too (all the variables you ask your subject to fill in in the start of exp.)\n", "\n", "### you might perform this step in excel as well, on the comnined_df.csv you created in previous step\n", "\n", "selected_columns = [\n", " 'duration', 'corrAns', 'feedback', 'noFeedback',\n", " 'Blocks.thisTrialN', 'Block.thisTrialN',\n", " 'stress', 'accuracy', 'polygonBlackSquare.started',\n", " 'polygonBlackSquare.stopped', 'key_respBorN.keys',\n", " 'key_respBorN.corr', 'key_respBorN.rt', 'participant',\n", " 'session', 'gender', 'age', 'date', 'expName', 'frameRate'\n", "]\n", "\n", "# Creating a new DataFrame with selected columns\n", "new_df = combined_df[selected_columns]\n", "\n", "# Removing rows where 'duration' is not recorded (contains NaN) - to get rid of lines that were not part of the experiment\n", "new_df = new_df.dropna(subset=['duration'])\n", "\n", "# Display the new DataFrame\n", "print(new_df)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 320 }, "id": "3o1Lwy-Vov_u", "outputId": "773e8be8-5132-4658-806c-5e1c2d823925" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " duration feedback noFeedback Blocks.thisTrialN \\\n", "count 1300.000000 1300.000000 1300.000000 720.000000 \n", "mean 1.000000 0.538462 0.538462 2.500000 \n", "std 0.334793 0.498710 0.498710 1.709012 \n", "min 0.400000 0.000000 0.000000 0.000000 \n", "25% 0.800000 0.000000 0.000000 1.000000 \n", "50% 1.000000 1.000000 1.000000 2.500000 \n", "75% 1.200000 1.000000 1.000000 4.000000 \n", "max 1.600000 1.000000 1.000000 5.000000 \n", "\n", " Block.thisTrialN stress accuracy polygonBlackSquare.started \\\n", "count 1200.000000 1300.000000 1300.000000 780.000000 \n", "mean 4.500000 0.300000 0.700000 945.172965 \n", "std 2.873479 0.458434 0.458434 548.104237 \n", "min 0.000000 0.000000 0.000000 40.477526 \n", "25% 2.000000 0.000000 0.000000 517.157083 \n", "50% 4.500000 0.000000 1.000000 909.189956 \n", "75% 7.000000 1.000000 1.000000 1241.964606 \n", "max 9.000000 1.000000 1.000000 2265.776836 \n", "\n", " polygonBlackSquare.stopped key_respBorN.corr key_respBorN.rt \\\n", "count 758.000000 1300.000000 1300.000000 \n", "mean 938.123635 0.738462 2.116771 \n", "std 543.883611 0.439642 5.056605 \n", "min 41.470338 0.000000 -6.815403 \n", "25% 512.116400 0.000000 1.456822 \n", "50% 896.160819 1.000000 1.727881 \n", "75% 1237.199589 1.000000 2.036317 \n", "max 2252.765863 1.000000 153.627700 \n", "\n", " participant session age frameRate \n", "count 1300.000000 1300.0 1040.00000 1040.000000 \n", "mean 101712.500000 1.0 22.12500 66.793397 \n", "std 60370.444564 0.0 1.90075 31.447802 \n", "min 3105.000000 1.0 21.00000 29.411765 \n", "25% 53758.000000 1.0 21.00000 59.725775 \n", "50% 94133.000000 1.0 21.50000 59.880240 \n", "75% 165627.000000 1.0 22.00000 60.024233 \n", "max 191786.000000 1.0 27.00000 145.658149 " ], "text/html": [ "\n", "
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\"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Blocks.thisTrialN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 253.71944562357643,\n \"min\": 0.0,\n \"max\": 720.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 720.0,\n 2.5,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Block.thisTrialN\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 422.7645680740164,\n \"min\": 0.0,\n \"max\": 1200.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1200.0,\n 4.5,\n 7.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stress\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 459.4802744120792,\n \"min\": 0.0,\n \"max\": 1300.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.3,\n 1.0,\n 0.4584339241565275\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 459.40956634805013,\n \"min\": 0.0,\n \"max\": 1300.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.7,\n 1.0,\n 0.4584339241565275\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"polygonBlackSquare.started\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 655.493630636809,\n \"min\": 40.47752620908432,\n \"max\": 2265.776836,\n \"num_unique_values\": 8,\n \"samples\": [\n 945.172965040828,\n 909.1899555044947,\n 780.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"polygonBlackSquare.stopped\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 652.4246574611108,\n \"min\": 41.47033837507479,\n \"max\": 2252.765863299999,\n \"num_unique_values\": 8,\n \"samples\": [\n 938.1236350711071,\n 896.1608186334488,\n 758.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"key_respBorN.corr\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 459.4085752367766,\n \"min\": 0.0,\n \"max\": 1300.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.7384615384615385,\n 1.0,\n 0.4396416441500007\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"key_respBorN.rt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 454.74116702419695,\n \"min\": -6.815403459127992,\n \"max\": 1300.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 2.1167708045241542,\n 1.7278811869909987,\n 1300.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"participant\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 69233.99179435614,\n \"min\": 1300.0,\n \"max\": 191786.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 101712.5,\n 94133.0,\n 1300.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"session\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 459.3164953026865,\n \"min\": 0.0,\n \"max\": 1300.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 1300.0,\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 360.87666542768176,\n \"min\": 1.9007495608750002,\n \"max\": 1040.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 1040.0,\n 22.125,\n 22.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"frameRate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 346.67361182346076,\n \"min\": 29.41176471,\n \"max\": 1040.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 66.7933965849459,\n 59.88023952,\n 1040.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 10 } ], "source": [ "#see the descriptive statistics\n", "\n", "\"\"\"\n", "you might notice, that there is different count of lines for certain variables,\n", "that is because the current data frame still contains training data from the first 10 trials, which we will not use\n", "in analysis.\n", "\"\"\"\n", "\n", "new_df.describe()" ] }, { "cell_type": "code", "source": [ "'''\n", "the count of measurements for each participant has to be the same and equal to the number of all trials in your experiment.\n", "in mine, there were 10 training trials plus 120 trials.\n", "now we check, if all participants submitted complete experiments\n", "'''\n", "# Display unique value counts for the 'participant' column in new_df\n", "participant_counts = new_df['participant'].value_counts()\n", "\n", "# Show the counts\n", "print(participant_counts)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8PkMOZjG_op_", "outputId": "43ef483a-36dd-4752-937a-5abf6a054854" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "participant\n", "3105.0 130\n", "28980.0 130\n", "53758.0 130\n", "89680.0 130\n", "94062.0 130\n", "94204.0 130\n", "113546.0 130\n", "165627.0 130\n", "182377.0 130\n", "191786.0 130\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "# Get the participant IDs that have exactly 130 occurrences (10 training trials and 120 trials)\n", "participants_to_keep = new_df['participant'].value_counts()\n", "participants_to_keep = participants_to_keep[participants_to_keep == 130].index\n", "\n", "# Filter new_df to keep only rows with these participants\n", "filtered_df = new_df[new_df['participant'].isin(participants_to_keep)]\n", "\n", "# Display the resulting DataFrame\n", "print(filtered_df)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pEmD0EAy_zXm", "outputId": "8a56391a-0849-4b70-fbc4-5bb8d6e49ed9" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " duration corrAns feedback noFeedback Blocks.thisTrialN \\\n", "4 1.0 b 1.0 1.0 NaN \n", "5 0.4 n 1.0 1.0 NaN \n", "6 0.8 n 1.0 1.0 NaN \n", "7 1.2 n 1.0 1.0 NaN \n", "8 1.0 b 1.0 1.0 NaN \n", "9 0.6 n 1.0 1.0 NaN \n", 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1.200000 191786.0 1.0 female 21.0 \n", "1409 1.385000 191786.0 1.0 female 21.0 \n", "1410 1.667000 191786.0 1.0 female 21.0 \n", "1411 1.623000 191786.0 1.0 female 21.0 \n", "1412 1.408000 191786.0 1.0 female 21.0 \n", "1413 1.589000 191786.0 1.0 female 21.0 \n", "1414 1.738000 191786.0 1.0 female 21.0 \n", "1415 1.875000 191786.0 1.0 female 21.0 \n", "1416 1.801000 191786.0 1.0 female 21.0 \n", "1418 1.500000 191786.0 1.0 female 21.0 \n", "1419 1.452000 191786.0 1.0 female 21.0 \n", "1420 1.583000 191786.0 1.0 female 21.0 \n", "1421 1.636000 191786.0 1.0 female 21.0 \n", "1422 1.684000 191786.0 1.0 female 21.0 \n", "1423 1.251000 191786.0 1.0 female 21.0 \n", "1424 1.271000 191786.0 1.0 female 21.0 \n", "1425 2.022000 191786.0 1.0 female 21.0 \n", "1426 1.633000 191786.0 1.0 female 21.0 \n", "1427 1.574000 191786.0 1.0 female 21.0 \n", "1429 2.267000 191786.0 1.0 female 21.0 \n", "1430 1.671000 191786.0 1.0 female 21.0 \n", "1431 1.676000 191786.0 1.0 female 21.0 \n", "1432 1.083000 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59.880240 \n", "29 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "30 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "31 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "32 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "33 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "34 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "35 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "37 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "38 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "39 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "40 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "41 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "42 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "43 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "44 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "45 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "46 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", 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2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "67 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "68 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "70 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "71 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "72 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "73 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "74 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "75 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "76 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "77 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "78 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "79 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "81 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "82 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "83 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "84 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "85 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2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "105 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "106 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "107 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "108 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "109 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "110 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "111 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "112 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "114 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "115 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "116 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "117 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "118 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "119 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "120 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "121 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", 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\n", "141 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "142 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "143 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "144 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "145 2024-10-17_20h59.26.821 time_gen00_A01 59.880240 \n", "151 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "152 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "153 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "154 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "155 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "156 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "157 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "158 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "159 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "160 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "162 2024-10-07_19h35.15.027 time_generalization_00 NaN \n", "163 2024-10-07_19h35.15.027 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time_generalization_00 NaN \n", "901 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "902 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "903 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "904 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "905 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "906 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "907 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "908 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "910 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "911 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "912 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "913 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "914 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "915 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "916 2024-10-13_14h15.48.566 time_generalization_00 NaN \n", "917 2024-10-13_14h15.48.566 time_generalization_00 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\"num_unique_values\": 2,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"key_respBorN.rt\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6333200194214612,\n \"min\": 1.4306,\n \"max\": 2.9019,\n \"num_unique_values\": 5,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"participant\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 3105.0,\n \"max\": 3105.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"session\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 1.0,\n \"max\": 1.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 21.0,\n \"max\": 21.0,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"expName\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"frameRate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7.944109290391274e-15,\n \"min\": 59.88023952,\n \"max\": 59.88023952,\n \"num_unique_values\": 1,\n \"samples\": [],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ], "source": [ "# now remove the training data\n", "\"\"\"\n", "you may probably have to replace 'Block.thisTrialN' with 'Trials.thisTrialN' or whatever name\n", "has your variable indicating the number of trial or block of trials,\n", "as those would have missing values during the training phase,\n", "so the lines containing data from training will be removed\n", "\"\"\"\n", "\n", "new_df01 = filtered_df.dropna(subset=['Block.thisTrialN'])\n", "display(new_df01.describe())\n", "\n", "# .head() displays first couple of lines of a data frame\n", "display(new_df01.head())\n", "\n", "# now the count should be equal for all variables\n", "# (in my case there are some missing data in thisRow.t, a variable indicating cumulative time at each trial, some logs just lack this measure)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "eDdI6Cjlov_v", "outputId": "02df511d-82cb-49c5-b3e3-7ea2adc1eb4b" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_1dca124a-3026-40c8-9cc8-58d1905a5b02\", \"new_df24.csv\", 1088232)" ] }, "metadata": {} } ], "source": [ "#save the final data frame to backup and/or inspect in excel\n", "new_df.to_csv('new_df24.csv')\n", "\n", "# Download the file\n", "files.download(\"new_df24.csv\")" ] }, { "cell_type": "markdown", "metadata": { "id": "bVyEDo-wov_v" }, "source": [ "## 2. STATISTICAL ANALYSIS" ] }, { "cell_type": "markdown", "metadata": { "id": "lPKWjxALov_w" }, "source": [ "#### Explanation of the Code\n", "\n", "In the following section, we perform the following tasks:
\n", "\n", "- calculate response ratios.
\n", "- Load the dataset using pandas.
\n", "- Clean the data by removing missing values and unnecesary variables.
\n", "- Conduct statistical analysis using pingouin library.
\n", "\n", "You run each individual cell by Ctrl+Enter.
\n", "Throughout the code, you will have to adjust names of variables to match your experiment.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "O_T11LA1ov_w" }, "source": [ "### new df for anova" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "IRaG3Dbbov_w" }, "outputs": [], "source": [ "#create new data frame with response ratios using the dataframe called new_df01 with your data\n", "#new variable Response ratio will be created with values 0 to 1, representing the ration of same vs different judgments about the length of the stimuli as compared to standard\n", "\n", "\"\"\"\"\n", "Create a pivot table to calculate the counts of 'b' and 'n' for each group\n", "\n", "we need to make the counts for each participant at each duration at each condition,\n", "so edit the code to contain your independent variables\n", "my independent variables were 'feedback' and 'stress'\n", "keep the variables 'duration' and 'participant' in the list\n", "\"\"\"\n", "\n", "pivot_table = new_df01.pivot_table(index=['duration', 'feedback', 'participant','stress'], columns='key_respBorN.keys',\n", " aggfunc='size', fill_value=0)\n", "\n", "# Calculate the Response Ratio (count of 'b' / count of 'n') for each group\n", "pivot_table['Response_Ratio'] = pivot_table['b'] / (pivot_table['b'] + pivot_table['n'])\n", "\n", "# Reset the index to have a new dataframe with the specified columns\n", "result_df = pivot_table.reset_index()\n", "\n", "# If you want to replace any NaN values with 0, you can use fillna method\n", "result_df.fillna(0, inplace=True)\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#result_df.to_csv(\"response_ratios.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "QCClX8qHov_w", "outputId": "a1af4392-b304-418c-c003-66797ffe0578" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "key_respBorN.keys duration feedback participant stress b n \\\n", "0 0.4 0.0 3105 0.0 1 5 \n", "1 0.4 0.0 28980.0 1.0 0 6 \n", "2 0.4 0.0 53758.0 0.0 0 6 \n", "3 0.4 0.0 89680 0.0 0 6 \n", "4 0.4 0.0 94062.0 0.0 0 6 \n", "5 0.4 0.0 94204 0.0 1 5 \n", "6 0.4 0.0 113546.0 1.0 0 6 \n", "7 0.4 0.0 165627.0 0.0 0 6 \n", "8 0.4 0.0 182377.0 1.0 0 12 \n", "9 0.4 0.0 191786 0.0 0 6 \n", "10 0.4 0.0 220818 1.0 0 6 \n", "11 0.4 0.0 224929 0.0 1 5 \n", "12 0.4 0.0 251228.0 1.0 0 6 \n", "13 0.4 0.0 279081 0.0 0 6 \n", "14 0.4 0.0 295127.0 1.0 0 6 \n", "15 0.4 0.0 295379.0 1.0 4 2 \n", "16 0.4 0.0 312424 0.0 0 6 \n", "17 0.4 0.0 331244.0 0.0 2 4 \n", "18 0.4 0.0 336165 0.0 0 6 \n", "19 0.4 0.0 364982.0 1.0 1 5 \n", "20 0.4 0.0 372152 1.0 0 6 \n", "21 0.4 0.0 379438.0 0.0 1 5 \n", "22 0.4 0.0 387757 1.0 0 6 \n", "23 0.4 0.0 392549.0 0.0 0 6 \n", "24 0.4 0.0 395970.0 0.0 0 6 \n", "25 0.4 0.0 468938.0 0.0 0 6 \n", "26 0.4 0.0 484214 0.0 3 3 \n", "27 0.4 0.0 508816 0.0 0 6 \n", "28 0.4 0.0 528876 1.0 0 6 \n", "29 0.4 0.0 542053.0 0.0 1 5 \n", "30 0.4 0.0 547889.0 0.0 0 6 \n", "31 0.4 0.0 609930 0.0 2 4 \n", "32 0.4 0.0 663018.0 0.0 0 6 \n", "33 0.4 0.0 687194 0.0 3 3 \n", "34 0.4 0.0 706888 0.0 0 6 \n", "35 0.4 0.0 712386.0 1.0 2 4 \n", "36 0.4 0.0 753211.0 1.0 2 10 \n", "37 0.4 0.0 756759 1.0 0 6 \n", "38 0.4 0.0 757560.0 0.0 0 6 \n", "39 0.4 0.0 762464.0 0.0 0 6 \n", "40 0.4 0.0 798994.0 0.0 0 6 \n", "41 0.4 0.0 815636.0 0.0 0 6 \n", "42 0.4 0.0 845695.0 0.0 0 6 \n", "43 0.4 0.0 847531 1.0 0 6 \n", "44 0.4 0.0 857023 1.0 0 6 \n", "45 0.4 0.0 872510 0.0 0 6 \n", "46 0.4 0.0 873613.0 0.0 0 6 \n", "47 0.4 0.0 895840 0.0 0 6 \n", "48 0.4 0.0 927609.0 1.0 1 5 \n", "49 0.4 0.0 929321 1.0 0 6 \n", "50 0.4 0.0 949904.0 0.0 0 6 \n", "51 0.4 0.0 Jolana Kodetová 0.0 4 2 \n", "52 0.4 1.0 3105 0.0 2 4 \n", "53 0.4 1.0 28980.0 1.0 0 6 \n", "54 0.4 1.0 53758.0 0.0 0 6 \n", "55 0.4 1.0 89680 0.0 0 6 \n", "56 0.4 1.0 94062.0 0.0 1 5 \n", "57 0.4 1.0 94204 0.0 0 6 \n", "58 0.4 1.0 113546.0 1.0 1 5 \n", "59 0.4 1.0 165627.0 0.0 0 6 \n", "60 0.4 1.0 182377.0 1.0 2 10 \n", "61 0.4 1.0 191786 0.0 0 6 \n", "62 0.4 1.0 220818 1.0 0 6 \n", "63 0.4 1.0 224929 0.0 1 5 \n", "64 0.4 1.0 251228.0 1.0 0 6 \n", "65 0.4 1.0 279081 0.0 0 6 \n", "66 0.4 1.0 295127.0 1.0 0 6 \n", "67 0.4 1.0 295379.0 1.0 1 5 \n", "68 0.4 1.0 312424 0.0 0 6 \n", "69 0.4 1.0 331244.0 0.0 2 4 \n", "70 0.4 1.0 336165 0.0 0 6 \n", "71 0.4 1.0 364982.0 1.0 0 6 \n", "72 0.4 1.0 372152 1.0 0 6 \n", "73 0.4 1.0 379438.0 0.0 0 6 \n", "74 0.4 1.0 387757 1.0 0 6 \n", "75 0.4 1.0 392549.0 0.0 0 6 \n", "76 0.4 1.0 395970.0 0.0 0 6 \n", "77 0.4 1.0 468938.0 0.0 0 6 \n", "78 0.4 1.0 484214 0.0 4 2 \n", "79 0.4 1.0 508816 0.0 0 6 \n", "80 0.4 1.0 528876 1.0 0 6 \n", "81 0.4 1.0 542053.0 0.0 1 5 \n", "82 0.4 1.0 547889.0 0.0 0 6 \n", "83 0.4 1.0 609930 0.0 0 6 \n", "84 0.4 1.0 663018.0 0.0 0 6 \n", "85 0.4 1.0 687194 0.0 1 5 \n", "86 0.4 1.0 706888 0.0 0 6 \n", "87 0.4 1.0 712386.0 1.0 2 4 \n", "88 0.4 1.0 753211.0 1.0 0 12 \n", "89 0.4 1.0 756759 1.0 0 6 \n", "90 0.4 1.0 757560.0 0.0 0 6 \n", "91 0.4 1.0 762464.0 0.0 0 6 \n", "92 0.4 1.0 798994.0 0.0 0 6 \n", "93 0.4 1.0 815636.0 0.0 0 6 \n", "94 0.4 1.0 845695.0 0.0 0 6 \n", "95 0.4 1.0 847531 1.0 2 4 \n", "96 0.4 1.0 857023 1.0 1 5 \n", "97 0.4 1.0 872510 0.0 0 6 \n", "98 0.4 1.0 873613.0 0.0 0 6 \n", "99 0.4 1.0 895840 0.0 0 6 \n", "100 0.4 1.0 927609.0 1.0 0 6 \n", "101 0.4 1.0 929321 1.0 0 6 \n", "102 0.4 1.0 949904.0 0.0 0 6 \n", "103 0.4 1.0 Jolana Kodetová 0.0 1 5 \n", "104 0.6 0.0 3105 0.0 3 3 \n", "105 0.6 0.0 28980.0 1.0 1 5 \n", "106 0.6 0.0 53758.0 0.0 0 6 \n", "107 0.6 0.0 89680 0.0 0 6 \n", "108 0.6 0.0 94062.0 0.0 1 5 \n", "109 0.6 0.0 94204 0.0 0 6 \n", "110 0.6 0.0 113546.0 1.0 3 3 \n", "111 0.6 0.0 165627.0 0.0 1 5 \n", "112 0.6 0.0 182377.0 1.0 0 12 \n", "113 0.6 0.0 191786 0.0 0 6 \n", "114 0.6 0.0 220818 1.0 4 2 \n", "115 0.6 0.0 224929 0.0 2 4 \n", "116 0.6 0.0 251228.0 1.0 0 6 \n", "117 0.6 0.0 279081 0.0 0 6 \n", "118 0.6 0.0 295127.0 1.0 1 5 \n", "119 0.6 0.0 295379.0 1.0 0 6 \n", "120 0.6 0.0 312424 0.0 2 4 \n", "121 0.6 0.0 331244.0 0.0 3 3 \n", "122 0.6 0.0 336165 0.0 0 6 \n", "123 0.6 0.0 364982.0 1.0 1 5 \n", "124 0.6 0.0 372152 1.0 1 5 \n", "125 0.6 0.0 379438.0 0.0 1 5 \n", "126 0.6 0.0 387757 1.0 1 5 \n", "127 0.6 0.0 392549.0 0.0 0 6 \n", "128 0.6 0.0 395970.0 0.0 0 6 \n", "129 0.6 0.0 468938.0 0.0 0 6 \n", "130 0.6 0.0 484214 0.0 4 2 \n", "131 0.6 0.0 508816 0.0 0 6 \n", "132 0.6 0.0 528876 1.0 2 4 \n", "133 0.6 0.0 542053.0 0.0 2 4 \n", "134 0.6 0.0 547889.0 0.0 1 5 \n", "135 0.6 0.0 609930 0.0 2 4 \n", "136 0.6 0.0 663018.0 0.0 0 6 \n", "137 0.6 0.0 687194 0.0 4 2 \n", "138 0.6 0.0 706888 0.0 1 5 \n", "139 0.6 0.0 712386.0 1.0 3 3 \n", "140 0.6 0.0 753211.0 1.0 0 12 \n", "141 0.6 0.0 756759 1.0 0 6 \n", "142 0.6 0.0 757560.0 0.0 1 5 \n", "143 0.6 0.0 762464.0 0.0 0 6 \n", "144 0.6 0.0 798994.0 0.0 1 5 \n", "145 0.6 0.0 815636.0 0.0 0 6 \n", "146 0.6 0.0 845695.0 0.0 0 6 \n", 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0 6 \n", "206 0.6 1.0 949904.0 0.0 0 6 \n", "207 0.6 1.0 Jolana Kodetová 0.0 4 2 \n", "208 0.8 0.0 3105 0.0 3 3 \n", "209 0.8 0.0 28980.0 1.0 5 1 \n", "210 0.8 0.0 53758.0 0.0 4 2 \n", "211 0.8 0.0 89680 0.0 1 5 \n", "212 0.8 0.0 94062.0 0.0 3 3 \n", "213 0.8 0.0 94204 0.0 2 4 \n", "214 0.8 0.0 113546.0 1.0 3 3 \n", "215 0.8 0.0 165627.0 0.0 3 3 \n", "216 0.8 0.0 182377.0 1.0 6 6 \n", "217 0.8 0.0 191786 0.0 3 3 \n", "218 0.8 0.0 220818 1.0 4 2 \n", "219 0.8 0.0 224929 0.0 4 2 \n", "220 0.8 0.0 251228.0 1.0 3 3 \n", "221 0.8 0.0 279081 0.0 1 5 \n", "222 0.8 0.0 295127.0 1.0 1 5 \n", "223 0.8 0.0 295379.0 1.0 3 3 \n", "224 0.8 0.0 312424 0.0 5 1 \n", "225 0.8 0.0 331244.0 0.0 5 1 \n", "226 0.8 0.0 336165 0.0 3 3 \n", "227 0.8 0.0 364982.0 1.0 1 5 \n", "228 0.8 0.0 372152 1.0 1 5 \n", "229 0.8 0.0 379438.0 0.0 4 2 \n", "230 0.8 0.0 387757 1.0 3 3 \n", "231 0.8 0.0 392549.0 0.0 4 2 \n", "232 0.8 0.0 395970.0 0.0 5 1 \n", "233 0.8 0.0 468938.0 0.0 4 2 \n", "234 0.8 0.0 484214 0.0 4 2 \n", 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191786 0.0 0 6 \n", "583 1.4 1.0 220818 1.0 0 6 \n", "584 1.4 1.0 224929 0.0 0 6 \n", "585 1.4 1.0 251228.0 1.0 0 6 \n", "586 1.4 1.0 279081 0.0 0 6 \n", "587 1.4 1.0 295127.0 1.0 1 5 \n", "588 1.4 1.0 295379.0 1.0 2 4 \n", "589 1.4 1.0 312424 0.0 1 5 \n", "590 1.4 1.0 331244.0 0.0 1 5 \n", "591 1.4 1.0 336165 0.0 0 6 \n", "592 1.4 1.0 364982.0 1.0 2 4 \n", "593 1.4 1.0 372152 1.0 2 4 \n", "594 1.4 1.0 379438.0 0.0 0 6 \n", "595 1.4 1.0 387757 1.0 0 6 \n", "596 1.4 1.0 392549.0 0.0 0 6 \n", "597 1.4 1.0 395970.0 0.0 0 6 \n", "598 1.4 1.0 468938.0 0.0 1 5 \n", "599 1.4 1.0 484214 0.0 2 4 \n", "600 1.4 1.0 508816 0.0 0 6 \n", "601 1.4 1.0 528876 1.0 1 5 \n", "602 1.4 1.0 542053.0 0.0 1 5 \n", "603 1.4 1.0 547889.0 0.0 0 6 \n", "604 1.4 1.0 609930 0.0 5 1 \n", "605 1.4 1.0 663018.0 0.0 2 4 \n", "606 1.4 1.0 687194 0.0 3 3 \n", "607 1.4 1.0 697586.0 1.0 1 0 \n", "608 1.4 1.0 706888 0.0 0 6 \n", "609 1.4 1.0 712386.0 1.0 0 6 \n", "610 1.4 1.0 753211.0 1.0 0 12 \n", "611 1.4 1.0 756759 1.0 0 6 \n", "612 1.4 1.0 757560.0 0.0 2 4 \n", "613 1.4 1.0 762464.0 0.0 0 6 \n", "614 1.4 1.0 798994.0 0.0 2 4 \n", "615 1.4 1.0 815636.0 0.0 3 3 \n", "616 1.4 1.0 845695.0 0.0 1 5 \n", "617 1.4 1.0 847531 1.0 2 4 \n", "618 1.4 1.0 857023 1.0 0 6 \n", "619 1.4 1.0 872510 0.0 0 6 \n", "620 1.4 1.0 873613.0 0.0 2 4 \n", "621 1.4 1.0 895840 0.0 3 3 \n", "622 1.4 1.0 927609.0 1.0 1 5 \n", "623 1.4 1.0 929321 1.0 0 6 \n", "624 1.4 1.0 949904.0 0.0 0 6 \n", "625 1.4 1.0 Jolana Kodetová 0.0 5 1 \n", "626 1.6 0.0 3105 0.0 1 5 \n", "627 1.6 0.0 28980.0 1.0 0 6 \n", "628 1.6 0.0 53758.0 0.0 0 6 \n", "629 1.6 0.0 89680 0.0 0 6 \n", "630 1.6 0.0 94062.0 0.0 0 6 \n", "631 1.6 0.0 94204 0.0 4 2 \n", "632 1.6 0.0 113546.0 1.0 1 5 \n", "633 1.6 0.0 165627.0 0.0 1 5 \n", "634 1.6 0.0 182377.0 1.0 0 12 \n", "635 1.6 0.0 191786 0.0 1 5 \n", "636 1.6 0.0 220818 1.0 0 6 \n", "637 1.6 0.0 224929 0.0 0 6 \n", "638 1.6 0.0 251228.0 1.0 2 4 \n", "639 1.6 0.0 279081 0.0 0 6 \n", "640 1.6 0.0 295127.0 1.0 0 6 \n", 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key_respBorN.keysdurationfeedbackparticipantstressbnResponse_Ratio
00.40.031050.0150.166667
10.40.028980.01.0060.000000
20.40.053758.00.0060.000000
30.40.0896800.0060.000000
40.40.094062.00.0060.000000
50.40.0942040.0150.166667
60.40.0113546.01.0060.000000
70.40.0165627.00.0060.000000
80.40.0182377.01.00120.000000
90.40.01917860.0060.000000
100.40.02208181.0060.000000
110.40.02249290.0150.166667
120.40.0251228.01.0060.000000
130.40.02790810.0060.000000
140.40.0295127.01.0060.000000
150.40.0295379.01.0420.666667
160.40.03124240.0060.000000
170.40.0331244.00.0240.333333
180.40.03361650.0060.000000
190.40.0364982.01.0150.166667
200.40.03721521.0060.000000
210.40.0379438.00.0150.166667
220.40.03877571.0060.000000
230.40.0392549.00.0060.000000
240.40.0395970.00.0060.000000
250.40.0468938.00.0060.000000
260.40.04842140.0330.500000
270.40.05088160.0060.000000
280.40.05288761.0060.000000
290.40.0542053.00.0150.166667
300.40.0547889.00.0060.000000
310.40.06099300.0240.333333
320.40.0663018.00.0060.000000
330.40.06871940.0330.500000
340.40.07068880.0060.000000
350.40.0712386.01.0240.333333
360.40.0753211.01.02100.166667
370.40.07567591.0060.000000
380.40.0757560.00.0060.000000
390.40.0762464.00.0060.000000
400.40.0798994.00.0060.000000
410.40.0815636.00.0060.000000
420.40.0845695.00.0060.000000
430.40.08475311.0060.000000
440.40.08570231.0060.000000
450.40.08725100.0060.000000
460.40.0873613.00.0060.000000
470.40.08958400.0060.000000
480.40.0927609.01.0150.166667
490.40.09293211.0060.000000
500.40.0949904.00.0060.000000
510.40.0Jolana Kodetová0.0420.666667
520.41.031050.0240.333333
530.41.028980.01.0060.000000
540.41.053758.00.0060.000000
550.41.0896800.0060.000000
560.41.094062.00.0150.166667
570.41.0942040.0060.000000
580.41.0113546.01.0150.166667
590.41.0165627.00.0060.000000
600.41.0182377.01.02100.166667
610.41.01917860.0060.000000
620.41.02208181.0060.000000
630.41.02249290.0150.166667
640.41.0251228.01.0060.000000
650.41.02790810.0060.000000
660.41.0295127.01.0060.000000
670.41.0295379.01.0150.166667
680.41.03124240.0060.000000
690.41.0331244.00.0240.333333
700.41.03361650.0060.000000
710.41.0364982.01.0060.000000
720.41.03721521.0060.000000
730.41.0379438.00.0060.000000
740.41.03877571.0060.000000
750.41.0392549.00.0060.000000
760.41.0395970.00.0060.000000
770.41.0468938.00.0060.000000
780.41.04842140.0420.666667
790.41.05088160.0060.000000
800.41.05288761.0060.000000
810.41.0542053.00.0150.166667
820.41.0547889.00.0060.000000
830.41.06099300.0060.000000
840.41.0663018.00.0060.000000
850.41.06871940.0150.166667
860.41.07068880.0060.000000
870.41.0712386.01.0240.333333
880.41.0753211.01.00120.000000
890.41.07567591.0060.000000
900.41.0757560.00.0060.000000
910.41.0762464.00.0060.000000
920.41.0798994.00.0060.000000
930.41.0815636.00.0060.000000
940.41.0845695.00.0060.000000
950.41.08475311.0240.333333
960.41.08570231.0150.166667
970.41.08725100.0060.000000
980.41.0873613.00.0060.000000
990.41.08958400.0060.000000
1000.41.0927609.01.0060.000000
1010.41.09293211.0060.000000
1020.41.0949904.00.0060.000000
1030.41.0Jolana Kodetová0.0150.166667
1040.60.031050.0330.500000
1050.60.028980.01.0150.166667
1060.60.053758.00.0060.000000
1070.60.0896800.0060.000000
1080.60.094062.00.0150.166667
1090.60.0942040.0060.000000
1100.60.0113546.01.0330.500000
1110.60.0165627.00.0150.166667
1120.60.0182377.01.00120.000000
1130.60.01917860.0060.000000
1140.60.02208181.0420.666667
1150.60.02249290.0240.333333
1160.60.0251228.01.0060.000000
1170.60.02790810.0060.000000
1180.60.0295127.01.0150.166667
1190.60.0295379.01.0060.000000
1200.60.03124240.0240.333333
1210.60.0331244.00.0330.500000
1220.60.03361650.0060.000000
1230.60.0364982.01.0150.166667
1240.60.03721521.0150.166667
1250.60.0379438.00.0150.166667
1260.60.03877571.0150.166667
1270.60.0392549.00.0060.000000
1280.60.0395970.00.0060.000000
1290.60.0468938.00.0060.000000
1300.60.04842140.0420.666667
1310.60.05088160.0060.000000
1320.60.05288761.0240.333333
1330.60.0542053.00.0240.333333
1340.60.0547889.00.0150.166667
1350.60.06099300.0240.333333
1360.60.0663018.00.0060.000000
1370.60.06871940.0420.666667
1380.60.07068880.0150.166667
1390.60.0712386.01.0330.500000
1400.60.0753211.01.00120.000000
1410.60.07567591.0060.000000
1420.60.0757560.00.0150.166667
1430.60.0762464.00.0060.000000
1440.60.0798994.00.0150.166667
1450.60.0815636.00.0060.000000
1460.60.0845695.00.0060.000000
1470.60.08475311.0150.166667
1480.60.08570231.0060.000000
1490.60.08725100.0150.166667
1500.60.0873613.00.0060.000000
1510.60.08958400.0060.000000
1520.60.0927609.01.0150.166667
1530.60.09293211.0060.000000
1540.60.0949904.00.0060.000000
1550.60.0Jolana Kodetová0.0420.666667
1560.61.031050.0240.333333
1570.61.028980.01.0150.166667
1580.61.053758.00.0060.000000
1590.61.0896800.0060.000000
1600.61.094062.00.0060.000000
1610.61.0942040.0060.000000
1620.61.0113546.01.0240.333333
1630.61.0165627.00.0420.666667
1640.61.0182377.01.0480.333333
1650.61.01917860.0060.000000
1660.61.02208181.0150.166667
1670.61.02249290.0150.166667
1680.61.0251228.01.0240.333333
1690.61.02790810.0060.000000
1700.61.0295127.01.0240.333333
1710.61.0295379.01.0240.333333
1720.61.03124240.0060.000000
1730.61.0331244.00.0240.333333
1740.61.03361650.0060.000000
1750.61.0364982.01.0150.166667
1760.61.03721521.0060.000000
1770.61.0379438.00.0060.000000
1780.61.03877571.0240.333333
1790.61.0392549.00.0060.000000
1800.61.0395970.00.0060.000000
1810.61.0468938.00.0060.000000
1820.61.04842140.0420.666667
1830.61.05088160.0060.000000
1840.61.05288761.0150.166667
1850.61.0542053.00.0060.000000
1860.61.0547889.00.0150.166667
1870.61.06099300.0420.666667
1880.61.0663018.00.0060.000000
1890.61.06871940.0330.500000
1900.61.07068880.0060.000000
1910.61.0712386.01.0330.500000
1920.61.0753211.01.00120.000000
1930.61.07567591.0240.333333
1940.61.0757560.00.0240.333333
1950.61.0762464.00.0150.166667
1960.61.0798994.00.0060.000000
1970.61.0815636.00.0150.166667
1980.61.0845695.00.0060.000000
1990.61.08475311.0150.166667
2000.61.08570231.0240.333333
2010.61.08725100.0060.000000
2020.61.0873613.00.0150.166667
2030.61.08958400.0060.000000
2040.61.0927609.01.0330.500000
2050.61.09293211.0060.000000
2060.61.0949904.00.0060.000000
2070.61.0Jolana Kodetová0.0420.666667
2080.80.031050.0330.500000
2090.80.028980.01.0510.833333
2100.80.053758.00.0420.666667
2110.80.0896800.0150.166667
2120.80.094062.00.0330.500000
2130.80.0942040.0240.333333
2140.80.0113546.01.0330.500000
2150.80.0165627.00.0330.500000
2160.80.0182377.01.0660.500000
2170.80.01917860.0330.500000
2180.80.02208181.0420.666667
2190.80.02249290.0420.666667
2200.80.0251228.01.0330.500000
2210.80.02790810.0150.166667
2220.80.0295127.01.0150.166667
2230.80.0295379.01.0330.500000
2240.80.03124240.0510.833333
2250.80.0331244.00.0510.833333
2260.80.03361650.0330.500000
2270.80.0364982.01.0150.166667
2280.80.03721521.0150.166667
2290.80.0379438.00.0420.666667
2300.80.03877571.0330.500000
2310.80.0392549.00.0420.666667
2320.80.0395970.00.0510.833333
2330.80.0468938.00.0420.666667
2340.80.04842140.0420.666667
2350.80.05088160.0510.833333
2360.80.05288761.0420.666667
2370.80.0542053.00.0420.666667
2380.80.0547889.00.0420.666667
2390.80.06099300.0240.333333
2400.80.0663018.00.0510.833333
2410.80.06871940.0420.666667
2420.80.07068880.0330.500000
2430.80.0712386.01.0330.500000
2440.80.0753211.01.0660.500000
2450.80.07567591.0420.666667
2460.80.0757560.00.0330.500000
2470.80.0762464.00.0330.500000
2480.80.0798994.00.0510.833333
2490.80.0815636.00.0510.833333
2500.80.0845695.00.0330.500000
2510.80.08475311.0420.666667
2520.80.08570231.0240.333333
2530.80.08725100.0240.333333
2540.80.0873613.00.0330.500000
2550.80.08958400.0420.666667
2560.80.0927609.01.0420.666667
2570.80.09293211.0420.666667
2580.80.0949904.00.0240.333333
2590.80.0Jolana Kodetová0.0420.666667
2600.81.031050.0420.666667
2610.81.028980.01.0330.500000
2620.81.053758.00.0330.500000
2630.81.0896800.0330.500000
2640.81.094062.00.0330.500000
2650.81.0942040.0240.333333
2660.81.0113546.01.0420.666667
2670.81.0165627.00.0330.500000
2680.81.0182377.01.0840.666667
2690.81.01917860.0330.500000
2700.81.02208181.0330.500000
2710.81.02249290.0420.666667
2720.81.0251228.01.0240.333333
2730.81.02790810.0510.833333
2740.81.0295127.01.0420.666667
2750.81.0295379.01.0060.000000
2760.81.03124240.0601.000000
2770.81.0331244.00.0420.666667
2780.81.03361650.0330.500000
2790.81.0364982.01.0330.500000
2800.81.03721521.0601.000000
2810.81.0379438.00.0420.666667
2820.81.03877571.0420.666667
2830.81.0392549.00.0330.500000
2840.81.0395970.00.0330.500000
2850.81.0468938.00.0420.666667
2860.81.04842140.0330.500000
2870.81.05088160.0420.666667
2880.81.05288761.0150.166667
2890.81.0542053.00.0240.333333
2900.81.0547889.00.0420.666667
2910.81.06099300.0240.333333
2920.81.0663018.00.0330.500000
2930.81.06871940.0330.500000
2940.81.07068880.0420.666667
2950.81.0712386.01.0330.500000
2960.81.0753211.01.0480.333333
2970.81.07567591.0330.500000
2980.81.0757560.00.0420.666667
2990.81.0762464.00.0330.500000
3000.81.0798994.00.0420.666667
3010.81.0815636.00.0150.166667
3020.81.0845695.00.0330.500000
3030.81.08475311.0240.333333
3040.81.08570231.0330.500000
3050.81.08725100.0240.333333
3060.81.0873613.00.0330.500000
3070.81.08958400.0150.166667
3080.81.0927609.01.0330.500000
3090.81.09293211.0601.000000
3100.81.0949904.00.0240.333333
3110.81.0Jolana Kodetová0.0510.833333
3121.00.031050.02040.833333
3131.00.028980.01.01860.750000
3141.00.053758.00.02130.875000
3151.00.0896800.01860.750000
3161.00.094062.00.02130.875000
3171.00.0942040.01590.625000
3181.00.0113546.01.01680.666667
3191.00.0165627.00.01860.750000
3201.00.0182377.01.032160.666667
3211.00.01917860.02310.958333
3221.00.02208181.02130.875000
3231.00.02249290.01860.750000
3241.00.0251228.01.01950.791667
3251.00.02790810.02310.958333
3261.00.0295127.01.01680.666667
3271.00.0295379.01.011130.458333
3281.00.03124240.02220.916667
3291.00.0331244.00.02040.833333
3301.00.03361650.01770.708333
3311.00.0364982.01.012120.500000
3321.00.03721521.01860.750000
3331.00.0379438.00.01860.750000
3341.00.03877571.01860.750000
3351.00.0392549.00.01860.750000
3361.00.0395970.00.02130.875000
3371.00.0468938.00.01950.791667
3381.00.04842140.014100.583333
3391.00.05088160.01950.791667
3401.00.05288761.02310.958333
3411.00.0542053.00.01680.666667
3421.00.0547889.00.01860.750000
3431.00.06099300.01860.750000
3441.00.0663018.00.02040.833333
3451.00.06871940.012120.500000
3461.00.07068880.02130.875000
3471.00.0712386.01.09150.375000
3481.00.0753211.01.04080.833333
3491.00.07567591.01680.666667
3501.00.0757560.00.02040.833333
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\"number\",\n \"std\": 0.47620533426120526,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 1.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"b\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 0,\n \"max\": 40,\n \"num_unique_values\": 26,\n \"samples\": [\n 20,\n 11\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"n\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 24,\n \"num_unique_values\": 18,\n \"samples\": [\n 5,\n 6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Response_Ratio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.30496501192913683,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 18,\n \"samples\": [\n 0.16666666666666666,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 12 } ], "source": [ "result_df" ] }, { "cell_type": "code", "source": [ "'''\n", "before you proceed to statistics section, make sure that each participant has the same number of measurements,\n", "overally, have a look on expected number of rows and real number of rows, if they match, proceed,\n", "otherwise, identify participants with not all measurements recorded and remove them from the data set\n", "'''" ], "metadata": { "id": "U-Mj9JgXIok7" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# Group the data by 'participant' and 'duration' and count the Response_Ratio occurrences\n", "count_df = result_df.groupby(['participant', 'duration']).size().reset_index(name='count')\n", "\n", "# Identify participants who do not have exactly 2 measurements per duration\n", "invalid_participants = count_df[count_df['count'] != 2]['participant'].unique()\n", "\n", "# Print the list of participants who do not meet the condition\n", "if len(invalid_participants) > 0:\n", " print(\"Participants with unequal measurements (not 2 for each duration):\")\n", " for participant in invalid_participants:\n", " print(participant)\n", "else:\n", " print(\"All participants have exactly 2 measurements for each duration.\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2AZt9_DaEC8Q", "outputId": "908eca46-ab0e-4563-bde3-553f80539e48" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "All participants have exactly 2 measurements for each duration.\n" ] } ] }, { "cell_type": "code", "source": [ "#next 3 cells serve for potential debugging, may skip" ], "metadata": { "id": "TMi5pjBXunf2" }, "execution_count": 20, "outputs": [] }, { "cell_type": "code", "source": [ "# Number of unique participants\n", "unique_participants = result_df['participant'].nunique()\n", "\n", "# Number of unique durations\n", "unique_durations = result_df['duration'].nunique()\n", "\n", "# Number of unique feedback values\n", "unique_feedback = result_df['feedback'].nunique()\n", "\n", "# Print the results\n", "print(f\"Number of unique participants: {unique_participants}\")\n", "print(f\"Number of unique durations: {unique_durations}\")\n", "print(f\"Number of unique feedback values: {unique_feedback}\")\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0GdG1OaIEGIo", "outputId": "5f5c3e56-54c9-401b-e84e-1261751d0541" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of unique participants: 50\n", "Number of unique durations: 7\n", "Number of unique feedback values: 2\n" ] } ] }, { "cell_type": "code", "source": [ "expected_rows = unique_participants * unique_durations * unique_feedback" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wJQpjCFSErmT", "outputId": "2086c6fb-6094-4a3e-ef44-30455a0c93f1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "700" ] }, "metadata": {}, "execution_count": 59 } ] }, { "cell_type": "code", "source": [ "# Get the number of rows in result_df\n", "num_rows = result_df.shape[0]\n", "print(f\"Number of rows in result_df: {num_rows}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "35MKCAurFhPJ", "outputId": "4477fae0-b508-462b-e801-908d833f82f4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of rows in result_df: 700\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "sf8WPcf9ov_w" }, "source": [ "### plot the temporal generalization gradients" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 466 }, "id": "UI38nyRVov_w", "outputId": "8838e572-a20a-479c-f235-7a31f8d399ac" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 21 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ], "source": [ "# here I use my within factor 'feedback' as hue to observe the distinct gradients in one plot\n", "\n", "sns.lineplot(data=result_df, x=\"duration\", y=\"Response_Ratio\", hue=\"feedback\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "id": "2HEWsbaAov_x", "outputId": "f57792cf-1635-4505-c50a-d0c8641fa527" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ], "source": [ "# Plot using 'stress' as the hue\n", "sns.lineplot(data=result_df, x=\"duration\", y=\"Response_Ratio\", hue=\"stress\")\n", "\n", "# Customizing the legend labels\n", "new_legend_labels = ['accuracy', 'speeded'] # Replace with your desired labels\n", "\n", "# Fetch handles and labels, then set new custom labels\n", "handles, _ = plt.gca().get_legend_handles_labels()\n", "plt.legend(handles=handles, labels=new_legend_labels, title='Condition')\n", "\n", "# Show the plot\n", "plt.show()\n" ] }, { "cell_type": "code", "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Plot lines without shaded variance (ci deprecated, use errorbar=None)\n", "sns.lineplot(\n", " data=result_df,\n", " x=\"duration\",\n", " y=\"Response_Ratio\",\n", " hue=\"stress\",\n", " style=\"feedback\",\n", " markers=True,\n", " dashes=False,\n", " errorbar=None # Replaces ci=None\n", ")\n", "\n", "# Customizing the legend labels for clarity\n", "# Use actual legend labels as keys instead of tuples\n", "legend_labels = {\n", " '0': 'No Stress',\n", " '1': 'Stress',\n", " '0_feedback': 'No Feedback',\n", " '1_feedback': 'Feedback'\n", "}\n", "\n", "# Update legend with custom labels\n", "handles, labels = plt.gca().get_legend_handles_labels()\n", "\n", "# Loop to map labels to the legend_labels dictionary\n", "new_labels = []\n", "for label in labels:\n", " if 'stress' in label.lower(): # Example of identifying 'stress'\n", " new_labels.append(legend_labels.get(label, label))\n", " elif 'feedback' in label.lower():\n", " new_labels.append(legend_labels.get(f\"{label}_feedback\", label))\n", " else:\n", " new_labels.append(label)\n", "\n", "plt.legend(handles=handles, labels=new_labels, title='Condition')\n", "\n", "# Display the plot\n", "plt.show()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 449 }, "id": "NbhVB8-9Am8u", "outputId": "3ccf34b5-b249-4a9e-fd37-913e7fd56ec4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "metadata": { "id": "SCszKyoYov_x" }, "source": [ "### perform repeated measures anova on your within factor and duration" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 213 }, "id": "3A0FoxJBov_x", "outputId": "f39165fd-3bba-4bf9-8d42-c7f08e38a3b3" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/pingouin/distribution.py:507: FutureWarning: DataFrame.groupby with axis=1 is deprecated. Do `frame.T.groupby(...)` without axis instead.\n", " data.groupby(level=1, axis=1, observed=True, group_keys=False)\n", "/usr/local/lib/python3.10/dist-packages/pingouin/distribution.py:507: FutureWarning: DataFrameGroupBy.diff with axis=1 is deprecated and will be removed in a future version. Operate on the un-grouped DataFrame instead\n", " data.groupby(level=1, axis=1, observed=True, group_keys=False)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Source SS ddof1 ddof2 MS F \\\n", "0 duration 37.869365 6 294 6.311561 135.951389 \n", "1 feedback 0.280000 1 49 0.280000 13.253117 \n", "2 duration * feedback 0.202778 6 294 0.033796 1.445023 \n", "\n", " p-unc p-GG-corr ng2 eps \n", "0 9.517926e-82 3.222409e-48 0.588453 0.575967 \n", "1 6.548062e-04 6.548062e-04 0.010462 1.000000 \n", "2 1.971909e-01 2.193782e-01 0.007598 0.683963 " ], "text/html": [ "\n", "
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\"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Parametric\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"T\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 2.733967130597301,\n \"max\": 2.733967130597301,\n \"num_unique_values\": 1,\n \"samples\": [\n 2.733967130597301\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dof\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 49.0,\n \"max\": 49.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 49.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"alternative\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"two-sided\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p-unc\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.008683638412080683,\n \"max\": 0.008683638412080683,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.008683638412080683\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"BF10\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"4.243\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cohen\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.42640143271122066,\n \"max\": 0.42640143271122066,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.42640143271122066\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} } ], "source": [ "\"\"\"\"\n", "to be able to perform this test, we need to have same number of elements in all conditions,\n", "In my case, I found one participant that was causing error, as he/she completed only halve of the experiment\n", "I found it using the code in the cell bellow, hope you might not need it\n", "\"\"\"\n", "# Convert 'participant' to string to ensure consistency\n", "result_df['participant'] = result_df['participant'].astype('category')\n", "\n", "# Re-check the column types\n", "print(result_df.dtypes)\n", "\n", "# Perform the sanity check again\n", "#print(result_df.groupby(['participant', 'duration', 'feedback']).size())\n", "\n", "result_df['duration'] = result_df['duration'].astype('category')\n", "result_df['feedback'] = result_df['feedback'].astype('category')\n", "# Convert 'Response_Ratio' to numeric, coerce invalid values to NaN\n", "result_df['Response_Ratio'] = pd.to_numeric(result_df['Response_Ratio'], errors='coerce')\n", "\n", "# Drop rows with NaN in 'Response_Ratio'\n", "result_df = result_df.dropna(subset=['Response_Ratio'])\n", "\n", "#perform pairwise t-tests\n", "\n", "\n", "# run pairwise ttest\n", "a = pg.pairwise_tests(data=result_df, within=['duration','feedback'], dv='Response_Ratio', subject='participant', effsize = 'cohen')\n", "\n", "# you might want to see only significant results, and only those for interaction of feedback*duration\n", "display(a[(a[\"p-unc\"] < 0.05) & (a[\"Contrast\"] == 'duration * feedback')])\n", "\n" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "LlU4UCDbD_48" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aXnyY-Ofov_y", "outputId": "c880b360-f76f-48eb-f73d-9a8be69175cd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of comparisons: 14\n", "Bonferroni-corrected alpha: 0.0035714285714285718\n" ] } ], "source": [ "\"\"\"\n", "as we are performing multiple comparisons, we should make sure we aplly p correction,\n", "if not applied during testing, you can calculate the desired alpha after bonferroni correction manually\n", "\"\"\"\n", "\n", "def calculate_num_comparisons(levels_factor1, levels_factor2):\n", " return levels_factor1 * levels_factor2\n", "\n", "def calculate_corrected_alpha(desired_fwer, num_comparisons):\n", " return desired_fwer / num_comparisons\n", "\n", "# Example usage:\n", "levels_factor1 = 2 # Number of levels in the first factor (feedback)\n", "levels_factor2 = 7 # Number of levels in the second factor (duration)\n", "desired_fwer = 0.05 # Desired family-wise error rate\n", "\n", "num_comparisons = calculate_num_comparisons(levels_factor1, levels_factor2)\n", "corrected_alpha = calculate_corrected_alpha(desired_fwer, num_comparisons)\n", "\n", "print(f\"Number of comparisons: {num_comparisons}\")\n", "print(f\"Bonferroni-corrected alpha: {corrected_alpha}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "jA2kzYgiov_y" }, "source": [ "### perform ANOVA on your between factor" ] }, { 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\"samples\": [\n 0.3967045155659961\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sphericity\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"W-spher\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.005754289674251913,\n \"max\": 0.005754289674251913,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.005754289674251913\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p-spher\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.031302200638962635,\n \"max\": 0.031302200638962635,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.031302200638962635\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 24 } ], "source": [ "\"\"\"\n", "as we measured the influence of our between factor various times in a participant (in distinct durations),\n", "mixed anova (version of anova taking account of both within and between factors) should be performed\n", "to figure out if there were significant differences in between the groups at specific durations\n", "\"\"\"\n", "\n", "pg.mixed_anova(data=result_df, dv='Response_Ratio', within='duration', subject='participant',\n", " between='stress', correction='auto', effsize='np2')\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 115 }, "id": "uSC95Vxtov_y", "outputId": "dc606232-fe3b-4202-9ab4-c82a9b86a3c1" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/pingouin/pairwise.py:28: UserWarning: pairwise_ttests is deprecated, use pairwise_tests instead.\n", " warnings.warn(\"pairwise_ttests is deprecated, use pairwise_tests instead.\", UserWarning)\n" ] }, { "output_type": 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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "repr_error": "0" } }, "metadata": {} } ], "source": [ "#perform pairwise t-tests\n", "\"\"\"\"\n", "to be able to perform this test, we need to have same number of elements in all conditions,\n", "see the section above if having issues with the balance of the sample error...\n", "\"\"\"\n", "\n", "# run pairwise ttest\n", "a = pg.pairwise_ttests(data=result_df, within='duration', dv='Response_Ratio', subject='participant', between = 'stress', effsize = 'cohen')\n", "# you might want to see only significant results, and only those for interaction of feedback*duration\n", "display(a[(a[\"p-unc\"] < 0.05) & (a[\"Contrast\"] == 'duration * stress')])\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zgp3I3rvov_z", "outputId": "491b48a1-f58c-4b30-8a82-49d8b476837b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of comparisons: 14\n", "Bonferroni-corrected alpha: 0.0035714285714285718\n" ] } ], "source": [ "\"\"\"\"\n", "as we are performing multiple comparisons, we should make sure we aplly p correction,\n", "if not applied during testing, you can calculate the desired alpha after bonferroni correction manually\n", "\"\"\"\"\n", "\n", "def calculate_num_comparisons(levels_factor1, levels_factor2):\n", " return levels_factor1 * levels_factor2\n", "\n", "def calculate_corrected_alpha(desired_fwer, num_comparisons):\n", " return desired_fwer / num_comparisons\n", "\n", "# Example usage:\n", "levels_factor1 = 2 # Number of levels in the first factor (stress)\n", "levels_factor2 = 7 # Number of levels in the second factor (duration)\n", "desired_fwer = 0.05 # Desired family-wise error rate\n", "\n", "num_comparisons = calculate_num_comparisons(levels_factor1, levels_factor2)\n", "corrected_alpha = calculate_corrected_alpha(desired_fwer, num_comparisons)\n", "\n", "print(f\"Number of comparisons: {num_comparisons}\")\n", "print(f\"Bonferroni-corrected alpha: {corrected_alpha}\")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FNogNIVzov_z" }, "outputs": [], "source": [ "#now you can check if the results of posthoc testing stand significant against the bonferroni-corrected alpha" ] }, { "cell_type": "markdown", "metadata": { "id": "4Ub8cQAmov_z" }, "source": [ "### calculate weighted mean gradient\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true, "id": "lK6vP4Q0ov_z" }, "outputs": [], "source": [ "# Assuming you have the 'result_df' dataframe from the previous step\n", "\n", "\"\"\"\n", "We are replicating a procedure from Klapproth and Wearden, 2011\n", "Here is citation from the paper, describing the construction of the parameter:\n", "\n", "In order to numerically examine the shift of the\n", "gradients, we calculated the weighted mean of the\n", "gradient of each participant.\n", "This was done by\n", "[1]multiplying the number of “same” responses with the\n", "duration at which a “same” response was given,\n", "[2] adding these products,\n", "and then\n", "[4] dividing this weighted sum by the number of “same” responses\n", "of each participant given at all durations (calculated at step [3] in our case)\n", "\"\"\"\n", "\n", "\n", "# [1] Calculate the weighted sum for each participant and duration combination (result_df already contains number of same responses as variable 'b')\n", "\n", "result_df['Weighted_Sum'] = result_df['b'] * result_df['duration']\n", "\n", "# [2] Add the weighted sums to have a total for each combination of participant and values of your independent variables\n", "\"\"\"\"\n", "You will need to replace my independent variables by yours.\n", "Keep the participant in the list, though.\n", "\"\"\"\n", "grouped = result_df.groupby(['participant', 'feedback','stress'])['Weighted_Sum'].sum().reset_index()\n", "\n", "# [3] Calculate the total count of \"b\" responses for each participant and feedback combination\n", "grouped['Total_b_Responses'] = result_df.groupby(['participant', 'feedback'])['b'].transform('sum')\n", "\n", "# [4] Calculate the weighted mean of the gradient for each participant and feedback combination\n", "grouped['Weighted_Mean_Gradient'] = grouped['Weighted_Sum'] / grouped['Total_b_Responses']\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#grouped.to_csv(\"weighted_mean_gradient.csv\", index=False)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ZWPoXcm3ov_0" }, "source": [ "### plot results and test your hypotheses" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "id": "O6tzYvuTov_0", "outputId": "40bd89ee-2171-454b-8d5f-bf6c2dc533b3", "colab": { "base_uri": "https://localhost:8080/", "height": 466 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 27 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ], "source": [ "# create boxplot supporting your analysis\n", "\"\"\"\n", "As I had two variables, I display on x and the other as hue, if you have less variables, use just x\n", "\"\"\"\n", "sns.boxplot(data=grouped, x=\"stress\", y=\"Weighted_Mean_Gradient\", hue=\"feedback\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zl5vuLOLov_0", "outputId": "677fd9cd-59a5-48d1-96dc-57ef49889bbe" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=grouped, x=\"stress\", y=\"Weighted_Mean_Gradient\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iqMDszqAov_5", "outputId": "92e7bc00-8e0e-457d-caf5-63bda640a4b2" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "Choose a version of ANOVA, congruent with your design.\n", " " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "TXxzwyO1ov_5", "outputId": "d29a7e00-e830-401b-e9a9-9b833c704202", "colab": { "base_uri": "https://localhost:8080/", "height": 81 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Source ddof1 ddof2 F p-unc ng2 eps\n", "0 feedback 1 9 2.374634 0.157709 0.098612 1.0" ], "text/html": [ "\n", "
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" ], "text/plain": [ " Source SS DF1 DF2 MS F p-unc np2 \\\n", "0 stress 0.014358 1 27 0.014358 0.160084 0.692227 0.005894 \n", "1 feedback 0.000226 1 27 0.000226 0.003812 0.951226 0.000141 \n", "2 Interaction 0.021490 1 27 0.021490 0.362350 0.552226 0.013243 \n", "\n", " eps \n", "0 NaN \n", "1 1.0 \n", "2 NaN " ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use this for mixed designs\n", "pg.mixed_anova(data=grouped, dv=\"Weighted_Mean_Gradient\", within=\"feedback\", between ='stress', subject=\"participant\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "r5jVC45fov_9", "outputId": "00a24562-e792-4d5c-e198-e98a0efb82dd" }, "outputs": [ { "data": { "text/html": [ "
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Sourceddof1ddof2Fp-uncnp2
0stress1570.2255550.6366540.003942
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" ], "text/plain": [ " Source ddof1 ddof2 F p-unc np2\n", "0 stress 1 57 0.225555 0.636654 0.003942" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# use this for between subject designs\n", "pg.anova(data=grouped, dv=\"Weighted_Mean_Gradient\", between ='stress')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NTDhIlRCov_-", "outputId": "9a2515ab-7fad-4cb9-d173-3cda77968e45" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.0321791542242609, 1.0716820718544977, 1.03491869257555, 1.0016745070050221]\n" ] } ], "source": [ "# generate mean values for each condition\n", "\"\"\"\n", "edit to calculate weighted mean gradients for each condition\n", "this is the proxy of how long the second lasted for the given situation\n", "\"\"\"\n", "\n", "mean_A = grouped[(grouped[\"stress\"]==0)&(grouped[\"feedback\"]==0)][\"Weighted_Mean_Gradient\"].mean()\n", "mean_B = grouped[(grouped[\"stress\"]==0)&(grouped[\"feedback\"]==1)][\"Weighted_Mean_Gradient\"].mean()\n", "mean_C = grouped[(grouped[\"stress\"]==1)&(grouped[\"feedback\"]==0)][\"Weighted_Mean_Gradient\"].mean()\n", "mean_D = grouped[(grouped[\"stress\"]==1)&(grouped[\"feedback\"]==1)][\"Weighted_Mean_Gradient\"].mean()\n", "\n", "#mean_A\n", "#mean_B\n", "print([mean_A, mean_B, mean_C, mean_D])" ] }, { "cell_type": "markdown", "metadata": { "id": "x9BLIvkRov_-" }, "source": [ "### TTESTS" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "id": "Bwg-hAdDov_-", "outputId": "7df40336-d3e2-4057-93e7-b4722f5ec95a", "colab": { "base_uri": "https://localhost:8080/", "height": 565 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " T dof alternative p-val CI95% cohen-d \\\n", "T-test 0.182096 17.953943 two-sided 0.857548 [-0.15, 0.17] 0.066167 \n", "\n", " BF10 power \n", "T-test 0.426 0.051894 " ], "text/html": [ "\n", "
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Tdofalternativep-valCI95%cohen-dBF10power
T-test0.18209617.953943two-sided0.857548[-0.15, 0.17]0.0661670.4260.051894
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"print(\\\"mean difference:\\\", mean_diff)\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"T\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.1820960137315769,\n \"max\": 0.1820960137315769,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.1820960137315769\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"dof\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 17.9539434637365,\n \"max\": 17.9539434637365,\n \"num_unique_values\": 1,\n \"samples\": [\n 17.9539434637365\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"alternative\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"two-sided\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"p-val\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.8575476013109964,\n \"max\": 0.8575476013109964,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.8575476013109964\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"CI95%\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"cohen-d\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.06616703277962882,\n \"max\": 0.06616703277962882,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.06616703277962882\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"BF10\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"0.426\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 0.05189449016002319,\n \"max\": 0.05189449016002319,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.05189449016002319\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "WMG under accuracy condition: 1.0233696373278274\n", "WMG under speeded condition: 1.0094175148522975\n", "mean difference: 0.013952122475529904\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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9kJCQapOgAQCAtRm6/UL9+vV1xx13/O56iYmJWrVqldcPBS1ZskRDhw7V22+/fc75PwAA4OLk0WEpT/344486efKkV1/zrbfe0uDBg/XWW29xqwcAAHAWQ3tuvKWsrKzaqeUFBQXKz89XZGSk7Ha70tPT9fPPP+u1116TdPpQ1KBBgzRnzhx16tRJ+/fvlySFhYUpIiLClM8AAAD8i0/33PyevLw8JSUluU7jTktLU1JSkiZOnCjp9EUDCwsLXetnZGTo1KlT+vvf/66YmBjXY9SoUabkBwAA/sfUPTfdu3eX0+k85/LMzMxqz3NycnwbCAAA1Hmm7rkBAADwNsoNAACwFJ+Wm0WLFikqKsqXbwEAAFCN4Tk3WVlZysrK0sGDB+VwOKote+WVVyRJ/fv3r106AAAADxkqN5MnT9bTTz+tjh07KiYmRjabzdu5AAAADDFUbhYuXKjMzEzdd9993s4DAABQK4bm3Jw4cUI33XSTt7MAAADUmqFyM3ToUL355pvezgIAAFBrhg5LVVRUKCMjQ5999pnatm2roKCgastnzZrllXAAAACeMlRuvv32W7Vv316S9N1331VbxuRiAABgJkPlJjs729s5AAAAvIIrFAMAAEsxfBG/vLw8LVu2TIWFhTpx4kS1ZcuXL691MAAAACMM7blZsmSJbrrpJm3btk3vvfeeTp48qa1bt2rt2rWKiIjwdkYAAAC3GSo3zz77rGbPnq0VK1YoODhYc+bM0fbt23X33XfLbrd7OyMAAIDbDJWbXbt26bbbbpMkBQcHq7y8XDabTaNHj1ZGRoZXAwIAAHjCULlp3LixSktLJUnNmjVznQ5+9OhRHTt2zHvpAAAAPGRoQnFycrI+/fRTJSYm6q677tKoUaO0du1affrpp+rZs6e3MwIAALjNULmZN2+eKioqJEnjxo1TUFCQvvrqK/3pT3/S+PHjvRoQAADAE4bKTWRkpOvngIAAjR071muBAAAAasPwdW527dqlxYsXa9euXZozZ46aNGmi1atXy26365prrvFmxjrpwIEDKi4uNjvGRW/Pnj3V/oT5IiIiFBUVZXaMi15FRYUKCwvNjoHfsNvtCg0NNTuGJdicTqfT040+//xzpaamqkuXLsrNzdW2bdvUokULPffcc8rLy9M777zji6xeUVJSooiICBUXFys8PNwn73HgwAH99b6BOnmi0ievD9RlQcEhev2fr1FwTLZjxw4NHz7c7Bj4jYyMDCUkJJgdw2958vvb0J6bsWPHasqUKUpLS1PDhg1d4z169NC8efOMvKSlFBcX6+SJSh1v0U2OUC5qCJwRUFEs7f5cxcXFlBuT2e32On/pjj179mjq1KkaN26c4uLizI5Ta1wnznsMlZstW7bozTffPGu8SZMmOnz4cK1DWYUjNEKOBpeZHQMAzhIaGmqZvQRxcXGW+SzwDkPXuWnUqJGKiorOGt+8ebOaNWtW61AAAABGGSo3f/nLX/Tkk09q//79stlscjgc+vLLLzVmzBgNHDjQ2xkBAADcZvjeUq1bt1ZsbKzKysrUpk0bJScn66abbuI6NwAAwFSG5twEBwfrpZde0oQJE/Tdd9+prKxMSUlJatmypbfzAQAAeMTwdW6k0zO7md0NAAD8iUfl5umnn3ZrvYkTJxoKAwAAUFselZunnnpKTZs2VZMmTXSua//ZbDbKDQAAMI1H5SY1NVVr165Vx44d9be//U3/9V//pYAAQ3OSAQAAfMKjZrJy5Urt2rVLnTp10uOPP65mzZrpySef1Pfff++rfAAAAB7xeEJx06ZNlZ6ervT0dOXm5mrx4sW6/vrrlZiYqM8++0xhYWG+yAkAfoMb4/oHbozrf/zlxri1Olvq+uuv148//qj//Oc/2rx5s06ePEm5AWBp3BjX/0ydOtXsCPh//OXGuIbKzfr16/XKK69o2bJlSkhI0ODBg9W/f3+f3WUbAPwFN8YFauZPN8b1qNxMnz5dmZmZOnz4sAYMGKAvvvhCbdu29VU2APBb3BgX8F8elZuxY8fKbrfr7rvvls1mU2ZmZo3rzZo1yxvZAAAAPOZRuUlOTpbNZtPWrVvPuY7NZqt1KAAAAKM8Kjc5OTk+igEAAOAdPr0CX3h4uHbv3u3LtwAAAKjGp+XmXLdoAAAA8BXunQAAACyFcgMAACyFcgMAACzFp+WG08IBAMCFxoRiAABgKT4tN6tXr1azZs18+RYAAADVuH0Rv7S0NLdf9MztF7p27ep5IgAAgFpwu9xs3ry52vNNmzbp1KlTatWqlSRpx44dCgwM1HXXXefdhAAAAB5wu9xkZ2e7fp41a5YaNmyoV199VY0bN5Yk/frrrxo8eLD+8Ic/eD8lAACAmwzNuZk5c6amTZvmKjaS1LhxY02ZMkUzZ870WjgAAABPGSo3JSUlOnTo0Fnjhw4dUmlpaa1DAQAAGGWo3Nxxxx0aPHiwli9frp9++kk//fST3n33XQ0ZMkR33nmntzMCAAC4ze05N7+1cOFCjRkzRv3799fJkydPv1C9ehoyZIhmzJjh1YAAAACeMFRu6tevrwULFmjGjBnatWuXJOnKK69UgwYNvBoOAADAU7W6iF9RUZGKiorUsmVLNWjQgCsSAwAA0xkqN7/88ot69uyphIQE9enTR0VFRZKkIUOG6LHHHvNqQAAAAE8YKjejR49WUFCQCgsLVb9+fdf4Pffco48//thr4QAAADxlaM7NJ598ojVr1uiKK66oNt6yZUvt2bPHK8EAAACMMLTnpry8vNoemzOOHDmikJCQWocCAAAwylC5+cMf/qDXXnvN9dxms8nhcGj69OlKSUnxWjgAAABPGTosNX36dPXs2VN5eXk6ceKEnnjiCW3dulVHjhzRl19+6e2MAAAAbjO05+baa6/Vjh071LVrV/3xj39UeXm57rzzTm3evFlXXnmltzMCAAC4zdCem8LCQsXGxmrcuHE1LrPb7bUOBgD+LOD4UbMjAH7Fn74ThspNfHy8ioqK1KRJk2rjv/zyi+Lj41VVVeWVcADgr8IKcs2OAOAcDJUbp9Mpm8121nhZWZlCQ0NrHQoA/N3x+GQ5whqZHQPwGwHHj/pN6feo3KSlpUk6fXbUhAkTqp0OXlVVpQ0bNqh9+/Zuv15ubq5mzJihjRs3qqioSO+995769et33m1ycnKUlpamrVu3KjY2VuPHj9f999/vyccAgFpzhDWSo8FlZscAUAOPys3mzZslnd5zs2XLFgUHB7uWBQcHq127dhozZozbr1deXq527drpb3/7m+68887fXb+goEC33XabRowYoTfeeENZWVkaOnSoYmJi1Lt3b08+CgAAsCiPyk12drYkafDgwZozZ47Cw8Nr9eapqalKTU11e/2FCxcqPj5eM2fOlCRdffXVWrdunWbPnk25AQAAkgyeCr548eJaFxsj1q9fr169elUb6927t9avX3/ObSorK1VSUlLtAQAArMvQhOLy8nI999xzysrK0sGDB+VwOKot3717t1fC/V/79+9XVFRUtbGoqCiVlJTo+PHjCgsLO2ubadOmafLkyT7JAwAA/I+hcjN06FB9/vnnuu+++xQTE1PjmVP+Ij093TURWpJKSkoUGxtrYiIAAOBLhsrN6tWrtXLlSnXp0sXbec4rOjpaBw4cqDZ24MABhYeH17jXRpJCQkK4mScAABcRQ3NuGjdurMjISG9n+V2dO3dWVlZWtbFPP/1UnTt3vuBZAACAfzJUbp555hlNnDhRx44dq9Wbl5WVKT8/X/n5+ZJOn+qdn5+vwsJCSacPKQ0cONC1/ogRI7R792498cQT2r59uxYsWKBly5Zp9OjRtcoBAACsw+3DUklJSdXm1uzcuVNRUVFq3ry5goKCqq27adMmt14zLy9PKSkprudn5sYMGjRImZmZKioqchUd6fRtH1auXKnRo0drzpw5uuKKK/Tyyy9zGjgAAHBxu9z83pWDjejevbucTuc5l2dmZta4zZmLCQIAAPxfbpebSZMm+TIHAACAVxiacwMAAOCvDJ0K3rhx4xqvbWOz2RQaGqqrrrpK999/vwYPHlzrgAAAAJ4wVG4mTpyoqVOnKjU1VTfccIMk6ZtvvtHHH3+sv//97yooKNDIkSN16tQpDRs2zKuBAQAAzsdQuVm3bp2mTJmiESNGVBtftGiRPvnkE7377rtq27at5s6dS7kBAAAXlKE5N2vWrDnrBpaS1LNnT61Zs0aS1KdPH5/dYwoAAOBcDJWbyMhIrVix4qzxFStWuK5cXF5eroYNG9YuHQAAgIcMHZaaMGGCRo4cqezsbNecm3/9619atWqVFi5cKOn0bRG6devmvaQAAABuMFRuhg0bpjZt2mjevHlavny5JKlVq1b6/PPPddNNN0mSHnvsMe+lBAAAcJOhciNJXbp0ueB3BQcAAPg9bpebkpIShYeHu34+nzPrAQAAXGhul5vGjRurqKhITZo0UaNGjWq8iJ/T6ZTNZlNVVZVXQwIAALjL7XKzdu1a15lQ2dnZPgtkJQHHj5odAfArfCcAXAhul5vfnvnEWVDuCSvINTsCAAAXHcMTir/44gstWrRIu3fv1ttvv61mzZrpn//8p+Lj49W1a1dvZqyzjscnyxHWyOwYgN8IOH6U0g/A5wyVm3fffVf33XefBgwYoE2bNqmyslKSVFxcrGeffVarVq3yasi6yhHWSI4Gl5kdAwCAi4qhKxRPmTJFCxcu1EsvvaSgoCDXeJcuXbRp0yavhQMAAPCUoXLz/fffKzk5+azxiIgIHT16tLaZAAAADDNUbqKjo7Vz586zxtetW6cWLVrUOhQAAIBRhsrNsGHDNGrUKG3YsEE2m0379u3TG2+8oTFjxmjkyJHezggAAOA2jyYUFxQUKD4+XmPHjpXD4VDPnj117NgxJScnKyQkRGPGjNHDDz/sq6wAAAC/y6Nyc+WVVyouLk4pKSlKSUnRtm3bVFpaqrKyMrVp00aXXHKJr3ICgF8JqCg2OwLgV/zpO+FRuVm7dq1ycnKUk5Ojt956SydOnFCLFi3Uo0cP9ejRQ927d1dUVJSvsgKA6SIiIhQUHCLt/tzsKIDfCQoOUUREhNkxPCs33bt3V/fu3SVJFRUV+uqrr1xl59VXX9XJkyfVunVrbd261RdZAcB0UVFRev2fr6m42H/+l3qx2rNnj6ZOnapx48YpLi7O7DjQ6fLvDzs5DF+hODQ0VD169FDXrl2VkpKi1atXa9GiRdq+fbs38wGA34mKivKLf8BxWlxcnBISEsyOAT/icbk5ceKEvv76a2VnZysnJ0cbNmxQbGyskpOTNW/ePO47BQAATOVRuenRo4c2bNig+Ph4devWTQ888IDefPNNxcTE+CofAACARzwqN1988YViYmJck4e7deumSy+91FfZAAAAPObRRfyOHj2qjIwM1a9fX88//7yaNm2qxMREPfTQQ3rnnXd06NAhX+UEAABwi0d7bho0aKBbb71Vt956qySptLRU69atU3Z2tqZPn64BAwaoZcuW+u6773wSFgAA4PcYuv3CGQ0aNFBkZKQiIyPVuHFj1atXT9u2bfNWNgAAAI95tOfG4XAoLy9POTk5ys7O1pdffqny8nI1a9ZMKSkpmj9/vlJSUnyVFQAA4Hd5VG4aNWqk8vJyRUdHKyUlRbNnz1b37t115ZVX+iofAACARzwqNzNmzFBKSgoXSwIAAH7Lo3LzwAMP+CoHAACAV9RqQjEAAIC/odwAAABLMXzjTABA3VVRUaHCwkKzY9TKnj17qv1Z19ntdoWGhpodwxIoNwBwESosLNTw4cPNjuEVU6dONTuCV2RkZHDCjpdQbnwooKLY7AiAX+E74T/sdrsyMjLMjoHfsNvtZkewDMqND0RERCgoOETa/bnZUQC/ExQcooiICLNjXPRCQ0PZSwDLotz4QFRUlF7/52sqLuZ/qWbbs2ePpk6dqnHjxikuLs7sONDp8h8VFWV2DAAWRrnxkaioKP4B9yNxcXH8LxUALhKcCg4AACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACyFcgMAACzFL8rN/Pnz1bx5c4WGhqpTp0765ptvzrv+Cy+8oFatWiksLEyxsbEaPXq0KioqLlBaAADgz0wvN0uXLlVaWpomTZqkTZs2qV27durdu7cOHjxY4/pvvvmmxo4dq0mTJmnbtm36n//5Hy1dulT//d//fYGTAwAAf2R6uZk1a5aGDRumwYMHq02bNlq4cKHq16+vV155pcb1v/rqK3Xp0kX9+/dX8+bNdcstt+jee+/93b09AADg4mBquTlx4oQ2btyoXr16ucYCAgLUq1cvrV+/vsZtbrrpJm3cuNFVZnbv3q1Vq1apT58+Na5fWVmpkpKSag8AAGBd9cx888OHD6uqqkpRUVHVxqOiorR9+/Yat+nfv78OHz6srl27yul06tSpUxoxYsQ5D0tNmzZNkydP9np2AADgn0w/LOWpnJwcPfvss1qwYIE2bdqk5cuXa+XKlXrmmWdqXD89PV3FxcWux969ey9wYgAAcCGZuufmsssuU2BgoA4cOFBt/MCBA4qOjq5xmwkTJui+++7T0KFDJUmJiYkqLy/X8OHDNW7cOAUEVO9rISEhCgkJ8c0HAAAAfsfUPTfBwcG67rrrlJWV5RpzOBzKyspS586da9zm2LFjZxWYwMBASZLT6fRdWAAAUCeYuudGktLS0jRo0CB17NhRN9xwg1544QWVl5dr8ODBkqSBAweqWbNmmjZtmiSpb9++mjVrlpKSktSpUyft3LlTEyZMUN++fV0lBwAAXLxMLzf33HOPDh06pIkTJ2r//v1q3769Pv74Y9ck48LCwmp7asaPHy+bzabx48fr559/1uWXX66+fftq6tSpZn0EAADgR2zOi+xYTklJiSIiIlRcXKzw8HCz48DHduzYoeHDhysjI0MJCQlmxwEAGOTJ7+86d7YUAADA+VBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApVBuAACApdQzOwD8V0VFhQoLC82OUSt79uyp9mddZ7fbFRoaanYMAPBrlBucU2FhoYYPH252DK+YOnWq2RG8IiMjQwkJCWbHAAC/RrnBOdntdmVkZJgdA79ht9vNjgAAfo9yg3MKDQ1lLwEAoM5hQjEAALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALAUyg0AALCUi+6u4E6nU5JUUlJichIAAOCuM7+3z/weP5+LrtyUlpZKkmJjY01OAgAAPFVaWqqIiIjzrmNzulOBLMThcGjfvn1q2LChbDab2XHgYyUlJYqNjdXevXsVHh5udhwAXsT3++LidDpVWlqqpk2bKiDg/LNqLro9NwEBAbriiivMjoELLDw8nH/8AIvi+33x+L09NmcwoRgAAFgK5QYAAFgK5QaWFhISokmTJikkJMTsKAC8jO83zuWim1AMAACsjT03AADAUig3AADAUig3AADAUig3AADAUig3qPPmz5+v5s2bKzQ0VJ06ddI333xz3vXffvtttW7dWqGhoUpMTNSqVasuUFIAnsjNzVXfvn3VtGlT2Ww2vf/++7+7TU5Ojjp06KCQkBBdddVVyszM9HlO+B/KDeq0pUuXKi0tTZMmTdKmTZvUrl079e7dWwcPHqxx/a+++kr33nuvhgwZos2bN6tfv37q16+fvvvuuwucHMDvKS8vV7t27TR//ny31i8oKNBtt92mlJQU5efn69FHH9XQoUO1Zs0aHyeFv+FUcNRpnTp10vXXX6958+ZJOn3vsNjYWD388MMaO3bsWevfc889Ki8v10cffeQau/HGG9W+fXstXLjwguUG4Bmbzab33ntP/fr1O+c6Tz75pFauXFntPyt/+ctfdPToUX388ccXICX8BXtuUGedOHFCGzduVK9evVxjAQEB6tWrl9avX1/jNuvXr6+2viT17t37nOsDqDv4fuMMyg3qrMOHD6uqqkpRUVHVxqOiorR///4at9m/f79H6wOoO871/S4pKdHx48dNSgUzUG4AAIClUG5QZ1122WUKDAzUgQMHqo0fOHBA0dHRNW4THR3t0foA6o5zfb/Dw8MVFhZmUiqYgXKDOis4OFjXXXedsrKyXGMOh0NZWVnq3Llzjdt07ty52vqS9Omnn55zfQB1B99vnEG5QZ2Wlpaml156Sa+++qq2bdumkSNHqry8XIMHD5YkDRw4UOnp6a71R40apY8//lgzZ87U9u3b9dRTTykvL08PPfSQWR8BwDmUlZUpPz9f+fn5kk6f6p2fn6/CwkJJUnp6ugYOHOhaf8SIEdq9e7eeeOIJbd++XQsWLNCyZcs0evRoM+LDTE6gjnvxxReddrvdGRwc7LzhhhucX3/9tWtZt27dnIMGDaq2/rJly5wJCQnO4OBg5zXXXONcuXLlBU4MwB3Z2dlOSWc9znynBw0a5OzWrdtZ27Rv394ZHBzsbNGihXPx4sUXPDfMx3VuAACApXBYCgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBoBfu//++9WvXz+zYwCoQyg3ACzh5MmTZkcA4CcoNwD8wjvvvKPExESFhYXp0ksvVa9evfT444/r1Vdf1QcffCCbzSabzaacnBz9+OOPstlsWrp0qbp166bQ0FC98cYbkqSXX35ZV199tUJDQ9W6dWstWLDA9R4nTpzQQw89pJiYGIWGhiouLk7Tpk2TJDmdTj311FOy2+0KCQlR06ZN9cgjj5jydwGgduqZHQAAioqKdO+992r69Om64447VFpaqi+++EIDBw5UYWGhSkpKtHjxYklSZGSk9u3bJ0kaO3asZs6cqaSkJFfBmThxoubNm6ekpCRt3rxZw4YNU4MGDTRo0CDNnTtXH374oZYtWya73a69e/dq7969kqR3331Xs2fP1pIlS3TNNddo//79+ve//23a3wkA4yg3AExXVFSkU6dO6c4771RcXJwkKTExUZIUFhamyspKRUdHn7Xdo48+qjvvvNP1fNKkSZo5c6ZrLD4+Xv/5z3+0aNEiDRo0SIWFhWrZsqW6du0qm83mei9JKiwsVHR0tHr16qWgoCDZ7XbdcMMNvvzYAHyEw1IATNeuXTv17NlTiYmJuuuuu/TSSy/p119//d3tOnbs6Pq5vLxcu3bt0pAhQ3TJJZe4HlOmTNGuXbsknZ6cnJ+fr1atWumRRx7RJ5984tr+rrvu0vHjx9WiRQsNGzZM7733nk6dOuX9DwvA5yg3AEwXGBioTz/9VKtXr1abNm304osvqlWrViooKDjvdg0aNHD9XFZWJkl66aWXlJ+f73p89913+vrrryVJHTp0UEFBgZ555hkdP35cd999t/785z9LkmJjY/X9999rwYIFCgsL04MPPqjk5GQmKgN1kM3pdDrNDgEAv1VVVaW4uDilpaVp+/btKioq0ooVK1zLf/zxR8XHx2vz5s1q3769a7xZs2YaMWKEJkyY4Nb7rFmzRrfeeqt++eUXRUZGVlv2/fffq3Xr1tq4caM6dOjglc8F4MJgzg0A023YsEFZWVm65ZZb1KRJE23YsEGHDh3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}, "metadata": {} } ], "source": [ "#ttest for between factor\n", "x = grouped[(grouped[\"stress\"]==0)][\"Weighted_Mean_Gradient\"]\n", "y = grouped[(grouped[\"stress\"]==1)][\"Weighted_Mean_Gradient\"]\n", "display(pg.ttest(x,y, paired=False))\n", "sns.boxplot(data=grouped, x=\"stress\", y=\"Weighted_Mean_Gradient\")\n", "print(\"WMG under accuracy condition:\", x.mean())\n", "print(\"WMG under speeded condition:\", y.mean())\n", "mean_diff = x.mean() - y.mean()\n", "print(\"mean difference:\", mean_diff)\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "-11Ct5k6ov_-", "outputId": "e8a3251a-35e0-4b04-a4be-ba8066303b85", "colab": { "base_uri": "https://localhost:8080/", "height": 565 } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ " T dof alternative p-val CI95% cohen-d BF10 \\\n", "T-test 1.540985 9 two-sided 0.157709 [-0.06, 0.31] 0.627568 0.766 \n", "\n", " power \n", "T-test 0.425952 " ], "text/html": [ "\n", "
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Tdofalternativep-valCI95%cohen-dBF10power
T-test1.5409859two-sided0.157709[-0.06, 0.31]0.6275680.7660.425952
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0KXecn5+fyyRoAABgbYZuv1C3bl3dfffdVxzXsWNHrV692uOHgt5//32NGTNGH3zwQbnzfwAAwNXJrcNS7vrpp590/vx5j77me++9p1GjRum9997jVg8AAKAUQ3tuPKWgoMDl1PLDhw8rIyNDwcHBCg8PV2xsrH755RctXbpU0sVDUSNHjtSCBQvUrVs3HT9+XJIUEBCgoKAgUz4DAACoWap0z82VpKWlKSoqynka96RJkxQVFaVnn31W0sWLBmZlZTnHJyYm6sKFC3rssccUGhrqfEyYMMGU/AAAoOYxdc9Nnz595HA4yl2flJTk8jw1NbVqAwEAgFrP1D03AAAAnka5AQAAllKlh6Vee+01hYSEVOVb1Fg5OTlu3UQUVePIkSMuf8J8QUFBV+2/CwCqh81xuUkvl5GcnKzk5GSdOHFCdrvdZd2bb77pkXBVIS8vT0FBQcrNzVVgYGCVvEdOTo4eeHCEzhefu/Jg4Crj4+und95eSsEB4BZ3fn8b2nMza9YsPffcc+ratatCQ0Nls9kMBbWq3NxcnS8+p7Ote8vuzynqwCVeRbnSoY3Kzc2l3JisqKjI5WxUmC88PFz+/v5mx7AEQ+UmISFBSUlJevDBBz2dx1Ls/kGy12tsdgwAKCUrK0tjx441Owb+IDExUZGRkWbHsARD5aa4uFg9evTwdBYAQDUJDw9XYmKi2TEq5ciRI4qLi9O0adMUERFhdpxKCw8PNzuCZRgqN2PGjNG7776rGTNmeDoPAKAa+Pv7W2YvQUREhGU+CzzDULkpKipSYmKivvrqK3Xq1Ek+Pj4u6+fNm+eRcAAAAO4yVG527dqlLl26SJL27Nnjso7JxQAAwEyGyk1KSoqncwAAAHgEVygGAACWYvgKxWlpaVqxYoWysrJUXFzssm7lypWVDgYAAGCEoT0377//vnr06KG9e/fq448/1vnz55WZmakNGzYoKIiL1gEAAPMYKjcvvPCC5s+fr88//1y+vr5asGCB9u3bp/vuu4/z9AEAgKkMlZuDBw/qzjvvlCT5+vqqsLBQNptNEydOrPUXhQIAALWboXLTqFEj5efnS5KaN2/uPB389OnTOnPmjOfSAQAAuMnQhOJevXpp/fr16tixo+69915NmDBBGzZs0Pr169WvXz9PZwQAAKgwQ+Xm1VdfVVFRkSRp2rRp8vHx0datW/W3v/1N06dP92hAAAAAdxgqN8HBwc6/e3l5aerUqR4LBAAAUBmGL+J38OBBTZ8+XUOHDtWJEyckSV9++aUyMzM9Fg4AAMBdhsrNxo0b1bFjR23fvl0rV65UQUGBJOn777/XzJkzPRoQAADAHYbKzdSpUzV79mytX79evr6+zuV9+/bVN99847FwAAAA7jJUbnbv3q2777671PImTZro5MmTlQ4FAABglKFy07BhQ2VnZ5davnPnTjVv3rzSoQAAAIwyVG7+/ve/65lnntHx48dls9lkt9u1ZcsWTZkyRSNGjPB0RgAAgAozfG+p6667TmFhYSooKFD79u3Vq1cv9ejRg+vcAAAAUxm6zo2vr69ef/11zZgxQ3v27FFBQYGioqLUtm1bT+cDAABwi6Fyc0l4eDh3AQcAADWKW+Xmueeeq9C4Z5991lAYAACAynKr3Pzzn/9Us2bN1KRJEzkcjjLH2Gw2yg0AADCNW+UmJiZGGzZsUNeuXfWPf/xDf/nLX+TlZfgODgAAAB7nVjNZtWqVDh48qG7duumpp55S8+bN9cwzz+iHH36oqnwAAABucXu3S7NmzRQbG6sffvhBy5cv14kTJ3TTTTepZ8+eOnv2bFVkBAAAqLBKnS1100036aefftL//u//aufOnTp//rwCAgI8lQ0AAMBthibMbNu2TQ8//LCaNm2qV155RSNHjtSxY8cUGBjo6XwAAABucWvPzUsvvaSkpCSdPHlSw4cP19dff61OnTpVVTYAAAC3uVVupk6dqvDwcN13332y2WxKSkoqc9y8efM8kQ0AAMBtbpWbXr16yWazKTMzs9wxNput0qEAAACMcqvcpKamVlEMAAAAz6jSK/AFBgbq0KFDVfkWAAAALip1KviVlHeLBgCozXJycpSbm2t2jKvekSNHXP6E+YKCghQSEmJ2jKotNwBgNTk5OXrgwRE6X3zO7Cj4l7i4OLMj4F98fP30zttLTS84lBsAcENubq7OF5/T2da9ZfcPMjsOUGN4FeVKhzYqNzeXcgMAtZHdP0j2eo3NjgGgDFU6oZjTwgEAQHWr0nLDhGIAAFDdqrTcfPnll2revHlVvgUAAICLCs+5mTRpUoVf9NLtF2699Vb3EwEAAFRChcvNzp07XZ6np6frwoULateunSRp//798vb21o033ujZhAAAAG6ocLlJSUlx/n3evHlq0KCBlixZokaNGkmSfv/9d40aNUq33Xab51MCAABUkKE5N3PnzlV8fLyz2EhSo0aNNHv2bM2dO9dj4QAAANxlqNzk5eXp119/LbX8119/VX5+fqVDAQAAGGWo3Nx9990aNWqUVq5cqZ9//lk///yzPvroI40ePVr33HOPpzMCAABUmKErFCckJGjKlCkaNmyYzp8/f/GF6tTR6NGjNWfOHI8GBAAAcIehclO3bl0tXrxYc+bM0cGDByVJbdq0Ub169TwaDgAAwF2Vuohfdna2srOz1bZtW9WrV48rEgMAANMZKje//fab+vXrp8jISA0aNEjZ2dmSpNGjR2vy5MkeDQgAAOAOQ+Vm4sSJ8vHxUVZWlurWretcfv/992vNmjUeCwcAAOAuQ3Nu1q1bp7Vr16pFixYuy9u2basjR454JBgAAIARhvbcFBYWuuyxueTUqVPy8/OrdCgAAACjDJWb2267TUuXLnU+t9lsstvteumllxQdHe2xcAAAAO4ydFjqpZdeUr9+/ZSWlqbi4mI9/fTTyszM1KlTp7RlyxZPZwQAAKgwQ3tuOnTooP379+vWW2/VX//6VxUWFuqee+7Rzp071aZNG09nBAAAqDBDe26ysrIUFhamadOmlbkuPDy80sEAAACMMLTnplWrVmXeOPO3335Tq1atKh0KAADAKEPlxuFwyGazlVpeUFAgf3//SocCAAAwyq3DUpMmTZJ08eyoGTNmuJwOXlJSou3bt6tLly4Vfr1NmzZpzpw52rFjh7Kzs/Xxxx9ryJAhl90mNTVVkyZNUmZmpsLCwjR9+nQ99NBD7nwMAABgYW6Vm507d0q6uOdm9+7d8vX1da7z9fVV586dNWXKlAq/XmFhoTp37qx//OMfuueee644/vDhw7rzzjs1btw4LVu2TMnJyRozZoxCQ0M1cOBAdz4KAACwKLfKTUpKiiRp1KhRWrBggQIDAyv15jExMYqJianw+ISEBLVq1Upz586VJF1//fXavHmz5s+fT7kBAACSDM65eeuttypdbIzYtm2b+vfv77Js4MCB2rZtW7nbnDt3Tnl5eS4PAABgXYZOBS8sLNR//ud/Kjk5WSdOnJDdbndZf+jQIY+E+7+OHz+ukJAQl2UhISHKy8vT2bNnFRAQUGqb+Ph4zZo1q0ryAACAmsdQuRkzZow2btyoBx98UKGhoWWeOVVTxMbGOidCS1JeXp7CwsJMTAQAAKqSoXLz5ZdfatWqVerZs6en81xW06ZNlZOT47IsJydHgYGBZe61kSQ/Pz9u5gkAwFXE0JybRo0aKTg42NNZrqh79+5KTk52WbZ+/Xp179692rMAAICayVC5ef755/Xss8/qzJkzlXrzgoICZWRkKCMjQ9LFU70zMjKUlZUl6eIhpREjRjjHjxs3TocOHdLTTz+tffv2afHixVqxYoUmTpxYqRwAAMA6KnxYKioqymVuzYEDBxQSEqKWLVvKx8fHZWx6enqFXjMtLU3R0dHO55fmxowcOVJJSUnKzs52Fh3p4m0fVq1apYkTJ2rBggVq0aKF3njjDU4DBwAAThUuN1e6crARffr0kcPhKHd9UlJSmdtcupggAADA/1XhcjNz5syqzGFJXmdPmx0BqFH4TgCoDobOlkLFBBzeZHYEAACuOobKTaNGjcq8to3NZpO/v7+uvfZaPfTQQxo1alSlA9ZmZ1v1kj2godkxgBrD6+xpSj+AKmeo3Dz77LOKi4tTTEyMbr75ZknSt99+qzVr1uixxx7T4cOHNX78eF24cEEPP/ywRwPXJvaAhrLXa2x2DAAAriqGys3mzZs1e/ZsjRs3zmX5a6+9pnXr1umjjz5Sp06dtHDhwqu63AAAgOpn6Do3a9euLXUDS0nq16+f1q5dK0kaNGhQld1jCgAAoDyGyk1wcLA+//zzUss///xz55WLCwsL1aBBg8qlAwAAcJOhw1IzZszQ+PHjlZKS4pxz891332n16tVKSEiQdPG2CL179/ZcUgAAgAowVG4efvhhtW/fXq+++qpWrlwpSWrXrp02btyoHj16SJImT57suZQAAAAVZPg6Nz179qz2u4IDAABcSYXLTV5engIDA51/v5xL4wAAAKpbhctNo0aNlJ2drSZNmqhhw4ZlXsTP4XDIZrOppKTEoyEBAAAqqsLlZsOGDc4zoVJSUqosEAAAQGVUuNz88cwnzoICAAA1laHr3EjS119/rQceeEA9evTQL7/8Ikl6++23tXnzZo+FAwAAcJehcvPRRx9p4MCBCggIUHp6us6dOydJys3N1QsvvODRgAAAAO4wVG5mz56thIQEvf766/Lx8XEu79mzp9LT0z0WDgAAwF2Gys0PP/ygXr16lVoeFBSk06dPVzYTAACAYYbKTdOmTXXgwIFSyzdv3qzWrVtXOhQAAIBRhsrNww8/rAkTJmj79u2y2Ww6duyYli1bpilTpmj8+PGezggAAFBhbt1+4fDhw2rVqpWmTp0qu92ufv366cyZM+rVq5f8/Pw0ZcoUPfHEE1WVFQBqDK+zp82OANQoNek74Va5adOmjSIiIhQdHa3o6Gjt3btX+fn5KigoUPv27VW/fv2qygkANUrA4U1mRwBQDrfKzYYNG5SamqrU1FS99957Ki4uVuvWrdW3b1/17dtXffr0UUhISFVlrXW8inLNjgDUKFb6Tpxt1Uv2gIZmxwBqDK+zp2tM6Xer3PTp00d9+vSRJBUVFWnr1q3OsrNkyRKdP39e1113nTIzM6sia60RFBQkH18/6dBGs6MANY6Pr5+CgoLMjlFp9oCGstdrbHYMAGVwq9z8kb+/v/r27atbb71V0dHR+vLLL/Xaa69p3759nsxXK4WEhOidt5cqN9c6/0utrY4cOaK4uDhNmzZNERERZseBLpZ/9vACqEpul5vi4mJ98803SklJUWpqqrZv366wsDD16tVLr776Kved+peQkBD+Aa9BIiIiFBkZaXYMAEA1cKvc9O3bV9u3b1erVq3Uu3dvPfLII3r33XcVGhpaVfkAAADc4la5+frrrxUaGuqcPNy7d2/96U9/qqpsAAAAbnPrIn6nT59WYmKi6tatqxdffFHNmjVTx44d9fjjj+vDDz/Ur7/+WlU5AQAAKsStPTf16tXTHXfcoTvuuEOSlJ+fr82bNyslJUUvvfSShg8frrZt22rPnj1VEhYAAOBKDN1+4ZJ69eopODhYwcHBatSokerUqaO9e/d6KhsAAIDb3NpzY7fblZaWptTUVKWkpGjLli0qLCxU8+bNFR0drUWLFik6OrqqsgIAAFyRW+WmYcOGKiwsVNOmTRUdHa358+erT58+atOmTVXlAwAAcItb5WbOnDmKjo7meiEAAKDGcqvcPPLII1WVAwAAwCMqNaEYAACgpqHcAAAAS6HcAAAAS6HcAAAAS3H7ruAAAMmrKNfsCECNUpO+E5QbAHBDUFCQfHz9pEMbzY4C1Dg+vn4KCgoyOwblBgDcERISonfeXqrc3Jrzv9Sr1ZEjRxQXF6dp06YpIiLC7DjQxfIfEhJidgzKDQC4KyQkpEb8A46LIiIiuLgsXDChGAAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWArlBgAAWAr3lkK5ioqKlJWVZXaMSjly5IjLn7VdeHi4/P39zY4BADUa5QblysrK0tixY82O4RFxcXFmR/CIxMREbhAIAFdAuUG5wsPDlZiYaHYM/EF4eLjZEQCgxqPcoFz+/v7sJQAA1DpMKAYAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZCuQEAAJZSI8rNokWL1LJlS/n7+6tbt2769ttvLzv+5ZdfVrt27RQQEKCwsDBNnDhRRUVF1ZQWAADUZKaXm+XLl2vSpEmaOXOm0tPT1blzZw0cOFAnTpwoc/y7776rqVOnaubMmdq7d6/+53/+R8uXL9d//Md/VHNyAABQE5lebubNm6eHH35Yo0aNUvv27ZWQkKC6devqzTffLHP81q1b1bNnTw0bNkwtW7bUgAEDNHTo0Cvu7QEAAFcHU8tNcXGxduzYof79+zuXeXl5qX///tq2bVuZ2/To0UM7duxwlplDhw5p9erVGjRoUJnjz507p7y8PJcHAACwrjpmvvnJkydVUlKikJAQl+UhISHat29fmdsMGzZMJ0+e1K233iqHw6ELFy5o3Lhx5R6Wio+P16xZszyeHQAA1EymH5ZyV2pqql544QUtXrxY6enpWrlypVatWqXnn3++zPGxsbHKzc11Po4ePVrNiQEAQHUydc9N48aN5e3trZycHJflOTk5atq0aZnbzJgxQw8++KDGjBkjSerYsaMKCws1duxYTZs2TV5ern3Nz89Pfn5+VfMBAABAjWPqnhtfX1/deOONSk5Odi6z2+1KTk5W9+7dy9zmzJkzpQqMt7e3JMnhcFRdWAAAUCuYuudGkiZNmqSRI0eqa9euuvnmm/Xyyy+rsLBQo0aNkiSNGDFCzZs3V3x8vCRp8ODBmjdvnqKiotStWzcdOHBAM2bM0ODBg50lBwAAXL1MLzf333+/fv31Vz377LM6fvy4unTpojVr1jgnGWdlZbnsqZk+fbpsNpumT5+uX375Rddcc40GDx6suLg4sz4CAACoQWyOq+xYTl5enoKCgpSbm6vAwECz4wAADNq/f7/Gjh2rxMRERUZGmh0HVcyd39+17mwpAACAy6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6HcAAAAS6ljdgAAQPUrKipSVlaW2TEq5ciRIy5/1nbh4eHy9/c3O4YlUG4A4CqUlZWlsWPHmh3DI+Li4syO4BGJiYmKjIw0O4YlUG4A4CoUHh6uxMREs2PgD8LDw82OYBmUGwC4Cvn7+7OXAJbFhGIAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGAplBsAAGApV91dwR0OhyQpLy/P5CQAAKCiLv3evvR7/HKuunKTn58vSQoLCzM5CQAAcFd+fr6CgoIuO8bmqEgFshC73a5jx46pQYMGstlsZsdBFcvLy1NYWJiOHj2qwMBAs+MA8CC+31cXh8Oh/Px8NWvWTF5el59Vc9XtufHy8lKLFi3MjoFqFhgYyD9+gEXx/b56XGmPzSVMKAYAAJZCuQEAAJZCuYGl+fn5aebMmfLz8zM7CgAP4/uN8lx1E4oBAIC1secGAABYCuUGAABYCuUGAABYCuUGAABYCuUGtd6iRYvUsmVL+fv7q1u3bvr2228vO/6DDz7QddddJ39/f3Xs2FGrV6+upqQA3LFp0yYNHjxYzZo1k81m0yeffHLFbVJTU3XDDTfIz89P1157rZKSkqo8J2oeyg1qteXLl2vSpEmaOXOm0tPT1blzZw0cOFAnTpwoc/zWrVs1dOhQjR49Wjt37tSQIUM0ZMgQ7dmzp5qTA7iSwsJCde7cWYsWLarQ+MOHD+vOO+9UdHS0MjIy9OSTT2rMmDFau3ZtFSdFTcOp4KjVunXrpptuukmvvvqqpIv3DgsLC9MTTzyhqVOnlhp///33q7CwUF988YVz2S233KIuXbooISGh2nIDcI/NZtPHH3+sIUOGlDvmmWee0apVq1z+s/L3v/9dp0+f1po1a6ohJWoK9tyg1iouLtaOHTvUv39/5zIvLy/1799f27ZtK3Obbdu2uYyXpIEDB5Y7HkDtwfcbl1BuUGudPHlSJSUlCgkJcVkeEhKi48ePl7nN8ePH3RoPoPYo7/udl5ens2fPmpQKZqDcAAAAS6HcoNZq3LixvL29lZOT47I8JydHTZs2LXObpk2bujUeQO1R3vc7MDBQAQEBJqWCGSg3qLV8fX114403Kjk52bnMbrcrOTlZ3bt3L3Ob7t27u4yXpPXr15c7HkDtwfcbl1BuUKtNmjRJr7/+upYsWaK9e/dq/PjxKiws1KhRoyRJI0aMUGxsrHP8hAkTtGbNGs2dO1f79u3TP//5T6Wlpenxxx836yMAKEdBQYEyMjKUkZEh6eKp3hkZGcrKypIkxcbGasSIEc7x48aN06FDh/T0009r3759Wrx4sVasWKGJEyeaER9mcgC13CuvvOIIDw93+Pr6Om6++WbHN99841zXu3dvx8iRI13Gr1ixwhEZGenw9fV1/PnPf3asWrWqmhMDqIiUlBSHpFKPS9/pkSNHOnr37l1qmy5dujh8fX0drVu3drz11lvVnhvm4zo3AADAUjgsBQAALIVyAwAALIVyAwAALIVyAwAALIVyAwAALIVyAwAALIVyAwAALIVyAwAALIVyA6BKOBwOjR07VsHBwbLZbM5L6Htanz599OSTTzqft2zZUi+//HKVvFd57wmgZqljdgAA1rRmzRolJSUpNTVVrVu3VuPGjc2OBOAqQbkBUCUOHjyo0NBQ9ejRw+woAK4yHJYC4HEPPfSQnnjiCWVlZclms6lly5ay2+2Kj49Xq1atFBAQoM6dO+vDDz902W7Pnj2KiYlR/fr1FRISogcffFAnT550ri8sLNSIESNUv359hYaGau7cuWW+f35+voYOHap69eqpefPmWrRokcv6efPmqWPHjqpXr57CwsL06KOPqqCgwGXMli1b1KdPH9WtW1eNGjXSwIED9fvvv5f5fqtWrVJQUJCWLVtm5McFwMMoNwA8bsGCBXruuefUokULZWdn67vvvlN8fLyWLl2qhIQEZWZmauLEiXrggQe0ceNGSdLp06fVt29fRUVFKS0tTWvWrFFOTo7uu+8+5+s+9dRT2rhxoz799FOtW7dOqampSk9PL/X+c+bMUefOnbVz505NnTpVEyZM0Pr1653rvby8tHDhQmVmZmrJkiXasGGDnn76aef6jIwM9evXT+3bt9e2bdu0efNmDR48WCUlJaXe691339XQoUO1bNkyDR8+3JM/RgBGmXxXcgAWNX/+fEdERITD4XA4ioqKHHXr1nVs3brVZczo0aMdQ4cOdTgcDsfzzz/vGDBggMv6o0ePOiQ5fvjhB0d+fr7D19fXsWLFCuf63377zREQEOCYMGGCc1lERITjjjvucHmd+++/3xETE1Nu1g8++MDxpz/9yfl86NChjp49e5Y7vnfv3o4JEyY4Xn31VUdQUJAjNTW13LEAqh9zbgBUuQMHDujMmTO6/fbbXZYXFxcrKipKkvT9998rJSVF9evXL7X9wYMHdfbsWRUXF6tbt27O5cHBwWrXrl2p8d27dy/1/I9nUH311VeKj4/Xvn37lJeXpwsXLqioqEhnzpxR3bp1lZGRoXvvvfeyn+nDDz/UiRMntGX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}, "metadata": {} } ], "source": [ "#ttest for within factor\n", "x = grouped[(grouped[\"feedback\"]==0)][\"Weighted_Mean_Gradient\"]\n", "y = grouped[(grouped[\"feedback\"]==1)][\"Weighted_Mean_Gradient\"]\n", "display(pg.ttest(x,y, paired=True))\n", "sns.boxplot(data=grouped, x=\"feedback\", y=\"Weighted_Mean_Gradient\")\n", "print(\"WMG under no-feedback condition:\", x.mean())\n", "print(\"WMG under feedback condition:\", y.mean())\n", "mean_diff = x.mean() - y.mean()\n", "print(\"mean difference:\", mean_diff)" ] }, { "cell_type": "markdown", "metadata": { "id": "6aoHiSz9ov_-" }, "source": [ "## 3. ANALYSIS TAKING ACCOUNT OF BLOCK NUMBER" ] }, { "cell_type": "markdown", "metadata": { "id": "voomIL3pov_-" }, "source": [ "### FOR BETWEEN FACTOR AND BLOCK" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3X8_8HFGov__" }, "outputs": [], "source": [ "#create new data frame with response ratios using the dataframe called new_df01 with your data you generated in the section 1\n", "\n", "\"\"\"\"\n", "Create a pivot table to calculate the counts of 'b' and 'n' for each group\n", "\n", "we need to make the counts for each participant at each duration at each condition,\n", "so edit the code to contain your independent variables\n", "my independent variables were 'stress' and the number of the block.\n", "keep the variables 'duration' and 'participant' in the list\n", "\"\"\"\n", "\n", "pivot_table = new_df01.pivot_table(index=['duration', 'participant','stress', 'Blocks.thisTrialN'], columns='key_respBorN.keys',\n", " aggfunc='size', fill_value=0)\n", "\n", "# Calculate the Response Ratio (count of 'b' / count of 'n') for each group\n", "pivot_table['Response_Ratio'] = pivot_table['b'] / (pivot_table['b'] + pivot_table['n'])\n", "\n", "# Reset the index to have a new dataframe with the specified columns\n", "result_df01 = pivot_table.reset_index()\n", "\n", "# If you want to replace any NaN values with 0, you can use fillna method\n", "result_df01.fillna(0, inplace=True)\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#result_df01.to_csv(\"response_ratios.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false, "id": "ly7SOELeov__" }, "outputs": [], "source": [ "#rename Blocks.thisTrialN to 'block'\n", "result_df01.rename(columns={'Blocks.thisTrialN': 'block'}, inplace=True)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8LLIvHlSov__", "outputId": "2ce8a950-be13-40b0-9ef4-ed1227ad5eae" }, "outputs": [ { "data": { "text/html": [ "
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key_respBorN.keysdurationfeedbackparticipantstressBlocks.thisTrialNbnResponse_RatioWeighted_Sum
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427694.0 1.0 2.0 \n", "1725 1.2 1.0 427694.0 1.0 3.0 \n", "1726 1.2 1.0 427694.0 1.0 4.0 \n", "1727 1.2 1.0 427694.0 1.0 5.0 \n", "1728 1.2 1.0 475207.0 0.0 0.0 \n", "1729 1.2 1.0 475207.0 0.0 1.0 \n", "1730 1.2 1.0 475207.0 0.0 2.0 \n", "1731 1.2 1.0 475207.0 0.0 3.0 \n", "1732 1.2 1.0 475207.0 0.0 4.0 \n", "1733 1.2 1.0 475207.0 0.0 5.0 \n", "1734 1.2 1.0 572697.0 1.0 0.0 \n", "1735 1.2 1.0 572697.0 1.0 1.0 \n", "1736 1.2 1.0 572697.0 1.0 2.0 \n", "1737 1.2 1.0 572697.0 1.0 3.0 \n", "1738 1.2 1.0 572697.0 1.0 4.0 \n", "1739 1.2 1.0 572697.0 1.0 5.0 \n", "1740 1.2 1.0 573178.0 1.0 0.0 \n", "1741 1.2 1.0 573178.0 1.0 1.0 \n", "1742 1.2 1.0 573178.0 1.0 2.0 \n", "1743 1.2 1.0 573178.0 1.0 3.0 \n", "1744 1.2 1.0 573178.0 1.0 4.0 \n", "1745 1.2 1.0 573178.0 1.0 5.0 \n", "1746 1.2 1.0 590401.0 0.0 0.0 \n", "1747 1.2 1.0 590401.0 0.0 1.0 \n", "1748 1.2 1.0 590401.0 0.0 2.0 \n", "1749 1.2 1.0 590401.0 0.0 3.0 \n", "1750 1.2 1.0 590401.0 0.0 4.0 \n", "1751 1.2 1.0 590401.0 0.0 5.0 \n", "1752 1.2 1.0 612161.0 1.0 0.0 \n", "1753 1.2 1.0 612161.0 1.0 1.0 \n", "1754 1.2 1.0 612161.0 1.0 2.0 \n", "1755 1.2 1.0 612161.0 1.0 3.0 \n", "1756 1.2 1.0 612161.0 1.0 4.0 \n", "1757 1.2 1.0 612161.0 1.0 5.0 \n", "1758 1.2 1.0 661427.0 1.0 0.0 \n", "1759 1.2 1.0 661427.0 1.0 1.0 \n", "1760 1.2 1.0 661427.0 1.0 2.0 \n", "1761 1.2 1.0 661427.0 1.0 3.0 \n", "1762 1.2 1.0 661427.0 1.0 4.0 \n", "1763 1.2 1.0 661427.0 1.0 5.0 \n", "1764 1.2 1.0 706068.0 1.0 0.0 \n", "1765 1.2 1.0 706068.0 1.0 1.0 \n", "1766 1.2 1.0 706068.0 1.0 2.0 \n", "1767 1.2 1.0 706068.0 1.0 3.0 \n", "1768 1.2 1.0 706068.0 1.0 4.0 \n", "1769 1.2 1.0 706068.0 1.0 5.0 \n", "1770 1.2 1.0 716849.0 1.0 0.0 \n", "1771 1.2 1.0 716849.0 1.0 1.0 \n", "1772 1.2 1.0 716849.0 1.0 2.0 \n", "1773 1.2 1.0 716849.0 1.0 3.0 \n", "1774 1.2 1.0 716849.0 1.0 4.0 \n", "1775 1.2 1.0 716849.0 1.0 5.0 \n", "1776 1.2 1.0 763498.0 0.0 0.0 \n", "1777 1.2 1.0 763498.0 0.0 1.0 \n", "1778 1.2 1.0 763498.0 0.0 2.0 \n", "1779 1.2 1.0 763498.0 0.0 3.0 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1.0 1.0 \n", "1808 1.2 1.0 906239.0 1.0 2.0 \n", "1809 1.2 1.0 906239.0 1.0 3.0 \n", "1810 1.2 1.0 906239.0 1.0 4.0 \n", "1811 1.2 1.0 906239.0 1.0 5.0 \n", "1812 1.2 1.0 909153.0 1.0 0.0 \n", "1813 1.2 1.0 909153.0 1.0 1.0 \n", "1814 1.2 1.0 909153.0 1.0 2.0 \n", "1815 1.2 1.0 909153.0 1.0 3.0 \n", "1816 1.2 1.0 909153.0 1.0 4.0 \n", "1817 1.2 1.0 909153.0 1.0 5.0 \n", "1818 1.2 1.0 934835.0 0.0 0.0 \n", "1819 1.2 1.0 934835.0 0.0 1.0 \n", "1820 1.2 1.0 934835.0 0.0 2.0 \n", "1821 1.2 1.0 934835.0 0.0 3.0 \n", "1822 1.2 1.0 934835.0 0.0 4.0 \n", "1823 1.2 1.0 934835.0 0.0 5.0 \n", "1824 1.2 1.0 954504.0 0.0 0.0 \n", "1825 1.2 1.0 954504.0 0.0 1.0 \n", "1826 1.2 1.0 954504.0 0.0 2.0 \n", "1827 1.2 1.0 954504.0 0.0 3.0 \n", "1828 1.2 1.0 954504.0 0.0 4.0 \n", "1829 1.2 1.0 954504.0 0.0 5.0 \n", "1830 1.4 0.0 27171.0 1.0 0.0 \n", "1831 1.4 0.0 27171.0 1.0 1.0 \n", "1832 1.4 0.0 27171.0 1.0 2.0 \n", "1833 1.4 0.0 27171.0 1.0 3.0 \n", "1834 1.4 0.0 27171.0 1.0 4.0 \n", "1835 1.4 0.0 27171.0 1.0 5.0 \n", "1836 1.4 0.0 80551.0 0.0 0.0 \n", "1837 1.4 0.0 80551.0 0.0 1.0 \n", "1838 1.4 0.0 80551.0 0.0 2.0 \n", "1839 1.4 0.0 80551.0 0.0 3.0 \n", "1840 1.4 0.0 80551.0 0.0 4.0 \n", "1841 1.4 0.0 80551.0 0.0 5.0 \n", "1842 1.4 0.0 96604.0 1.0 0.0 \n", "1843 1.4 0.0 96604.0 1.0 1.0 \n", "1844 1.4 0.0 96604.0 1.0 2.0 \n", "1845 1.4 0.0 96604.0 1.0 3.0 \n", "1846 1.4 0.0 96604.0 1.0 4.0 \n", "1847 1.4 0.0 96604.0 1.0 5.0 \n", "1848 1.4 0.0 124367.0 0.0 0.0 \n", "1849 1.4 0.0 124367.0 0.0 1.0 \n", "1850 1.4 0.0 124367.0 0.0 2.0 \n", "1851 1.4 0.0 124367.0 0.0 3.0 \n", "1852 1.4 0.0 124367.0 0.0 4.0 \n", "1853 1.4 0.0 124367.0 0.0 5.0 \n", "1854 1.4 0.0 135784.0 0.0 0.0 \n", "1855 1.4 0.0 135784.0 0.0 1.0 \n", "1856 1.4 0.0 135784.0 0.0 2.0 \n", "1857 1.4 0.0 135784.0 0.0 3.0 \n", "1858 1.4 0.0 135784.0 0.0 4.0 \n", "1859 1.4 0.0 135784.0 0.0 5.0 \n", "1860 1.4 0.0 144507.0 0.0 0.0 \n", "1861 1.4 0.0 144507.0 0.0 1.0 \n", "1862 1.4 0.0 144507.0 0.0 2.0 \n", "1863 1.4 0.0 144507.0 0.0 3.0 \n", "1864 1.4 0.0 144507.0 0.0 4.0 \n", "1865 1.4 0.0 144507.0 0.0 5.0 \n", "1866 1.4 0.0 195972.0 0.0 0.0 \n", "1867 1.4 0.0 195972.0 0.0 1.0 \n", "1868 1.4 0.0 195972.0 0.0 2.0 \n", "1869 1.4 0.0 195972.0 0.0 3.0 \n", "1870 1.4 0.0 195972.0 0.0 4.0 \n", "1871 1.4 0.0 195972.0 0.0 5.0 \n", "1872 1.4 0.0 201266.0 1.0 0.0 \n", "1873 1.4 0.0 201266.0 1.0 1.0 \n", "1874 1.4 0.0 201266.0 1.0 2.0 \n", "1875 1.4 0.0 201266.0 1.0 3.0 \n", "1876 1.4 0.0 201266.0 1.0 4.0 \n", "1877 1.4 0.0 201266.0 1.0 5.0 \n", "1878 1.4 0.0 231280.0 0.0 0.0 \n", "1879 1.4 0.0 231280.0 0.0 1.0 \n", "1880 1.4 0.0 231280.0 0.0 2.0 \n", "1881 1.4 0.0 231280.0 0.0 3.0 \n", "1882 1.4 0.0 231280.0 0.0 4.0 \n", "1883 1.4 0.0 231280.0 0.0 5.0 \n", "1884 1.4 0.0 247059.0 1.0 0.0 \n", "1885 1.4 0.0 247059.0 1.0 1.0 \n", "1886 1.4 0.0 247059.0 1.0 2.0 \n", "1887 1.4 0.0 247059.0 1.0 3.0 \n", "1888 1.4 0.0 247059.0 1.0 4.0 \n", "1889 1.4 0.0 247059.0 1.0 5.0 \n", "1890 1.4 0.0 341031.0 0.0 0.0 \n", "1891 1.4 0.0 341031.0 0.0 1.0 \n", "1892 1.4 0.0 341031.0 0.0 2.0 \n", "1893 1.4 0.0 341031.0 0.0 3.0 \n", "1894 1.4 0.0 341031.0 0.0 4.0 \n", "1895 1.4 0.0 341031.0 0.0 5.0 \n", "1896 1.4 0.0 344553.0 1.0 0.0 \n", "1897 1.4 0.0 344553.0 1.0 1.0 \n", "1898 1.4 0.0 344553.0 1.0 2.0 \n", "1899 1.4 0.0 344553.0 1.0 3.0 \n", "1900 1.4 0.0 344553.0 1.0 4.0 \n", "1901 1.4 0.0 344553.0 1.0 5.0 \n", "1902 1.4 0.0 379479.0 1.0 0.0 \n", "1903 1.4 0.0 379479.0 1.0 1.0 \n", "1904 1.4 0.0 379479.0 1.0 2.0 \n", "1905 1.4 0.0 379479.0 1.0 3.0 \n", "1906 1.4 0.0 379479.0 1.0 4.0 \n", "1907 1.4 0.0 379479.0 1.0 5.0 \n", "1908 1.4 0.0 427694.0 1.0 0.0 \n", "1909 1.4 0.0 427694.0 1.0 1.0 \n", "1910 1.4 0.0 427694.0 1.0 2.0 \n", "1911 1.4 0.0 427694.0 1.0 3.0 \n", "1912 1.4 0.0 427694.0 1.0 4.0 \n", "1913 1.4 0.0 427694.0 1.0 5.0 \n", "1914 1.4 0.0 475207.0 0.0 0.0 \n", "1915 1.4 0.0 475207.0 0.0 1.0 \n", "1916 1.4 0.0 475207.0 0.0 2.0 \n", "1917 1.4 0.0 475207.0 0.0 3.0 \n", "1918 1.4 0.0 475207.0 0.0 4.0 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1.0 2.0 \n", "1947 1.4 0.0 661427.0 1.0 3.0 \n", "1948 1.4 0.0 661427.0 1.0 4.0 \n", "1949 1.4 0.0 661427.0 1.0 5.0 \n", "1950 1.4 0.0 706068.0 1.0 0.0 \n", "1951 1.4 0.0 706068.0 1.0 1.0 \n", "1952 1.4 0.0 706068.0 1.0 2.0 \n", "1953 1.4 0.0 706068.0 1.0 3.0 \n", "1954 1.4 0.0 706068.0 1.0 4.0 \n", "1955 1.4 0.0 706068.0 1.0 5.0 \n", "1956 1.4 0.0 716849.0 1.0 0.0 \n", "1957 1.4 0.0 716849.0 1.0 1.0 \n", "1958 1.4 0.0 716849.0 1.0 2.0 \n", "1959 1.4 0.0 716849.0 1.0 3.0 \n", "1960 1.4 0.0 716849.0 1.0 4.0 \n", "1961 1.4 0.0 716849.0 1.0 5.0 \n", "1962 1.4 0.0 763498.0 0.0 0.0 \n", "1963 1.4 0.0 763498.0 0.0 1.0 \n", "1964 1.4 0.0 763498.0 0.0 2.0 \n", "1965 1.4 0.0 763498.0 0.0 3.0 \n", "1966 1.4 0.0 763498.0 0.0 4.0 \n", "1967 1.4 0.0 763498.0 0.0 5.0 \n", "1968 1.4 0.0 776689.0 0.0 0.0 \n", "1969 1.4 0.0 776689.0 0.0 1.0 \n", "1970 1.4 0.0 776689.0 0.0 2.0 \n", "1971 1.4 0.0 776689.0 0.0 3.0 \n", "1972 1.4 0.0 776689.0 0.0 4.0 \n", "1973 1.4 0.0 776689.0 0.0 5.0 \n", "1974 1.4 0.0 827411.0 0.0 0.0 \n", "1975 1.4 0.0 827411.0 0.0 1.0 \n", "1976 1.4 0.0 827411.0 0.0 2.0 \n", "1977 1.4 0.0 827411.0 0.0 3.0 \n", "1978 1.4 0.0 827411.0 0.0 4.0 \n", "1979 1.4 0.0 827411.0 0.0 5.0 \n", "1980 1.4 0.0 853026.0 0.0 0.0 \n", "1981 1.4 0.0 853026.0 0.0 1.0 \n", "1982 1.4 0.0 853026.0 0.0 2.0 \n", "1983 1.4 0.0 853026.0 0.0 3.0 \n", "1984 1.4 0.0 853026.0 0.0 4.0 \n", "1985 1.4 0.0 853026.0 0.0 5.0 \n", "1986 1.4 0.0 883636.0 1.0 0.0 \n", "1987 1.4 0.0 883636.0 1.0 1.0 \n", "1988 1.4 0.0 883636.0 1.0 2.0 \n", "1989 1.4 0.0 883636.0 1.0 3.0 \n", "1990 1.4 0.0 883636.0 1.0 4.0 \n", "1991 1.4 0.0 883636.0 1.0 5.0 \n", "1992 1.4 0.0 906239.0 1.0 0.0 \n", "1993 1.4 0.0 906239.0 1.0 1.0 \n", "1994 1.4 0.0 906239.0 1.0 2.0 \n", "1995 1.4 0.0 906239.0 1.0 3.0 \n", "1996 1.4 0.0 906239.0 1.0 4.0 \n", "1997 1.4 0.0 906239.0 1.0 5.0 \n", "1998 1.4 0.0 909153.0 1.0 0.0 \n", "1999 1.4 0.0 909153.0 1.0 1.0 \n", "2000 1.4 0.0 909153.0 1.0 2.0 \n", "2001 1.4 0.0 909153.0 1.0 3.0 \n", "2002 1.4 0.0 909153.0 1.0 4.0 \n", "2003 1.4 0.0 909153.0 1.0 5.0 \n", "2004 1.4 0.0 934835.0 0.0 0.0 \n", "2005 1.4 0.0 934835.0 0.0 1.0 \n", "2006 1.4 0.0 934835.0 0.0 2.0 \n", "2007 1.4 0.0 934835.0 0.0 3.0 \n", "2008 1.4 0.0 934835.0 0.0 4.0 \n", "2009 1.4 0.0 934835.0 0.0 5.0 \n", "2010 1.4 0.0 954504.0 0.0 0.0 \n", "2011 1.4 0.0 954504.0 0.0 1.0 \n", "2012 1.4 0.0 954504.0 0.0 2.0 \n", "2013 1.4 0.0 954504.0 0.0 3.0 \n", "2014 1.4 0.0 954504.0 0.0 4.0 \n", "2015 1.4 0.0 954504.0 0.0 5.0 \n", "2016 1.4 1.0 27171.0 1.0 0.0 \n", "2017 1.4 1.0 27171.0 1.0 1.0 \n", "2018 1.4 1.0 27171.0 1.0 2.0 \n", "2019 1.4 1.0 27171.0 1.0 3.0 \n", "2020 1.4 1.0 27171.0 1.0 4.0 \n", "2021 1.4 1.0 27171.0 1.0 5.0 \n", "2022 1.4 1.0 80551.0 0.0 0.0 \n", "2023 1.4 1.0 80551.0 0.0 1.0 \n", "2024 1.4 1.0 80551.0 0.0 2.0 \n", "2025 1.4 1.0 80551.0 0.0 3.0 \n", "2026 1.4 1.0 80551.0 0.0 4.0 \n", "2027 1.4 1.0 80551.0 0.0 5.0 \n", "2028 1.4 1.0 96604.0 1.0 0.0 \n", "2029 1.4 1.0 96604.0 1.0 1.0 \n", "2030 1.4 1.0 96604.0 1.0 2.0 \n", "2031 1.4 1.0 96604.0 1.0 3.0 \n", "2032 1.4 1.0 96604.0 1.0 4.0 \n", "2033 1.4 1.0 96604.0 1.0 5.0 \n", "2034 1.4 1.0 124367.0 0.0 0.0 \n", "2035 1.4 1.0 124367.0 0.0 1.0 \n", "2036 1.4 1.0 124367.0 0.0 2.0 \n", "2037 1.4 1.0 124367.0 0.0 3.0 \n", "2038 1.4 1.0 124367.0 0.0 4.0 \n", "2039 1.4 1.0 124367.0 0.0 5.0 \n", "2040 1.4 1.0 135784.0 0.0 0.0 \n", "2041 1.4 1.0 135784.0 0.0 1.0 \n", "2042 1.4 1.0 135784.0 0.0 2.0 \n", "2043 1.4 1.0 135784.0 0.0 3.0 \n", "2044 1.4 1.0 135784.0 0.0 4.0 \n", "2045 1.4 1.0 135784.0 0.0 5.0 \n", "2046 1.4 1.0 144507.0 0.0 0.0 \n", "2047 1.4 1.0 144507.0 0.0 1.0 \n", "2048 1.4 1.0 144507.0 0.0 2.0 \n", "2049 1.4 1.0 144507.0 0.0 3.0 \n", "2050 1.4 1.0 144507.0 0.0 4.0 \n", "2051 1.4 1.0 144507.0 0.0 5.0 \n", "2052 1.4 1.0 195972.0 0.0 0.0 \n", "2053 1.4 1.0 195972.0 0.0 1.0 \n", "2054 1.4 1.0 195972.0 0.0 2.0 \n", "2055 1.4 1.0 195972.0 0.0 3.0 \n", "2056 1.4 1.0 195972.0 0.0 4.0 \n", "2057 1.4 1.0 195972.0 0.0 5.0 \n", "2058 1.4 1.0 231280.0 0.0 0.0 \n", "2059 1.4 1.0 231280.0 0.0 1.0 \n", "2060 1.4 1.0 231280.0 0.0 2.0 \n", "2061 1.4 1.0 231280.0 0.0 3.0 \n", "2062 1.4 1.0 231280.0 0.0 4.0 \n", "2063 1.4 1.0 231280.0 0.0 5.0 \n", "2064 1.4 1.0 247059.0 1.0 0.0 \n", "2065 1.4 1.0 247059.0 1.0 1.0 \n", "2066 1.4 1.0 247059.0 1.0 2.0 \n", "2067 1.4 1.0 247059.0 1.0 3.0 \n", "2068 1.4 1.0 247059.0 1.0 4.0 \n", "2069 1.4 1.0 247059.0 1.0 5.0 \n", "2070 1.4 1.0 341031.0 0.0 0.0 \n", "2071 1.4 1.0 341031.0 0.0 1.0 \n", "2072 1.4 1.0 341031.0 0.0 2.0 \n", "2073 1.4 1.0 341031.0 0.0 3.0 \n", "2074 1.4 1.0 341031.0 0.0 4.0 \n", "2075 1.4 1.0 341031.0 0.0 5.0 \n", "2076 1.4 1.0 344553.0 1.0 0.0 \n", "2077 1.4 1.0 344553.0 1.0 1.0 \n", "2078 1.4 1.0 344553.0 1.0 2.0 \n", "2079 1.4 1.0 344553.0 1.0 3.0 \n", "2080 1.4 1.0 344553.0 1.0 4.0 \n", "2081 1.4 1.0 344553.0 1.0 5.0 \n", "2082 1.4 1.0 379479.0 1.0 0.0 \n", "2083 1.4 1.0 379479.0 1.0 1.0 \n", "2084 1.4 1.0 379479.0 1.0 2.0 \n", "2085 1.4 1.0 379479.0 1.0 3.0 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0.0 1.0 \n", "2114 1.4 1.0 590401.0 0.0 2.0 \n", "2115 1.4 1.0 590401.0 0.0 3.0 \n", "2116 1.4 1.0 590401.0 0.0 4.0 \n", "2117 1.4 1.0 590401.0 0.0 5.0 \n", "2118 1.4 1.0 612161.0 1.0 0.0 \n", "2119 1.4 1.0 612161.0 1.0 1.0 \n", "2120 1.4 1.0 612161.0 1.0 2.0 \n", "2121 1.4 1.0 612161.0 1.0 3.0 \n", "2122 1.4 1.0 612161.0 1.0 4.0 \n", "2123 1.4 1.0 612161.0 1.0 5.0 \n", "2124 1.4 1.0 661427.0 1.0 0.0 \n", "2125 1.4 1.0 661427.0 1.0 1.0 \n", "2126 1.4 1.0 661427.0 1.0 2.0 \n", "2127 1.4 1.0 661427.0 1.0 3.0 \n", "2128 1.4 1.0 661427.0 1.0 4.0 \n", "2129 1.4 1.0 661427.0 1.0 5.0 \n", "2130 1.4 1.0 706068.0 1.0 0.0 \n", "2131 1.4 1.0 706068.0 1.0 1.0 \n", "2132 1.4 1.0 706068.0 1.0 2.0 \n", "2133 1.4 1.0 706068.0 1.0 3.0 \n", "2134 1.4 1.0 706068.0 1.0 4.0 \n", "2135 1.4 1.0 706068.0 1.0 5.0 \n", "2136 1.4 1.0 716849.0 1.0 0.0 \n", "2137 1.4 1.0 716849.0 1.0 1.0 \n", "2138 1.4 1.0 716849.0 1.0 2.0 \n", "2139 1.4 1.0 716849.0 1.0 3.0 \n", "2140 1.4 1.0 716849.0 1.0 4.0 \n", "2141 1.4 1.0 716849.0 1.0 5.0 \n", "2142 1.4 1.0 763498.0 0.0 0.0 \n", "2143 1.4 1.0 763498.0 0.0 1.0 \n", "2144 1.4 1.0 763498.0 0.0 2.0 \n", "2145 1.4 1.0 763498.0 0.0 3.0 \n", "2146 1.4 1.0 763498.0 0.0 4.0 \n", "2147 1.4 1.0 763498.0 0.0 5.0 \n", "2148 1.4 1.0 776689.0 0.0 0.0 \n", "2149 1.4 1.0 776689.0 0.0 1.0 \n", "2150 1.4 1.0 776689.0 0.0 2.0 \n", "2151 1.4 1.0 776689.0 0.0 3.0 \n", "2152 1.4 1.0 776689.0 0.0 4.0 \n", "2153 1.4 1.0 776689.0 0.0 5.0 \n", "2154 1.4 1.0 827411.0 0.0 0.0 \n", "2155 1.4 1.0 827411.0 0.0 1.0 \n", "2156 1.4 1.0 827411.0 0.0 2.0 \n", "2157 1.4 1.0 827411.0 0.0 3.0 \n", "2158 1.4 1.0 827411.0 0.0 4.0 \n", "2159 1.4 1.0 827411.0 0.0 5.0 \n", "2160 1.4 1.0 853026.0 0.0 0.0 \n", "2161 1.4 1.0 853026.0 0.0 1.0 \n", "2162 1.4 1.0 853026.0 0.0 2.0 \n", "2163 1.4 1.0 853026.0 0.0 3.0 \n", "2164 1.4 1.0 853026.0 0.0 4.0 \n", "2165 1.4 1.0 853026.0 0.0 5.0 \n", "2166 1.4 1.0 883636.0 1.0 0.0 \n", "2167 1.4 1.0 883636.0 1.0 1.0 \n", "2168 1.4 1.0 883636.0 1.0 2.0 \n", "2169 1.4 1.0 883636.0 1.0 3.0 \n", "2170 1.4 1.0 883636.0 1.0 4.0 \n", "2171 1.4 1.0 883636.0 1.0 5.0 \n", "2172 1.4 1.0 906239.0 1.0 0.0 \n", "2173 1.4 1.0 906239.0 1.0 1.0 \n", "2174 1.4 1.0 906239.0 1.0 2.0 \n", "2175 1.4 1.0 906239.0 1.0 3.0 \n", "2176 1.4 1.0 906239.0 1.0 4.0 \n", "2177 1.4 1.0 906239.0 1.0 5.0 \n", "2178 1.4 1.0 909153.0 1.0 0.0 \n", "2179 1.4 1.0 909153.0 1.0 1.0 \n", "2180 1.4 1.0 909153.0 1.0 2.0 \n", "2181 1.4 1.0 909153.0 1.0 3.0 \n", "2182 1.4 1.0 909153.0 1.0 4.0 \n", "2183 1.4 1.0 909153.0 1.0 5.0 \n", "2184 1.4 1.0 934835.0 0.0 0.0 \n", "2185 1.4 1.0 934835.0 0.0 1.0 \n", "2186 1.4 1.0 934835.0 0.0 2.0 \n", "2187 1.4 1.0 934835.0 0.0 3.0 \n", "2188 1.4 1.0 934835.0 0.0 4.0 \n", "2189 1.4 1.0 934835.0 0.0 5.0 \n", "2190 1.4 1.0 954504.0 0.0 0.0 \n", "2191 1.4 1.0 954504.0 0.0 1.0 \n", "2192 1.4 1.0 954504.0 0.0 2.0 \n", "2193 1.4 1.0 954504.0 0.0 3.0 \n", "2194 1.4 1.0 954504.0 0.0 4.0 \n", "2195 1.4 1.0 954504.0 0.0 5.0 \n", "2196 1.6 0.0 27171.0 1.0 0.0 \n", 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1.0 5.0 \n", "2448 1.6 1.0 379479.0 1.0 0.0 \n", "2449 1.6 1.0 379479.0 1.0 1.0 \n", "2450 1.6 1.0 379479.0 1.0 2.0 \n", "2451 1.6 1.0 379479.0 1.0 3.0 \n", "2452 1.6 1.0 379479.0 1.0 4.0 \n", "2453 1.6 1.0 379479.0 1.0 5.0 \n", "2454 1.6 1.0 427694.0 1.0 0.0 \n", "2455 1.6 1.0 427694.0 1.0 1.0 \n", "2456 1.6 1.0 427694.0 1.0 2.0 \n", "2457 1.6 1.0 427694.0 1.0 3.0 \n", "2458 1.6 1.0 427694.0 1.0 4.0 \n", "2459 1.6 1.0 427694.0 1.0 5.0 \n", "2460 1.6 1.0 475207.0 0.0 0.0 \n", "2461 1.6 1.0 475207.0 0.0 1.0 \n", "2462 1.6 1.0 475207.0 0.0 2.0 \n", "2463 1.6 1.0 475207.0 0.0 3.0 \n", "2464 1.6 1.0 475207.0 0.0 4.0 \n", "2465 1.6 1.0 475207.0 0.0 5.0 \n", "2466 1.6 1.0 572697.0 1.0 0.0 \n", "2467 1.6 1.0 572697.0 1.0 1.0 \n", "2468 1.6 1.0 572697.0 1.0 2.0 \n", "2469 1.6 1.0 572697.0 1.0 3.0 \n", "2470 1.6 1.0 572697.0 1.0 4.0 \n", "2471 1.6 1.0 572697.0 1.0 5.0 \n", "2472 1.6 1.0 573178.0 1.0 0.0 \n", "2473 1.6 1.0 573178.0 1.0 1.0 \n", "2474 1.6 1.0 573178.0 1.0 2.0 \n", "2475 1.6 1.0 573178.0 1.0 3.0 \n", "2476 1.6 1.0 573178.0 1.0 4.0 \n", "2477 1.6 1.0 573178.0 1.0 5.0 \n", "2478 1.6 1.0 590401.0 0.0 0.0 \n", "2479 1.6 1.0 590401.0 0.0 1.0 \n", "2480 1.6 1.0 590401.0 0.0 2.0 \n", "2481 1.6 1.0 590401.0 0.0 3.0 \n", "2482 1.6 1.0 590401.0 0.0 4.0 \n", "2483 1.6 1.0 590401.0 0.0 5.0 \n", "2484 1.6 1.0 612161.0 1.0 0.0 \n", "2485 1.6 1.0 612161.0 1.0 1.0 \n", "2486 1.6 1.0 612161.0 1.0 2.0 \n", "2487 1.6 1.0 612161.0 1.0 3.0 \n", "2488 1.6 1.0 612161.0 1.0 4.0 \n", "2489 1.6 1.0 612161.0 1.0 5.0 \n", "2490 1.6 1.0 661427.0 1.0 0.0 \n", "2491 1.6 1.0 661427.0 1.0 1.0 \n", "2492 1.6 1.0 661427.0 1.0 2.0 \n", "2493 1.6 1.0 661427.0 1.0 3.0 \n", "2494 1.6 1.0 661427.0 1.0 4.0 \n", "2495 1.6 1.0 661427.0 1.0 5.0 \n", "2496 1.6 1.0 706068.0 1.0 0.0 \n", "2497 1.6 1.0 706068.0 1.0 1.0 \n", "2498 1.6 1.0 706068.0 1.0 2.0 \n", "2499 1.6 1.0 706068.0 1.0 3.0 \n", "2500 1.6 1.0 706068.0 1.0 4.0 \n", "2501 1.6 1.0 706068.0 1.0 5.0 \n", "2502 1.6 1.0 716849.0 1.0 0.0 \n", "2503 1.6 1.0 716849.0 1.0 1.0 \n", "2504 1.6 1.0 716849.0 1.0 2.0 \n", "2505 1.6 1.0 716849.0 1.0 3.0 \n", "2506 1.6 1.0 716849.0 1.0 4.0 \n", "2507 1.6 1.0 716849.0 1.0 5.0 \n", "2508 1.6 1.0 763498.0 0.0 0.0 \n", "2509 1.6 1.0 763498.0 0.0 1.0 \n", "2510 1.6 1.0 763498.0 0.0 2.0 \n", "2511 1.6 1.0 763498.0 0.0 3.0 \n", "2512 1.6 1.0 763498.0 0.0 4.0 \n", "2513 1.6 1.0 763498.0 0.0 5.0 \n", "2514 1.6 1.0 776689.0 0.0 0.0 \n", "2515 1.6 1.0 776689.0 0.0 1.0 \n", "2516 1.6 1.0 776689.0 0.0 2.0 \n", "2517 1.6 1.0 776689.0 0.0 3.0 \n", "2518 1.6 1.0 776689.0 0.0 4.0 \n", "2519 1.6 1.0 776689.0 0.0 5.0 \n", "2520 1.6 1.0 827411.0 0.0 0.0 \n", "2521 1.6 1.0 827411.0 0.0 1.0 \n", "2522 1.6 1.0 827411.0 0.0 2.0 \n", "2523 1.6 1.0 827411.0 0.0 3.0 \n", "2524 1.6 1.0 827411.0 0.0 4.0 \n", "2525 1.6 1.0 827411.0 0.0 5.0 \n", "2526 1.6 1.0 853026.0 0.0 0.0 \n", "2527 1.6 1.0 853026.0 0.0 1.0 \n", "2528 1.6 1.0 853026.0 0.0 2.0 \n", "2529 1.6 1.0 853026.0 0.0 3.0 \n", "2530 1.6 1.0 853026.0 0.0 4.0 \n", "2531 1.6 1.0 853026.0 0.0 5.0 \n", "2532 1.6 1.0 883636.0 1.0 0.0 \n", "2533 1.6 1.0 883636.0 1.0 1.0 \n", "2534 1.6 1.0 883636.0 1.0 2.0 \n", "2535 1.6 1.0 883636.0 1.0 3.0 \n", "2536 1.6 1.0 883636.0 1.0 4.0 \n", "2537 1.6 1.0 883636.0 1.0 5.0 \n", "2538 1.6 1.0 906239.0 1.0 0.0 \n", "2539 1.6 1.0 906239.0 1.0 1.0 \n", "2540 1.6 1.0 906239.0 1.0 2.0 \n", "2541 1.6 1.0 906239.0 1.0 3.0 \n", "2542 1.6 1.0 906239.0 1.0 4.0 \n", "2543 1.6 1.0 906239.0 1.0 5.0 \n", "2544 1.6 1.0 909153.0 1.0 0.0 \n", "2545 1.6 1.0 909153.0 1.0 1.0 \n", "2546 1.6 1.0 909153.0 1.0 2.0 \n", "2547 1.6 1.0 909153.0 1.0 3.0 \n", "2548 1.6 1.0 909153.0 1.0 4.0 \n", "2549 1.6 1.0 909153.0 1.0 5.0 \n", "2550 1.6 1.0 934835.0 0.0 0.0 \n", "2551 1.6 1.0 934835.0 0.0 1.0 \n", "2552 1.6 1.0 934835.0 0.0 2.0 \n", "2553 1.6 1.0 934835.0 0.0 3.0 \n", "2554 1.6 1.0 934835.0 0.0 4.0 \n", "2555 1.6 1.0 934835.0 0.0 5.0 \n", "2556 1.6 1.0 954504.0 0.0 0.0 \n", "2557 1.6 1.0 954504.0 0.0 1.0 \n", "2558 1.6 1.0 954504.0 0.0 2.0 \n", "2559 1.6 1.0 954504.0 0.0 3.0 \n", "2560 1.6 1.0 954504.0 0.0 4.0 \n", "2561 1.6 1.0 954504.0 0.0 5.0 \n", "\n", "key_respBorN.keys b n Response_Ratio Weighted_Sum \n", "0 1 0 1.00 0.4 \n", "1 0 1 0.00 0.0 \n", "2 0 1 0.00 0.0 \n", "3 0 1 0.00 0.0 \n", "4 0 1 0.00 0.0 \n", "5 0 1 0.00 0.0 \n", "6 0 1 0.00 0.0 \n", "7 0 1 0.00 0.0 \n", "8 1 0 1.00 0.4 \n", "9 0 1 0.00 0.0 \n", "10 0 1 0.00 0.0 \n", "11 0 1 0.00 0.0 \n", "12 0 1 0.00 0.0 \n", "13 0 1 0.00 0.0 \n", "14 0 1 0.00 0.0 \n", "15 0 1 0.00 0.0 \n", "16 0 1 0.00 0.0 \n", "17 0 1 0.00 0.0 \n", "18 1 0 1.00 0.4 \n", "19 1 0 1.00 0.4 \n", "20 0 1 0.00 0.0 \n", "21 0 1 0.00 0.0 \n", "22 0 1 0.00 0.0 \n", "23 0 1 0.00 0.0 \n", "24 0 1 0.00 0.0 \n", "25 0 1 0.00 0.0 \n", "26 0 1 0.00 0.0 \n", "27 0 1 0.00 0.0 \n", "28 1 0 1.00 0.4 \n", "29 0 1 0.00 0.0 \n", "30 0 1 0.00 0.0 \n", "31 0 1 0.00 0.0 \n", "32 0 1 0.00 0.0 \n", "33 0 1 0.00 0.0 \n", "34 0 1 0.00 0.0 \n", "35 0 1 0.00 0.0 \n", "36 0 1 0.00 0.0 \n", "37 0 1 0.00 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construction of the parameter:\n", "\n", "In order to numerically examine the shift of the\n", "gradients, we calculated the weighted mean of the\n", "gradient of each participant.\n", "This was done by\n", "[1]multiplying the number of “same” responses with the\n", "duration at which a “same” response was given,\n", "[2] adding these products,\n", "and then\n", "[4] dividing this weighted sum by the number of “same” responses\n", "of each participant given at all durations (calculated at step [3] in our case)\n", "\"\"\"\n", "\n", "\n", "# [1] Calculate the weighted sum for each participant and duration combination (result_df01 already contains number of same responses as variable 'b')\n", "\n", "result_df01['Weighted_Sum'] = result_df01['b'] * result_df01['duration']\n", "\n", "# [2] Add the weighted sums to have a total for each combination of participant and values of your independent variables\n", "\"\"\"\"\n", "You will need to replace my independent variables by yours.\n", "Keep the participant in the list, though.\n", "\"\"\"\n", "grouped01 = result_df01.groupby(['participant','stress','block'])['Weighted_Sum'].sum().reset_index()\n", "\n", "# [3] Calculate the total count of \"b\" responses for each participant and feedback combination\n", "grouped01['Total_b_Responses'] = result_df01.groupby(['participant', 'block'])['b'].transform('sum')\n", "\n", "# [4] Calculate the weighted mean of the gradient for each participant and feedback combination\n", "grouped01['Weighted_Mean_Gradient'] = grouped01['Weighted_Sum'] / grouped01['Total_b_Responses']\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#grouped01.to_csv(\"weighted_mean_gradient.csv\", index=False)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OoyQ12vGov__", "outputId": "2aa4ee79-3b7e-45c4-fbb8-bc4154638eb6" }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " participant stress block Weighted_Sum Total_b_Responses \\\n", "0 27171.0 1.0 0.0 9.6 11 \n", "1 27171.0 1.0 1.0 6.2 6 \n", "2 27171.0 1.0 2.0 9.0 8 \n", "3 27171.0 1.0 3.0 8.8 9 \n", "4 27171.0 1.0 4.0 8.4 8 \n", "5 27171.0 1.0 5.0 8.0 7 \n", "6 80551.0 0.0 0.0 3.6 3 \n", "7 80551.0 0.0 1.0 10.2 11 \n", "8 80551.0 0.0 2.0 6.0 7 \n", "9 80551.0 0.0 3.0 4.2 3 \n", "10 80551.0 0.0 4.0 3.2 4 \n", "11 80551.0 0.0 5.0 12.6 13 \n", "12 96604.0 1.0 0.0 6.8 7 \n", "13 96604.0 1.0 1.0 4.8 5 \n", "14 96604.0 1.0 2.0 8.4 8 \n", "15 96604.0 1.0 3.0 10.0 10 \n", "16 96604.0 1.0 4.0 9.6 9 \n", "17 96604.0 1.0 5.0 11.2 11 \n", "18 124367.0 0.0 0.0 6.2 7 \n", "19 124367.0 0.0 1.0 4.8 6 \n", "20 124367.0 0.0 2.0 11.4 11 \n", "21 124367.0 0.0 3.0 9.2 9 \n", "22 124367.0 0.0 4.0 8.2 8 \n", "23 124367.0 0.0 5.0 7.6 7 \n", "24 135784.0 0.0 0.0 5.2 5 \n", "25 135784.0 0.0 1.0 8.0 8 \n", "26 135784.0 0.0 2.0 11.0 12 \n", "27 135784.0 0.0 3.0 14.2 14 \n", "28 135784.0 0.0 4.0 10.0 12 \n", "29 135784.0 0.0 5.0 11.4 11 \n", "30 144507.0 0.0 0.0 5.4 5 \n", "31 144507.0 0.0 1.0 9.8 10 \n", "32 144507.0 0.0 2.0 10.0 10 \n", "33 144507.0 0.0 3.0 8.2 9 \n", "34 144507.0 0.0 4.0 9.0 9 \n", "35 144507.0 0.0 5.0 9.8 10 \n", "36 195972.0 0.0 0.0 8.8 9 \n", "37 195972.0 0.0 1.0 9.2 10 \n", "38 195972.0 0.0 2.0 9.4 9 \n", "39 195972.0 0.0 3.0 6.6 7 \n", "40 195972.0 0.0 4.0 5.8 6 \n", "41 195972.0 0.0 5.0 9.0 10 \n", "42 201266.0 1.0 0.0 2.8 2 \n", "43 201266.0 1.0 1.0 4.4 5 \n", "44 201266.0 1.0 2.0 6.6 6 \n", "45 201266.0 1.0 3.0 4.8 4 \n", "46 201266.0 1.0 4.0 6.0 6 \n", "47 201266.0 1.0 5.0 5.4 5 \n", "48 231280.0 0.0 0.0 4.6 5 \n", "49 231280.0 0.0 1.0 6.0 7 \n", "50 231280.0 0.0 2.0 10.4 11 \n", "51 231280.0 0.0 3.0 7.2 7 \n", "52 231280.0 0.0 4.0 5.0 5 \n", "53 231280.0 0.0 5.0 7.8 8 \n", "54 247059.0 1.0 0.0 9.4 9 \n", "55 247059.0 1.0 1.0 6.2 6 \n", "56 247059.0 1.0 2.0 8.0 8 \n", "57 247059.0 1.0 3.0 4.0 4 \n", "58 247059.0 1.0 4.0 7.2 7 \n", "59 247059.0 1.0 5.0 9.4 9 \n", "60 341031.0 0.0 0.0 8.2 8 \n", "61 341031.0 0.0 1.0 7.8 8 \n", "62 341031.0 0.0 2.0 10.8 11 \n", "63 341031.0 0.0 3.0 9.4 9 \n", "64 341031.0 0.0 4.0 11.8 11 \n", "65 341031.0 0.0 5.0 9.2 9 \n", "66 344553.0 1.0 0.0 8.8 9 \n", "67 344553.0 1.0 1.0 10.2 10 \n", "68 344553.0 1.0 2.0 12.2 12 \n", "69 344553.0 1.0 3.0 9.2 9 \n", "70 344553.0 1.0 4.0 13.0 13 \n", "71 344553.0 1.0 5.0 11.2 11 \n", "72 379479.0 1.0 0.0 13.2 12 \n", "73 379479.0 1.0 1.0 9.2 9 \n", "74 379479.0 1.0 2.0 11.8 12 \n", "75 379479.0 1.0 3.0 10.4 10 \n", "76 379479.0 1.0 4.0 10.0 10 \n", "77 379479.0 1.0 5.0 9.6 9 \n", "78 427694.0 1.0 0.0 11.8 11 \n", "79 427694.0 1.0 1.0 5.2 5 \n", "80 427694.0 1.0 2.0 9.2 9 \n", "81 427694.0 1.0 3.0 9.0 9 \n", "82 427694.0 1.0 4.0 5.0 5 \n", "83 427694.0 1.0 5.0 7.6 8 \n", "84 475207.0 0.0 0.0 10.4 10 \n", "85 475207.0 0.0 1.0 11.8 13 \n", "86 475207.0 0.0 2.0 7.8 8 \n", "87 475207.0 0.0 3.0 4.8 5 \n", "88 475207.0 0.0 4.0 14.4 14 \n", "89 475207.0 0.0 5.0 17.2 17 \n", "90 572697.0 1.0 0.0 5.4 6 \n", "91 572697.0 1.0 1.0 6.6 7 \n", "92 572697.0 1.0 2.0 9.6 9 \n", "93 572697.0 1.0 3.0 6.8 7 \n", "94 572697.0 1.0 4.0 7.8 8 \n", "95 572697.0 1.0 5.0 4.8 6 \n", "96 573178.0 1.0 0.0 8.2 8 \n", "97 573178.0 1.0 1.0 7.8 8 \n", "98 573178.0 1.0 2.0 9.8 10 \n", "99 573178.0 1.0 3.0 10.2 11 \n", "100 573178.0 1.0 4.0 8.6 9 \n", "101 573178.0 1.0 5.0 10.2 11 \n", "102 590401.0 0.0 0.0 8.2 8 \n", "103 590401.0 0.0 1.0 9.4 9 \n", "104 590401.0 0.0 2.0 9.2 9 \n", "105 590401.0 0.0 3.0 10.6 10 \n", "106 590401.0 0.0 4.0 9.6 9 \n", "107 590401.0 0.0 5.0 11.2 11 \n", "108 612161.0 1.0 0.0 6.6 7 \n", "109 612161.0 1.0 1.0 9.4 10 \n", "110 612161.0 1.0 2.0 6.2 7 \n", "111 612161.0 1.0 3.0 7.6 8 \n", "112 612161.0 1.0 4.0 5.8 7 \n", "113 612161.0 1.0 5.0 6.2 6 \n", "114 661427.0 1.0 0.0 9.0 9 \n", "115 661427.0 1.0 1.0 13.2 14 \n", "116 661427.0 1.0 2.0 7.6 8 \n", "117 661427.0 1.0 3.0 9.8 10 \n", "118 661427.0 1.0 4.0 9.0 9 \n", "119 661427.0 1.0 5.0 11.6 11 \n", "120 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7.8 8 \n", "150 853026.0 0.0 0.0 7.4 8 \n", "151 853026.0 0.0 1.0 9.2 9 \n", "152 853026.0 0.0 2.0 8.6 9 \n", "153 853026.0 0.0 3.0 7.8 8 \n", "154 853026.0 0.0 4.0 8.6 9 \n", "155 853026.0 0.0 5.0 8.0 9 \n", "156 883636.0 1.0 0.0 7.6 7 \n", "157 883636.0 1.0 1.0 10.2 10 \n", "158 883636.0 1.0 2.0 4.8 5 \n", "159 883636.0 1.0 3.0 6.4 7 \n", "160 883636.0 1.0 4.0 10.4 10 \n", "161 883636.0 1.0 5.0 10.8 11 \n", "162 906239.0 1.0 0.0 6.8 7 \n", "163 906239.0 1.0 1.0 11.0 11 \n", "164 906239.0 1.0 2.0 8.8 9 \n", "165 906239.0 1.0 3.0 10.0 10 \n", "166 906239.0 1.0 4.0 8.6 9 \n", "167 906239.0 1.0 5.0 10.2 10 \n", "168 909153.0 1.0 0.0 9.8 9 \n", "169 909153.0 1.0 1.0 7.8 8 \n", "170 909153.0 1.0 2.0 9.0 9 \n", "171 909153.0 1.0 3.0 9.2 10 \n", "172 909153.0 1.0 4.0 7.8 8 \n", "173 909153.0 1.0 5.0 10.0 10 \n", "174 934835.0 0.0 0.0 8.0 8 \n", "175 934835.0 0.0 1.0 9.4 9 \n", "176 934835.0 0.0 2.0 5.4 6 \n", "177 934835.0 0.0 3.0 9.0 9 \n", "178 934835.0 0.0 4.0 9.2 9 \n", "179 934835.0 0.0 5.0 10.6 11 \n", "180 954504.0 0.0 0.0 9.0 9 \n", "181 954504.0 0.0 1.0 10.6 11 \n", "182 954504.0 0.0 2.0 7.6 8 \n", "183 954504.0 0.0 3.0 10.0 10 \n", "184 954504.0 0.0 4.0 9.0 9 \n", "185 954504.0 0.0 5.0 10.8 11 \n", "\n", " Weighted_Mean_Gradient \n", "0 0.872727 \n", "1 1.033333 \n", "2 1.125000 \n", "3 0.977778 \n", "4 1.050000 \n", "5 1.142857 \n", "6 1.200000 \n", "7 0.927273 \n", "8 0.857143 \n", "9 1.400000 \n", "10 0.800000 \n", "11 0.969231 \n", "12 0.971429 \n", "13 0.960000 \n", "14 1.050000 \n", "15 1.000000 \n", "16 1.066667 \n", "17 1.018182 \n", "18 0.885714 \n", "19 0.800000 \n", "20 1.036364 \n", "21 1.022222 \n", "22 1.025000 \n", "23 1.085714 \n", "24 1.040000 \n", "25 1.000000 \n", "26 0.916667 \n", "27 1.014286 \n", "28 0.833333 \n", "29 1.036364 \n", "30 1.080000 \n", "31 0.980000 \n", "32 1.000000 \n", "33 0.911111 \n", "34 1.000000 \n", "35 0.980000 \n", "36 0.977778 \n", "37 0.920000 \n", "38 1.044444 \n", "39 0.942857 \n", "40 0.966667 \n", "41 0.900000 \n", "42 1.400000 \n", "43 0.880000 \n", "44 1.100000 \n", "45 1.200000 \n", "46 1.000000 \n", "47 1.080000 \n", "48 0.920000 \n", "49 0.857143 \n", "50 0.945455 \n", "51 1.028571 \n", "52 1.000000 \n", "53 0.975000 \n", "54 1.044444 \n", "55 1.033333 \n", "56 1.000000 \n", "57 1.000000 \n", "58 1.028571 \n", "59 1.044444 \n", "60 1.025000 \n", "61 0.975000 \n", "62 0.981818 \n", "63 1.044444 \n", "64 1.072727 \n", "65 1.022222 \n", "66 0.977778 \n", "67 1.020000 \n", "68 1.016667 \n", "69 1.022222 \n", "70 1.000000 \n", "71 1.018182 \n", "72 1.100000 \n", "73 1.022222 \n", "74 0.983333 \n", "75 1.040000 \n", "76 1.000000 \n", "77 1.066667 \n", "78 1.072727 \n", "79 1.040000 \n", "80 1.022222 \n", "81 1.000000 \n", "82 1.000000 \n", "83 0.950000 \n", "84 1.040000 \n", "85 0.907692 \n", "86 0.975000 \n", "87 0.960000 \n", "88 1.028571 \n", "89 1.011765 \n", "90 0.900000 \n", "91 0.942857 \n", "92 1.066667 \n", "93 0.971429 \n", "94 0.975000 \n", "95 0.800000 \n", "96 1.025000 \n", "97 0.975000 \n", "98 0.980000 \n", "99 0.927273 \n", "100 0.955556 \n", "101 0.927273 \n", "102 1.025000 \n", "103 1.044444 \n", "104 1.022222 \n", "105 1.060000 \n", "106 1.066667 \n", "107 1.018182 \n", "108 0.942857 \n", "109 0.940000 \n", "110 0.885714 \n", "111 0.950000 \n", "112 0.828571 \n", "113 1.033333 \n", "114 1.000000 \n", "115 0.942857 \n", "116 0.950000 \n", "117 0.980000 \n", "118 1.000000 \n", "119 1.054545 \n", "120 0.942857 \n", "121 0.955556 \n", "122 1.018182 \n", "123 0.980000 \n", "124 0.980000 \n", "125 0.940000 \n", "126 0.975000 \n", "127 1.000000 \n", "128 0.977778 \n", "129 1.000000 \n", "130 0.971429 \n", "131 0.900000 \n", "132 1.072727 \n", "133 1.218182 \n", "134 1.088889 \n", "135 1.072727 \n", "136 1.109091 \n", "137 1.025000 \n", "138 0.963636 \n", "139 1.020000 \n", "140 0.984615 \n", "141 1.036364 \n", "142 1.000000 \n", "143 1.050000 \n", "144 1.040000 \n", "145 1.033333 \n", "146 0.960000 \n", "147 1.050000 \n", "148 1.125000 \n", "149 0.975000 \n", "150 0.925000 \n", "151 1.022222 \n", "152 0.955556 \n", "153 0.975000 \n", "154 0.955556 \n", "155 0.888889 \n", "156 1.085714 \n", "157 1.020000 \n", "158 0.960000 \n", "159 0.914286 \n", "160 1.040000 \n", "161 0.981818 \n", "162 0.971429 \n", "163 1.000000 \n", "164 0.977778 \n", "165 1.000000 \n", "166 0.955556 \n", "167 1.020000 \n", "168 1.088889 \n", "169 0.975000 \n", "170 1.000000 \n", "171 0.920000 \n", "172 0.975000 \n", "173 1.000000 \n", "174 1.000000 \n", "175 1.044444 \n", "176 0.900000 \n", "177 1.000000 \n", "178 1.022222 \n", "179 0.963636 \n", "180 1.000000 \n", "181 0.963636 \n", "182 0.950000 \n", "183 1.000000 \n", "184 1.000000 \n", "185 0.981818 " ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#this data frame contains WMR calculated separately for each block under distinct conditions\n", "#in the first itteration (grouped), there are two WMG values per participant, now there are 6\n", "grouped01" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "iHY1sQpdowAA", "outputId": "c7d9436f-a00c-4302-d3a7-247c64cd78b2" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+LukX3bfu7RGxJLUIzUqkvb2diy++mPb21FdfNas4SRuQ/53coLO/Az7f42ZWNZqbm1mzZg1Lly4tdyhmJZc0GXRExHUR8VBErOq+pRqZWQm1t7fT2tpKRNDa2uragY04SdsMlkm6APgp8Fr3g91zFZllXXNzM11dXQB0dnaydOlS5s+fX+aosq1cvWJsaJLWDJrIXRb6FbAqf/MixFY1VqxYQUdHBwAdHR0sX768zBFVp1deeaUkPWNs8JIuezkt7UDMymn27Nm0tLTQ0dHB6NGjmTNnTrlDyrxy9YqxoUnctVTSEcAMeqxxEBFuabOq0NTURGtrKwA1NTXMnTvgdFxmVSdp19IFwDX52wnA14FTUozLrKTq6upobGxEEo2NjdTVee0mG1mSthn8JblVzv4nIs4B3gnsmVpUZmXQ1NTEzJkzXSuwESlpMnglIrqADkl7AZuAgws9SdJiSZsk9bn2gaTDJD0g6TVJlyQP26z46urquPrqq10rsBEpaTJYKWkf4HvkehKtBh5K8LwlQOMA27eSWzTnGwnjMDOzFCTtTXRB/s9FklqBvSLidwmed6+kqQNs3wRskvTBJHFUEvehzq6+3jvo//3ze2cjQdIGZEk6S9KXI+Jp4AVJx6Yb2m4xnCdppaSVmzdvLuVLJ+Y+1Nnm989GsqRdS/8J6AJOBC4DtgM/BmalFNduIuJ64HqAhoaGKNXr9sd9qLOrv7N8v382kiVNBu+KiGMkPQwQEX+UtEeKcZmZWQklTQY7JdUAASBpErmagllq3C5jVjpJk8HV5Cap20/SV8mNO/i7Qk+SdDPwXmCipDZgAVALEBGLJP0JuTmO9gK6JH0GmBERLw6yHDZC+Jq+VYJq7ISQtDfRTZJWkRt4JuDDEfFYguedWWD7/wD1SWKwkcftMpY1WT5ZGTAZSNq3x91NwM09t1X6FNbVmL3NrPyqsRNCoZrBFqAN6Mjf77nwfZBgFHIlGkz27i+h9KV7v+4PRCFOPmbF0d7ezsKFC1mwYIFHkA9RoWRwDblr/veTqxXcFxFl79aZVDGy9/r161n36MNMGd9ZcN89duaGbbz2TOGlHv7wUk3Bfay6uYG8eHouWepFiYZmwGQQEfMkiVxC+BhwjaSfA9dFxFMliK8iTBnfyZeOKW6b9j+s3quox7PqkOVrzuXSe8nSuXPnunYwBAUbkPM1gbvzYwzOAC4H1pGbp8jMhsgN5MXhJUuLY8DpKCS9RdJfS7oVaAHGA8dEhBOBmVUEL1laHIXmJtoEfIHc2sffBJ4EZkk6TdJpaQdnZlbI7NmzGT06d5HDS5YOXaHLRP9OrtfQYflbTwH8JI2gzMyS8pKlxVGoAfnsJAeR1BQRzUWJyMxsELqXLF22bJmXLB2GpIvbFJKsY72ZWQq8ZOnwJZ2bqBAV3sVsZKr2gYulLt9A4zMuu+yyRMew3RUrGWRmIFo18XQb2bB+/XoeWfsYneP2LbjvqNdzX6VVTz5fcN+aHZUxG0wllC+L4zMq7STBNYMqlMUvRrXrHLcvrxz2gaIec+zjLUU93nCUsnzVMj6j0mY3KFYyuL9IxxmytLLs+vXrOah2WKGlJq3JslzjyDa/f9lRSbMbFJq19LMDbY+Iq/L/XjikVy+i1KqqL++AfYYbXXVwjSPb/P7ZQArVDCbk/z2U3HrHt+Xv/wVwb1pBDVUaVdXxq/8VeL2ox6x01Tg970ji98+GotA4g4UA+cnpjomI7fn7f09uQJpZRau0RjqzSpW0zWAKbz49fh2YWvRorF/+URuaSmukM6tUSZPBvwIPSfopuW6kHwGWphbVEGzYsIGaHduK38Ois4PndxRrbN7QVUL3vayqpEY6y56R0jkl6RrIX5V0B/B/8g+dExEPpxeW9SWNNpG3PHIL69evT/zhBdc4BiutE5WaHe1s2NBReEcblpHSOWUwXUvHAS9GxA2SJkmaVkkL3Bx44IH8z2ujU2lAnjyuehuQ1bWTeO21RJdGfBnFRqpUOqesXMIz22uKXst8ZnsNb8l3Ix6MRMlA0gKggVyvohuAWuBG4N2DfkWrOL6Mkq60TlTGPt7CgQdOLuoxbeRKWjP4CHA0sBogIjZKmjDwU8ySS3pddrCXqjZs2MDEYUVmlpJRNbx1wmupnIjt2cca2oUkTQavR0RICsitgDboVzIbQNLrsoNtHB8/pjZXjzWzASVNBj+U9M/APpL+BjgX+H6hJ0laDHwI2BQRR/SxXcB3gA8AO4CzI2J10uCtuhT7uuzYx1uga3vRjmelNerVF1m/frs7N5RI0t5E35A0B3iRXLvBlyMiyUKjS4Br6b8b6snAIfnbu4Dr8v+a2Qjnzg2llbQB+cqIuBRY3sdj/YqIeyVNHWCXU4GlERHAryXtI2n/iHguSVxmhbz22ms882rl9Ngotg0bNiQ+G07r7Dm9MT47eV3JJkSePK4r8WEj3pi0z96Q9DLRHKD3D//JfTw2WAcCz/a435Z/bLdkIOk84DyAKVOmDPNlzarDK6+84hHWVhSFZi39FHABcLCk3/XYNIHiTFvdV9rvc6GciLgeuB6goaHBi+lYInvuuScH1b5SMT020lDursFpjvGZPmFHVb93laRQzeAHwB3AFcAXezy+PSKKMY9BG3BQj/v1wMYiHNdsRKj2y2BWOoVmLd0GbAPOlFQDTM4/Z7yk8RHxh2G+/m3AhZJuIddwvK3i2gu6OitqlGDRVXv5KkTNjq2JrqmPejV3Ftw1pvD7UbNjq9cYtKJJ2oB8IfD3wPNAd0tNAEcWeN7NwHuBiZLagAXke31HxCKghVy30vXkupaeM9gCmFW66dOnJ953/fpcV9jpBycZWTw5N6iu4zlfSrFhS9qA/Bng0IhoH8zBI+LMAtsD+PRgjllyFTZKsOiqvXwVYDD92Qe7AM28efN47ZnyV6bTqPnQWRmT8FX7jMjdkiaDZ8ldLrIyGSkfyDT84aVkl8G6/x+SdFP8w0s1HDLsyKpDWjWf3L7VO0lkpUm6BvKTwD2Sfga81r29ew1ks+FKI9nV7Gins7aGQw8/OtH+r+f74e/51sI/bocwuB/BapZWzSdX6yncDTZtI2VG5KRrIP8hf9sjf7MSGykfyGIbP378oC65gNcIriSVUqur5stg3RKtgZwVI+ENq1ZpJDtP8Zxtg6l5pVmrGymXwZL2JlrG7oPBtgErgX+OiFeLHdhgjZQ3zKy3Sjl7LrY0G94rIY5KuQzWLWkD8pPAJODm/P2/ItfN9O3A94CPFT+0wUnzDVv36MNV+WWz7KuUs2fLvqTJ4OiIOL7H/WWS7o2I4yU9mkZglcJfNqtklXL2bNmXNBlMkjSle8SxpCmwawGpqr6G4i+bmY0ESZPB54D7JP2e3AD4acAF+RXPmtMKzsyyq6+lTAeaRjvplNlpHXekS7q4TYukQ4DDyCWDx3s0Gn87pdisRKq1AbI//a233N8Pin9Mimfs2LGZOu5IUmjQ2YkR8QtJp/XadLAkIuInKcZmJRCjatEeeyRq50i7TSRJ1+BBT+RG8q6l/kEprrQSaDUl5ko6EStUM3gP8AvgL/rYFoCTQcZ1jdmL6QdPTtyzCtJpE0maOAY7kVtfx62mHxPLrkrrnFJo0NmC/L+eTdRSlfQH2o30Vi0qrXNKohnKJE2W9C+S7sjfnyHp46lFZWZmJZV0usolwJ3AAfn7T5Cb1trMzKpA0mQwMSJ+SH5hm4joAAqvwG1mZpmQNBm8LKmO/PxEko7D6xuYmVWNQl1LPwPcD3wBuJVcl9L7yc1T9H9Tj87MzEqiUNfSeuA75AabPQ4sB+4B/i0itqQbmvWW2qLqg+iLb9m1c+dOnnnmGdrb26mrqyt3OFZhCnUtvQRA0h5AA/BnwInA/5P0QkTMSD9Eg3QXVfeEedWlvxHWTzzxBB0dHZx33nnU19fvetwjrA2Sz000FtgL2Dt/2wisSSso212l9Um2bNm5cycdHbmFmrZu3crkyZOpra0tc1RWSQq1GVwPHA5sBx4EfgVcFRF/LEFsZjYEfZ04XHXVVbtqBjU1NRxyyCHMnz+/DNFZpSpUM5gC7AmsAzYAbcALKcdkNiKUcvbNFStW7KoZdHR0sHz5cicDe5MBu5ZGRCMwC/hG/qHPAb+R9HNJmVof2SwLxo4dm8qEebNnz2b06Ny53+jRo5kzZ07RX8OyrWCbQUQEsFbSC+TGFmwDPgQcCywo9HxJjeR6JNUA34+Ir/Xa/r+AxcDbgFeBcyNi7eCKYZY9pWy0bWpqorW1FYCamhrmzp1bste2bBiwZiDpYkm3SHoWuJdcEvhv4DRg30IHl1QDfBc4GZgBnCmpdw+kLwGPRMSRwFxyicPMiqiuro7GxkYk0djY6K6ltptCNYOpwI+A+RHx3BCOfyywPiKeBJB0C3Aq8F899pkBXAEQEY9LmippckQ8P4TXM7N+NDU18fTTT7tWUATVuEBSoXEGnx3m8Q8Enu1xvw14V699fkuupnGfpGOBt5Ib7PamZCDpPOA8gClTpgwzLLORp66ujquvvrrcYVS1PffckxdffJGdO3dmrutu0nEGQ6U+Hote978GfEfSI+TGLjwMdOz2pIjrgesBGhoaeh/DbNja29tZuHAhCxYs8GUUG1B/Z/lXXXUVy5Yty2TX3aQT1Q1VG3BQj/v15Aas7RIRL0bEORFxFLk2g0nAUynHZbab5uZm1qxZw9KlS8sdimVQe3s7ra2tRAStra20t7eXO6RBSTsZ/AY4RNK0/JQWZwC39dxB0j75bQCfAO6NiBdTjsvsTbL+Rbbya25upqsrt0ZxZ2dn5k4qUr1MFBEdki4ktzBODbA4Ih6VdH5++yLgHcBSSZ3kGpaLtoJaNTbyWDr6+iJnrZpv5ZX1gX1p1wyIiJaIeHtEvC0ivpp/bFE+ERARD0TEIRFxWEScVoqpLtIa2GPZ1dcX2Wwwsj6wL+0G5LLyWf7QjMQa1ezZs2lpaaGjoyOTX2Qrv6wP7Eu9ZmD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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#here we can plot the WMG for each group in each block\n", "sns.boxplot(data=grouped01, x=\"block\", y=\"Weighted_Mean_Gradient\", hue='stress')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "y5sz3W7aowAA", "outputId": "729d5029-537b-44d3-9d6f-5ebb5f664c50" }, "outputs": [ { "data": { "text/html": [ "
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SourceSSDF1DF2MSFp-uncp-GG-corrnp2epssphericityW-spherp-spher
0stress0.0000011290.0000010.0001310.990945NaN0.000005NaNNaNNaNNaN
1block0.02887551450.0057751.0410440.3959000.3800610.0346540.661969False0.3380510.006723
2Interaction0.02388651450.0047770.8611490.508924NaN0.028838NaNNaNNaNNaN
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" ], "text/plain": [ " Source SS DF1 DF2 MS F p-unc p-GG-corr \\\n", "0 stress 0.000001 1 29 0.000001 0.000131 0.990945 NaN \n", "1 block 0.028875 5 145 0.005775 1.041044 0.395900 0.380061 \n", "2 Interaction 0.023886 5 145 0.004777 0.861149 0.508924 NaN \n", "\n", " np2 eps sphericity W-spher p-spher \n", "0 0.000005 NaN NaN NaN NaN \n", "1 0.034654 0.661969 False 0.338051 0.006723 \n", "2 0.028838 NaN NaN NaN NaN " ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pg.mixed_anova(data=grouped01, dv='Weighted_Mean_Gradient', subject='participant', within='block', between='stress')" ] }, { "cell_type": "markdown", "metadata": { "id": "YgyQZ7z3owAA" }, "source": [ "### FOR WITHIN FACTOR AND BLOCK (ADVANCED ANALYSIS, NOT NECESSARILY NEEDED)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "h5-xzqPFowAA", "outputId": "78b2c98e-9a47-4985-efae-b4b73169bb45" }, "outputs": [ { "data": { "text/html": [ "
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durationfeedbacknoFeedbackBlocks.thisTrialNBlock.thisTrialNthisRow.tstressaccuracypolygonBlackSquare.startedpolygonBlackSquare.stoppedkey_respBorN.corrkey_respBorN.rtparticipantsessionage
count3660.000003660.0000003660.0000003660.0000003660.0000002940.0000003660.0000003660.0000003660.0000003435.0000003660.0000003660.0000003660.0000003660.03660.000000
mean1.000000.4918030.5081972.5000004.500000881.9093750.5081970.491803881.497263883.1920600.7306012.056278511267.6721311.022.573770
std0.334710.5000010.5000011.7080582.872674489.3603040.5000010.500001486.463570481.7951630.4437089.550355297657.2217110.01.833093
min0.400000.0000000.0000000.0000000.000000134.1625860.0000000.000000133.079389133.4792050.000000-21.76564027171.0000001.021.000000
25%0.800000.0000000.0000001.0000002.000000501.8006910.0000000.000000500.648925505.3250670.0000001.415446231280.0000001.021.000000
50%1.000000.0000001.0000002.5000004.500000831.9922581.0000000.000000824.594573830.2359211.0000001.695342572697.0000001.022.000000
75%1.200001.0000001.0000004.0000007.0000001156.3427921.0000001.0000001158.4218771164.3311341.0000002.020344776689.0000001.023.000000
max1.600001.0000001.0000005.0000009.0000002888.3059861.0000001.0000002887.0073612887.6067661.000000525.046891954504.0000001.030.000000
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" ], "text/plain": [ " duration feedback noFeedback Blocks.thisTrialN \\\n", "count 3660.00000 3660.000000 3660.000000 3660.000000 \n", "mean 1.00000 0.491803 0.508197 2.500000 \n", "std 0.33471 0.500001 0.500001 1.708058 \n", "min 0.40000 0.000000 0.000000 0.000000 \n", "25% 0.80000 0.000000 0.000000 1.000000 \n", "50% 1.00000 0.000000 1.000000 2.500000 \n", "75% 1.20000 1.000000 1.000000 4.000000 \n", "max 1.60000 1.000000 1.000000 5.000000 \n", "\n", " Block.thisTrialN thisRow.t stress accuracy \\\n", "count 3660.000000 2940.000000 3660.000000 3660.000000 \n", "mean 4.500000 881.909375 0.508197 0.491803 \n", "std 2.872674 489.360304 0.500001 0.500001 \n", "min 0.000000 134.162586 0.000000 0.000000 \n", "25% 2.000000 501.800691 0.000000 0.000000 \n", "50% 4.500000 831.992258 1.000000 0.000000 \n", "75% 7.000000 1156.342792 1.000000 1.000000 \n", "max 9.000000 2888.305986 1.000000 1.000000 \n", "\n", " polygonBlackSquare.started polygonBlackSquare.stopped \\\n", "count 3660.000000 3435.000000 \n", "mean 881.497263 883.192060 \n", "std 486.463570 481.795163 \n", "min 133.079389 133.479205 \n", "25% 500.648925 505.325067 \n", "50% 824.594573 830.235921 \n", "75% 1158.421877 1164.331134 \n", "max 2887.007361 2887.606766 \n", "\n", " key_respBorN.corr key_respBorN.rt participant session age \n", "count 3660.000000 3660.000000 3660.000000 3660.0 3660.000000 \n", "mean 0.730601 2.056278 511267.672131 1.0 22.573770 \n", "std 0.443708 9.550355 297657.221711 0.0 1.833093 \n", "min 0.000000 -21.765640 27171.000000 1.0 21.000000 \n", "25% 0.000000 1.415446 231280.000000 1.0 21.000000 \n", "50% 1.000000 1.695342 572697.000000 1.0 22.000000 \n", "75% 1.000000 2.020344 776689.000000 1.0 23.000000 \n", "max 1.000000 525.046891 954504.000000 1.0 30.000000 " ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df01.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LeJHrdunowAA", "outputId": "18ef4013-5cbe-447f-f493-06c5da59db52" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " duration corrAns feedback noFeedback Blocks.thisTrialN \\\n", "14 1.0 b 1.0 0.0 0.0 \n", "15 0.8 n 1.0 0.0 0.0 \n", "16 1.6 n 1.0 0.0 0.0 \n", "17 0.6 n 1.0 0.0 0.0 \n", "18 1.4 n 1.0 0.0 0.0 \n", "\n", " Block.thisTrialN thisRow.t stress accuracy \\\n", "14 0.0 160.033362 1.0 0.0 \n", "15 1.0 167.613858 1.0 0.0 \n", "16 2.0 175.680219 1.0 0.0 \n", "17 3.0 183.186162 1.0 0.0 \n", "18 4.0 191.197483 1.0 0.0 \n", "\n", " polygonBlackSquare.started polygonBlackSquare.stopped key_respBorN.keys \\\n", "14 157.854946 158.853922 b \n", "15 166.022553 166.821131 b \n", "16 173.605768 175.204885 n \n", "17 181.671835 182.271339 b \n", "18 189.171992 190.571622 n \n", "\n", " key_respBorN.corr key_respBorN.rt participant session gender age \\\n", "14 1.0 2.165862 27171.0 1.0 Male 24.0 \n", "15 0.0 1.575449 27171.0 1.0 Male 24.0 \n", "16 1.0 2.058987 27171.0 1.0 Male 24.0 \n", "17 0.0 1.497676 27171.0 1.0 Male 24.0 \n", "18 1.0 2.010307 27171.0 1.0 Male 24.0 \n", "\n", " date expName frameRate halve \n", "14 2023-10-16_09h41.06.754 time_generalization_00 120.706 1.0 \n", "15 2023-10-16_09h41.06.754 time_generalization_00 120.706 1.0 \n", "16 2023-10-16_09h41.06.754 time_generalization_00 120.706 1.0 \n", "17 2023-10-16_09h41.06.754 time_generalization_00 120.706 1.0 \n", "18 2023-10-16_09h41.06.754 time_generalization_00 120.706 1.0 \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ ":2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " new_df01['halve'] = np.where((new_df01['Blocks.thisTrialN'] >= 0) & (new_df01['Blocks.thisTrialN'] <= 2), 1,\n" ] } ], "source": [ "#first we need to create new variable indicating in which halve of the experiment each measurement was acquired\n", "new_df01['halve'] = np.where((new_df01['Blocks.thisTrialN'] >= 0) & (new_df01['Blocks.thisTrialN'] <= 2), 1,\n", " np.where((new_df01['Blocks.thisTrialN'] >= 3) & (new_df01['Blocks.thisTrialN'] <= 5), 2, np.nan))\n", "\n", "# Print the DataFrame to verify the new column\n", "print(new_df01.head())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JHJKGuLpowAB" }, "outputs": [], "source": [ "#create new data frame with response ratios using the dataframe called new_df01 with your data you generated in the section 1\n", "\n", "\"\"\"\"\n", "Create a pivot table to calculate the counts of 'b' and 'n' for each group\n", "\n", "we need to make the counts for each participant at each duration at each condition,\n", "so edit the code to contain your independent variables\n", "my independent variables were 'feedback' and the halves of the experiment.\n", "keep the variables 'duration' and 'participant' in the list\n", "\"\"\"\n", "\n", "pivot_table = new_df01.pivot_table(index=['duration', 'participant','feedback', \"stress\", 'halve'], columns='key_respBorN.keys',\n", " aggfunc='size', fill_value=0)\n", "\n", "# Calculate the Response Ratio (count of 'b' / count of 'n') for each group\n", "pivot_table['Response_Ratio'] = pivot_table['b'] / (pivot_table['b'] + pivot_table['n'])\n", "\n", "# Reset the index to have a new dataframe with the specified columns\n", "result_df02 = pivot_table.reset_index()\n", "\n", "# If you want to replace any NaN values with 0, you can use fillna method\n", "result_df02.fillna(0, inplace=True)\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#result_df02.to_csv(\"response_ratios.csv\", index=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WG5DPzpVowAB", "outputId": "b23bd1e6-eadd-4179-c117-655650e0e51d" }, "outputs": [ { "data": { "text/html": [ "
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1.6 573178.0 0.0 1.0 2.0 0 3 \n", "796 1.6 573178.0 1.0 1.0 1.0 0 3 \n", "797 1.6 573178.0 1.0 1.0 2.0 0 3 \n", "798 1.6 590401.0 0.0 0.0 1.0 0 3 \n", "799 1.6 590401.0 0.0 0.0 2.0 0 3 \n", "800 1.6 590401.0 1.0 0.0 1.0 0 3 \n", "801 1.6 590401.0 1.0 0.0 2.0 0 3 \n", "802 1.6 612161.0 0.0 1.0 1.0 0 3 \n", "803 1.6 612161.0 0.0 1.0 2.0 0 3 \n", "804 1.6 612161.0 1.0 1.0 1.0 0 3 \n", "805 1.6 612161.0 1.0 1.0 2.0 0 3 \n", "806 1.6 661427.0 0.0 1.0 1.0 0 3 \n", "807 1.6 661427.0 0.0 1.0 2.0 0 3 \n", "808 1.6 661427.0 1.0 1.0 1.0 0 3 \n", "809 1.6 661427.0 1.0 1.0 2.0 1 2 \n", "810 1.6 706068.0 0.0 1.0 1.0 0 3 \n", "811 1.6 706068.0 0.0 1.0 2.0 0 3 \n", "812 1.6 706068.0 1.0 1.0 1.0 0 3 \n", "813 1.6 706068.0 1.0 1.0 2.0 0 3 \n", "814 1.6 716849.0 0.0 1.0 1.0 0 3 \n", "815 1.6 716849.0 0.0 1.0 2.0 0 3 \n", "816 1.6 716849.0 1.0 1.0 1.0 0 3 \n", "817 1.6 716849.0 1.0 1.0 2.0 0 3 \n", "818 1.6 763498.0 0.0 0.0 1.0 1 2 \n", "819 1.6 763498.0 0.0 0.0 2.0 1 2 \n", "820 1.6 763498.0 1.0 0.0 1.0 2 1 \n", "821 1.6 763498.0 1.0 0.0 2.0 0 3 \n", "822 1.6 776689.0 0.0 0.0 1.0 0 3 \n", "823 1.6 776689.0 0.0 0.0 2.0 1 2 \n", "824 1.6 776689.0 1.0 0.0 1.0 0 3 \n", "825 1.6 776689.0 1.0 0.0 2.0 0 3 \n", "826 1.6 827411.0 0.0 0.0 1.0 0 3 \n", "827 1.6 827411.0 0.0 0.0 2.0 0 3 \n", "828 1.6 827411.0 1.0 0.0 1.0 0 3 \n", "829 1.6 827411.0 1.0 0.0 2.0 0 3 \n", "830 1.6 853026.0 0.0 0.0 1.0 0 3 \n", "831 1.6 853026.0 0.0 0.0 2.0 0 3 \n", "832 1.6 853026.0 1.0 0.0 1.0 0 3 \n", "833 1.6 853026.0 1.0 0.0 2.0 0 3 \n", "834 1.6 883636.0 0.0 1.0 1.0 0 3 \n", "835 1.6 883636.0 0.0 1.0 2.0 1 2 \n", "836 1.6 883636.0 1.0 1.0 1.0 1 2 \n", "837 1.6 883636.0 1.0 1.0 2.0 0 3 \n", "838 1.6 906239.0 0.0 1.0 1.0 0 3 \n", "839 1.6 906239.0 0.0 1.0 2.0 0 3 \n", "840 1.6 906239.0 1.0 1.0 1.0 0 3 \n", "841 1.6 906239.0 1.0 1.0 2.0 0 3 \n", "842 1.6 909153.0 0.0 1.0 1.0 0 3 \n", "843 1.6 909153.0 0.0 1.0 2.0 0 3 \n", "844 1.6 909153.0 1.0 1.0 1.0 0 3 \n", "845 1.6 909153.0 1.0 1.0 2.0 0 3 \n", "846 1.6 934835.0 0.0 0.0 1.0 0 3 \n", "847 1.6 934835.0 0.0 0.0 2.0 0 3 \n", "848 1.6 934835.0 1.0 0.0 1.0 0 3 \n", "849 1.6 934835.0 1.0 0.0 2.0 0 3 \n", "850 1.6 954504.0 0.0 0.0 1.0 0 3 \n", "851 1.6 954504.0 0.0 0.0 2.0 0 3 \n", "852 1.6 954504.0 1.0 0.0 1.0 0 3 \n", "853 1.6 954504.0 1.0 0.0 2.0 0 3 \n", "\n", "key_respBorN.keys Response_Ratio \n", "0 0.333333 \n", "1 0.000000 \n", "2 0.333333 \n", "3 0.000000 \n", "4 0.333333 \n", "5 0.000000 \n", "6 0.666667 \n", "7 0.666667 \n", "8 0.000000 \n", "9 0.000000 \n", "10 0.000000 \n", "11 0.000000 \n", "12 0.666667 \n", "13 0.000000 \n", "14 0.000000 \n", "15 0.000000 \n", "16 0.000000 \n", "17 0.333333 \n", "18 0.333333 \n", "19 0.333333 \n", "20 0.000000 \n", "21 0.000000 \n", "22 0.000000 \n", "23 0.000000 \n", "24 0.000000 \n", "25 0.333333 \n", "26 0.000000 \n", "27 0.333333 \n", "28 0.000000 \n", "29 0.000000 \n", "30 0.000000 \n", "31 0.000000 \n", "32 0.666667 \n", "33 0.000000 \n", "34 0.000000 \n", "35 0.000000 \n", "36 0.000000 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0.000000 \n", "93 0.000000 \n", "94 0.000000 \n", "95 0.000000 \n", "96 0.000000 \n", "97 0.000000 \n", "98 0.000000 \n", "99 0.000000 \n", "100 0.000000 \n", "101 0.000000 \n", "102 0.000000 \n", "103 0.000000 \n", "104 0.000000 \n", "105 0.000000 \n", "106 0.000000 \n", "107 0.000000 \n", "108 0.000000 \n", "109 0.000000 \n", "110 0.000000 \n", "111 0.000000 \n", "112 0.000000 \n", "113 0.333333 \n", "114 0.000000 \n", "115 0.000000 \n", "116 0.000000 \n", "117 0.000000 \n", "118 0.000000 \n", "119 0.000000 \n", "120 0.000000 \n", "121 0.000000 \n", "122 0.333333 \n", "123 0.000000 \n", "124 0.666667 \n", "125 0.333333 \n", "126 0.333333 \n", "127 0.333333 \n", "128 0.333333 \n", "129 0.333333 \n", "130 0.000000 \n", "131 0.000000 \n", "132 0.000000 \n", "133 0.000000 \n", "134 1.000000 \n", "135 0.333333 \n", "136 0.000000 \n", "137 0.333333 \n", "138 0.333333 \n", "139 1.000000 \n", "140 0.333333 \n", "141 0.666667 \n", "142 0.000000 \n", "143 0.333333 \n", "144 0.000000 \n", "145 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\n", "828 0.000000 \n", "829 0.000000 \n", "830 0.000000 \n", "831 0.000000 \n", "832 0.000000 \n", "833 0.000000 \n", "834 0.000000 \n", "835 0.333333 \n", "836 0.333333 \n", "837 0.000000 \n", "838 0.000000 \n", "839 0.000000 \n", "840 0.000000 \n", "841 0.000000 \n", "842 0.000000 \n", "843 0.000000 \n", "844 0.000000 \n", "845 0.000000 \n", "846 0.000000 \n", "847 0.000000 \n", "848 0.000000 \n", "849 0.000000 \n", "850 0.000000 \n", "851 0.000000 \n", "852 0.000000 \n", "853 0.000000 " ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_df02" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EX5EymCcowAB" }, "outputs": [], "source": [ "# Assuming you have the 'result_df02' dataframe from the previous step\n", "\n", "\"\"\"\n", "We are replicating a procedure from Klapproth and Wearden, 2011\n", "Here is citation from the paper, describing the construction of the parameter:\n", "\n", "In order to numerically examine the shift of the\n", "gradients, we calculated the weighted mean of the\n", "gradient of each participant.\n", "This was done by\n", "[1]multiplying the number of “same” responses with the\n", "duration at which a “same” response was given,\n", "[2] adding these products,\n", "and then\n", "[4] dividing this weighted sum by the number of “same” responses\n", "of each participant given at all durations (calculated at step [3] in our case)\n", "\"\"\"\n", "\n", "\n", "# [1] Calculate the weighted sum for each participant and duration combination (result_df02 already contains number of same responses as variable 'b')\n", "\n", "result_df02['Weighted_Sum'] = result_df02['b'] * result_df02['duration']\n", "\n", "# [2] Add the weighted sums to have a total for each combination of participant and values of your independent variables\n", "\"\"\"\"\n", "You will need to replace my independent variables by yours.\n", "Keep the participant in the list, though.\n", "\"\"\"\n", "grouped02 = result_df02.groupby(['participant','stress','halve', 'feedback'])['Weighted_Sum'].sum().reset_index()\n", "\n", "# [3] Calculate the total count of \"b\" responses for each participant and feedback combination\n", "grouped02['Total_b_Responses'] = result_df02.groupby(['participant', 'feedback', 'halve'])['b'].transform('sum')\n", "\n", "# [4] Calculate the weighted mean of the gradient for each participant and feedback combination\n", "grouped02['Weighted_Mean_Gradient'] = grouped02['Weighted_Sum'] / grouped02['Total_b_Responses']\n", "\n", "# If you want to save this new dataframe to a CSV file, you can use:\n", "#grouped02.to_csv(\"weighted_mean_gradient.csv\", index=False)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wfwW1LZuowAB", "outputId": "7fa966cc-2bd1-49af-b660-80c788f6a460" }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " participant stress halve feedback Weighted_Sum Total_b_Responses \\\n", "0 27171.0 1.0 1.0 0.0 11.8 11 \n", "1 27171.0 1.0 1.0 1.0 13.0 10 \n", "2 27171.0 1.0 2.0 0.0 10.6 14 \n", "3 27171.0 1.0 2.0 1.0 14.6 14 \n", "4 80551.0 0.0 1.0 0.0 10.0 10 \n", "5 80551.0 0.0 1.0 1.0 9.8 5 \n", "6 80551.0 0.0 2.0 0.0 5.4 11 \n", "7 80551.0 0.0 2.0 1.0 14.6 15 \n", "8 96604.0 1.0 1.0 0.0 8.6 8 \n", "9 96604.0 1.0 1.0 1.0 11.4 18 \n", "10 96604.0 1.0 2.0 0.0 18.6 12 \n", "11 96604.0 1.0 2.0 1.0 12.2 12 \n", "12 124367.0 0.0 1.0 0.0 14.4 16 \n", "13 124367.0 0.0 1.0 1.0 8.0 13 \n", "14 124367.0 0.0 2.0 0.0 14.0 8 \n", "15 124367.0 0.0 2.0 1.0 11.0 11 \n", "16 135784.0 0.0 1.0 0.0 11.2 11 \n", "17 135784.0 0.0 1.0 1.0 13.0 20 \n", "18 135784.0 0.0 2.0 0.0 18.6 14 \n", "19 135784.0 0.0 2.0 1.0 17.0 17 \n", "20 144507.0 0.0 1.0 0.0 15.4 15 \n", "21 144507.0 0.0 1.0 1.0 9.8 15 \n", "22 144507.0 0.0 2.0 0.0 14.8 10 \n", "23 144507.0 0.0 2.0 1.0 12.2 13 \n", "24 195972.0 0.0 1.0 0.0 15.2 16 \n", "25 195972.0 0.0 1.0 1.0 12.2 10 \n", "26 195972.0 0.0 2.0 0.0 9.4 12 \n", "27 195972.0 0.0 2.0 1.0 12.0 13 \n", "28 201266.0 1.0 1.0 0.0 13.8 13 \n", "29 201266.0 1.0 2.0 0.0 16.2 15 \n", "30 231280.0 0.0 1.0 0.0 11.6 12 \n", "31 231280.0 0.0 1.0 1.0 9.4 7 \n", "32 231280.0 0.0 2.0 0.0 6.8 11 \n", "33 231280.0 0.0 2.0 1.0 13.2 13 \n", "34 247059.0 1.0 1.0 0.0 11.4 11 \n", "35 247059.0 1.0 1.0 1.0 12.2 9 \n", "36 247059.0 1.0 2.0 0.0 9.2 12 \n", "37 247059.0 1.0 2.0 1.0 11.4 11 \n", "38 341031.0 0.0 1.0 0.0 11.8 12 \n", "39 341031.0 0.0 1.0 1.0 15.0 16 \n", "40 341031.0 0.0 2.0 0.0 17.8 15 \n", "41 341031.0 0.0 2.0 1.0 12.6 13 \n", "42 344553.0 1.0 1.0 0.0 16.0 16 \n", "43 344553.0 1.0 1.0 1.0 15.2 16 \n", "44 344553.0 1.0 2.0 0.0 16.8 15 \n", "45 344553.0 1.0 2.0 1.0 16.6 17 \n", "46 379479.0 1.0 1.0 0.0 19.6 18 \n", "47 379479.0 1.0 1.0 1.0 14.6 16 \n", "48 379479.0 1.0 2.0 0.0 16.8 15 \n", "49 379479.0 1.0 2.0 1.0 13.2 13 \n", "50 427694.0 1.0 1.0 0.0 11.2 11 \n", "51 427694.0 1.0 1.0 1.0 15.0 11 \n", "52 427694.0 1.0 2.0 0.0 10.8 14 \n", "53 427694.0 1.0 2.0 1.0 10.8 11 \n", "54 475207.0 0.0 1.0 0.0 14.6 16 \n", "55 475207.0 0.0 1.0 1.0 15.4 15 \n", "56 475207.0 0.0 2.0 0.0 14.6 15 \n", "57 475207.0 0.0 2.0 1.0 21.8 21 \n", "58 572697.0 1.0 1.0 0.0 9.8 10 \n", "59 572697.0 1.0 1.0 1.0 11.8 10 \n", "60 572697.0 1.0 2.0 0.0 8.8 12 \n", "61 572697.0 1.0 2.0 1.0 10.6 11 \n", "62 573178.0 1.0 1.0 0.0 11.8 12 \n", "63 573178.0 1.0 1.0 1.0 14.0 16 \n", "64 573178.0 1.0 2.0 0.0 14.6 14 \n", "65 573178.0 1.0 2.0 1.0 14.4 15 \n", "66 590401.0 0.0 1.0 0.0 12.4 12 \n", "67 590401.0 0.0 1.0 1.0 14.4 17 \n", "68 590401.0 0.0 2.0 0.0 18.4 14 \n", "69 590401.0 0.0 2.0 1.0 13.0 13 \n", "70 612161.0 1.0 1.0 0.0 14.8 16 \n", "71 612161.0 1.0 1.0 1.0 7.4 10 \n", "72 612161.0 1.0 2.0 0.0 9.2 8 \n", "73 612161.0 1.0 2.0 1.0 10.4 11 \n", "74 661427.0 1.0 1.0 0.0 14.6 15 \n", "75 661427.0 1.0 1.0 1.0 15.2 15 \n", "76 661427.0 1.0 2.0 0.0 15.0 16 \n", "77 661427.0 1.0 2.0 1.0 15.4 15 \n", "78 706068.0 1.0 1.0 0.0 14.2 14 \n", "79 706068.0 1.0 1.0 1.0 12.2 14 \n", "80 706068.0 1.0 2.0 0.0 14.2 13 \n", "81 706068.0 1.0 2.0 1.0 14.8 16 \n", "82 716849.0 1.0 1.0 0.0 14.8 15 \n", "83 716849.0 1.0 1.0 1.0 10.8 13 \n", "84 716849.0 1.0 2.0 0.0 12.6 11 \n", "85 716849.0 1.0 2.0 1.0 8.4 9 \n", "86 763498.0 0.0 1.0 0.0 17.4 16 \n", "87 763498.0 0.0 1.0 1.0 17.6 15 \n", "88 763498.0 0.0 2.0 0.0 17.0 15 \n", "89 763498.0 0.0 2.0 1.0 15.2 15 \n", "90 776689.0 0.0 1.0 0.0 16.0 17 \n", "91 776689.0 0.0 1.0 1.0 17.6 17 \n", "92 776689.0 0.0 2.0 0.0 17.8 17 \n", "93 776689.0 0.0 2.0 1.0 16.2 16 \n", "94 827411.0 0.0 1.0 0.0 9.2 9 \n", "95 827411.0 0.0 1.0 1.0 11.8 10 \n", "96 827411.0 0.0 2.0 0.0 10.4 12 \n", "97 827411.0 0.0 2.0 1.0 10.6 10 \n", "98 853026.0 0.0 1.0 0.0 15.6 16 \n", "99 853026.0 0.0 1.0 1.0 9.6 13 \n", "100 853026.0 0.0 2.0 0.0 12.2 10 \n", "101 853026.0 0.0 2.0 1.0 12.2 13 \n", "102 883636.0 1.0 1.0 0.0 9.0 9 \n", "103 883636.0 1.0 1.0 1.0 13.6 16 \n", "104 883636.0 1.0 2.0 0.0 16.6 13 \n", "105 883636.0 1.0 2.0 1.0 11.0 12 \n", "106 906239.0 1.0 1.0 0.0 12.8 13 \n", "107 906239.0 1.0 1.0 1.0 13.8 14 \n", "108 906239.0 1.0 2.0 0.0 13.8 14 \n", "109 906239.0 1.0 2.0 1.0 15.0 15 \n", "110 909153.0 1.0 1.0 0.0 13.6 13 \n", "111 909153.0 1.0 1.0 1.0 13.0 14 \n", "112 909153.0 1.0 2.0 0.0 13.4 13 \n", "113 909153.0 1.0 2.0 1.0 13.6 14 \n", "114 934835.0 0.0 1.0 0.0 10.6 11 \n", "115 934835.0 0.0 1.0 1.0 12.2 14 \n", "116 934835.0 0.0 2.0 0.0 14.2 12 \n", "117 934835.0 0.0 2.0 1.0 14.6 15 \n", "118 954504.0 0.0 1.0 0.0 13.0 13 \n", "119 954504.0 0.0 1.0 1.0 14.2 15 \n", "120 954504.0 0.0 2.0 0.0 15.2 15 \n", "121 954504.0 0.0 2.0 1.0 14.6 15 \n", "\n", " Weighted_Mean_Gradient \n", "0 1.072727 \n", "1 1.300000 \n", "2 0.757143 \n", "3 1.042857 \n", "4 1.000000 \n", "5 1.960000 \n", "6 0.490909 \n", "7 0.973333 \n", "8 1.075000 \n", "9 0.633333 \n", "10 1.550000 \n", "11 1.016667 \n", "12 0.900000 \n", "13 0.615385 \n", "14 1.750000 \n", "15 1.000000 \n", "16 1.018182 \n", "17 0.650000 \n", "18 1.328571 \n", "19 1.000000 \n", "20 1.026667 \n", "21 0.653333 \n", "22 1.480000 \n", "23 0.938462 \n", "24 0.950000 \n", "25 1.220000 \n", "26 0.783333 \n", "27 0.923077 \n", "28 1.061538 \n", "29 1.080000 \n", "30 0.966667 \n", "31 1.342857 \n", "32 0.618182 \n", "33 1.015385 \n", "34 1.036364 \n", "35 1.355556 \n", "36 0.766667 \n", "37 1.036364 \n", "38 0.983333 \n", "39 0.937500 \n", "40 1.186667 \n", "41 0.969231 \n", "42 1.000000 \n", "43 0.950000 \n", "44 1.120000 \n", "45 0.976471 \n", "46 1.088889 \n", "47 0.912500 \n", "48 1.120000 \n", "49 1.015385 \n", "50 1.018182 \n", "51 1.363636 \n", "52 0.771429 \n", "53 0.981818 \n", "54 0.912500 \n", "55 1.026667 \n", "56 0.973333 \n", "57 1.038095 \n", "58 0.980000 \n", "59 1.180000 \n", "60 0.733333 \n", "61 0.963636 \n", "62 0.983333 \n", "63 0.875000 \n", "64 1.042857 \n", "65 0.960000 \n", "66 1.033333 \n", "67 0.847059 \n", "68 1.314286 \n", "69 1.000000 \n", "70 0.925000 \n", "71 0.740000 \n", "72 1.150000 \n", "73 0.945455 \n", "74 0.973333 \n", "75 1.013333 \n", "76 0.937500 \n", "77 1.026667 \n", "78 1.014286 \n", "79 0.871429 \n", "80 1.092308 \n", "81 0.925000 \n", "82 0.986667 \n", "83 0.830769 \n", "84 1.145455 \n", "85 0.933333 \n", "86 1.087500 \n", "87 1.173333 \n", "88 1.133333 \n", "89 1.013333 \n", "90 0.941176 \n", "91 1.035294 \n", "92 1.047059 \n", "93 1.012500 \n", "94 1.022222 \n", "95 1.180000 \n", "96 0.866667 \n", "97 1.060000 \n", "98 0.975000 \n", "99 0.738462 \n", "100 1.220000 \n", "101 0.938462 \n", "102 1.000000 \n", "103 0.850000 \n", "104 1.276923 \n", "105 0.916667 \n", "106 0.984615 \n", "107 0.985714 \n", "108 0.985714 \n", "109 1.000000 \n", "110 1.046154 \n", "111 0.928571 \n", "112 1.030769 \n", "113 0.971429 \n", "114 0.963636 \n", "115 0.871429 \n", "116 1.183333 \n", "117 0.973333 \n", "118 1.000000 \n", "119 0.946667 \n", "120 1.013333 \n", "121 0.973333 " ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#in this version of final data frame for analysis, I have 2 measurements per participant\n", "grouped02" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Az-9Q2jIowAB", "outputId": "b86db0ee-b0f1-42d7-d497-1df91acba503" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lTGZmViJpRyDvbTvBPGA2sGBPBQptEv9IMjOqmZnloNjiNtXAsIj4VWH7GmBg4fC/RESHC61GxFJJw4rEMA24GxiXKmIzMyu5Yg3IN5MsZNPisyRtBkEyErlLJFUBFwJzunotMzPbe8UeEx1d6FLa4t2I+C6ApP8swf2/D3wpIpoldVhQ0hRgCsDQoUNLcGszM2tRLBn0a7P9kVavK0tw/xpgYSERDAbOl9QUEf/WtmBE3A7cDlBTUxMluLeZmRUUSwZvSzoqIl6AnVNZI+kDwDtdvXlE7FxDWdI84IH2EoGZmWWrWDK4HnhA0k1Ay5iCE4GvkGJ9A0l3AWcCgyXVF67XFyAi3E7QQ8yaNYu6ug77EnSooaEBgKqqqi7FMWLECKZNm9ala5j1Vh0mg4hYJOkvSdYy+Hxh9zPAX0bEM8UuHhGpV0OLiEvSlrWeZcuWLXmHYNbrFR1nUPijP6mjMpJmRYS/kvVSXf02Pn16UsmcMWNGKcIxs72Qdj2DYj5UouuYmVkOSpUMzMxsH+ZkYGZmJUsGHY8YMzOzbq1UycAtf2Zm+7BUs5ZKOgq4FviT1udExFmF3/OyCM7MzMoj7XoG/0oymdwdQHN24ZiZWR7SJoOmiLgt00jMzCw3adsM7pf0OUmHSTq45SfTyMzMrGzS1gwmF35f22pfAEeWNhwzM8tD2mUvhxcvZWZm+6q0NQMkjQZG0WqNg4jY49rGZma270jbtfR6kqmoRwG1wHnAr+hgoXszM9t3pG1A/gTJKmf/HRGXAscD+2cWlZmZlVXaZLAlInYATZIOBNbjxmMzsx4jbZvBckkHkQw6W0Gy5OWTWQVlZmbllbY30ecKL+dIWgQcGBErswvLzMzKKdVjIiX+WtLXIuIPwJuSTkpx3lxJ6yW1u0SmpE9LWln4eUzS8Z0L38zMSiFtm8GtwKlAy5rGbwO3pDhvHjC+g+MvAWdExBjgG8DtKeMxM7MSSttmcHJEjJX0NEBEvCFpv2InRcRSScM6OP5Yq83HgeqU8ZiZWQmlrRlsl1RBMgUFkoYAO0ocy98AD5b4mmZmlkLamsFM4OfAIZJuIhl38NVSBSHpz0mSwWkdlJkCTAEYOnRoqW5tZmaAIiJdQekDJAPPBPx7RKxOed4w4IGIGL2H42NIEs15EfFCmmvW1NTE8uXLU8VtHZs1axZ1dXW5xtBy/xEjRuQax4gRI5g2bVquMYD/n7TWXf6f9BSSVkRETXvHOqwZtJmmej1wV+tjEfF6FwMbCtwDfCZtIrDSqqurY82zTzN0YH5rFu23PXlaufXl/BL82ncqcrt3W3V1dfzmmdU0D8hvlvj3bUu+JK548bXcYqh4t0t/XqyTij0m2gjUA02F7dYL3xedwlrSXSRzGg2WVA9cD/QFiIg5wNeASuBWSZAsotNu1rLsDB3YzFfGvpV3GLn65lMH5h3CLpoHHMyWD5yfdxi56v98bd4h9CrFksEskj/mvyapFfwq0j5XAiJiYpHjlwOXp72emZllo8PeRBExHfgzkjWQPwM8Lenbkry+gZlZD1K0a2kklgB/D8wBLgXOzjowMzMrn2INyH8EfBy4CBhC0tg7NiJeKUNsZmZWJsXaDNYDa0jaC+pIGo3HSRoHEBH3ZBuemZmVQ7Fk8K8kCeADhZ/WgqSmYGZm+7gOk0FEXJLmIpImR8T8kkRkZmZll3ZuomKml+g6ZmaWg1IlAxUvYmZm3VXaieqKST0Qrafo6vwxDQ0NAFRVVXUpDs/dYmalUKpk4JpBJ23ZsiXvEMzMdipVMvh1ia6zz+jqt/Hp05NmlhkzZpQiHOtBGhoaqHh3U6+fm6fi3UYaGpqKF7SSKDbo7JqOjkfE9wq/p5YyKDMzK69iNYMDCr+PBsYB9xW2/wJYmlVQZr1ZVVUV/721j2ctfb6WqqpDu3SNUqwNUYr2vX2hba/YOIMbASQ9TDINxduF7RtIBqSZmfVovaV9L22bwVBgW6vtbcCwkkdjZlZCpfg23lva99Img/8HPCnp5yTdSC8EFmQWlZmZlVWqZBARN0l6EPhwYdelEfF0dmFlL+91Zlvu3fKtIy8NDQ0MzjUCM+sOOtO1dADwVkT8SNIQScMj4qWsAsta3uvMdpc1Zgf261tYiNTMerNUyUDS9UANSa+iH5H8+fhn4ENFzpsLfAxYHxGj2zkuYAZwPvAucElEPNWZN9AVvX2d2f7P18KOt/MOw2yPGhoacq89d4dafDl6I6WtGVwInAA8BRARr0o6oONTAJgHzGbP7QvnASMLPycDtxV+m5mxZcsW1jz7NEMHNucWw37bkynctr68PJf7r32noiz3SZsMtkVESArYuQJaURGxVNKwDop8HFgQEQE8LukgSYdFxLqUce01j/JMRnhuVfgxkXVrQwc285Wxb+UdRm6++dSBZblP2llLfyrpB8BBkv4WeAT4YQnuXwW0XkKzvrBvN5KmSFouafmGDRtKcGszM2uRtjfRdySdA7xF0m7wtYhYXIL7tzfBXbszoEbE7cDtADU1NV2eJdWjPJM2g4E73gZ6x6AaM9uztA3I/xgRXwIWt7OvK+qBI1ptVwOvdvGaZtZDbN26lZffqyjbo5Lu6OW3K/ijwpQYWUrbZnAO0PYP/3nt7Ous+4CpkhaSNBxvKkd7QYuKd1/Prc3gfe8lz0B39MvvQ17x7uvQzw0GZlZ81tIrgc8BR0pa2erQAaSYtlrSXcCZwGBJ9cD1FJorI2IOUEvSrbSOpGvppZ1/C3tnxIgR5bpVu+rqki6dI47s2kRcXXNoMglX05s5xmC2Z/vvvz9H9N3S6xuQ9+/iIlhpFKsZ/AvwIPB/gS+32v92RLxe7OIRMbHI8QCuKnadLOQ9g2B3me9k+vTpbH25bJUxs05b+06+j4leezfpZ3PogB253H/tOxWMLMN9is1augnYBEyUVAEcWjhnoKSBEbG2DDGaWS/Vv39/qnKuxW8rDDrb/0/yiWMk5XmSkbYBeSpwA/Aa0JIeAxiTTVhWLg0NDWx+u3c30EH5Gumsc6qqqrpF7Rnyr8VnLW0D8tXA0RHRmGEsZmaWk7TJ4BWSx0XWw1RVVbG1aV2vbqCD8jXSmXVXaddAfhF4VNIvgK0tx1vWQDYzs31b2jWQ1xZ+9iv8mJl1e6VYt6QUs5b2mDWQzcx6q/79++cdQlmk7U10P7vPGbQJWA78ICLeK3Vg3V1Xv3GUao70feEbh3VenqPjoRuNkKdrgzL9byO9tA3ILwJDgLsK2xeRdDM9CrgD+EzpQ+vZesu3Deu8vEfHQ/cZId8d/lv0FmmTwQkRcXqr7fslLY2I0yU9m0Vg3Z2/cVhWusNnq7f0rbf/kXY9gyGShrZsFF63rKO+reRRmZlZWaWtGXwR+JWk35OsQTAc+FxhxbP5WQVnZmblkXZxm1pJI4EPkCSD51s1Gn8/o9jMzKxMig06Oysi/kPSX7Y5dKQkIuKeDGMzM7MyKVYzOAP4D+Av2jkWgJNBD9DbpwiG8k0TbNZdFRt0dn3hd9kWnbHy6g5d9/KeIhjKN02wWXeVdtDZocA3gcMj4jxJo4BTI+LOTKOzzLkbo5lB+q6l84CHgMML2y+QTGttZmY9QNpkMDgifkphYZuIaAKa05woabyk30mqk/Tldo4PknS/pN9KelaSH0mZmZVZ2mSwWVIlhfmJJJ1CivUNCktl3gKcB4wiWT5zVJtiVwHPRcTxwJnAdyV5ZlQzszIq1rX0auDXwN8D95J0Kf01yTxFn0xx/ZOAuoh4sXC9hcDHgedalQngAEkCBgKvA02dextmZtYVxWoG1cAMYFGh7GLgX4APRsRvU1y/imSVtBb1hX2tzQaOAV4FVgHTI2K3PoaSpkhaLmn5hg0bUtzazMzS6jAZRMTfRcQHgT8G/g54AjgLWCnpuY7OLVB7l22zfS7wG5LG6T8DZkvardN7RNweETURUTNkyJAUtzYzs7TSthn0Bw4EBhV+XiVJDMXUA0e02q4unNvapcA9kagDXiKZ9sLMzMqkWJvB7cCxwNskf/wfA74XEW+kvP4yYKSk4UADcDHwqTZl1gIfAf6zMJ7haJL1E8zMrEyKDTobCuwPrCH5Y14PvJn24hHRJGkqyRiFCmBuRDwr6YrC8TnAN4B5klaRPFb6UkRs7OwbMTOzvVdsOorxhV4+xwIfJJnKerSk14H/apmuosg1aoHaNvvmtHr9KvDRvYjdzMxKpOh0FBERwDOS3iQZW7AJ+BhJt9GiycDMzLq/Ym0GnyepEXwI2E4y5uC/gLkk3UDNzKwHKFYzGAb8DPhCRKzLPhwzM8tDsTaDa8oViJmZ5SftOAMzM+vBnAzMzMzJwMzMnAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzEix0llXSRoPzCBZA/mHEfGtdsqcCXwf6AtsjIgzso7LSmfWrFnU1dXt9fkt506fPr1LcYwYMYJp06Z16RpmvVWmyUBSBXALcA5QDyyTdF9EPNeqzEHArcD4iFgr6ZAsY7Lup3///nmHYNbrZV0zOAmoi4gXASQtBD4OPNeqzKeAeyJiLUBErM84Jisxfxs32/dl3WZQBbzSaru+sK+1o4D3S3pU0gpJk9q7kKQpkpZLWr5hw4aMwjUz652yTgZqZ1+02e4DnAhcAJwLXCfpqN1Oirg9ImoiombIkCGlj9TMrBfL+jFRPXBEq+1q4NV2ymyMiM3AZklLgeOBFzKOzczMCrKuGSwDRkoaLmk/4GLgvjZl7gU+LKmPpAHAycDqjOMyM7NWMq0ZRESTpKnAQyRdS+dGxLOSrigcnxMRqyUtAlYCO0i6nz6TZVxmZrarzMcZREQtUNtm35w22zcDN2cdi5mZtc8jkM3MzMnAzMycDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzowyDzsysvLq62BCUZsEhLza0b3EyMLPdeMGh3sfJwKyH8bdx2xtuMzA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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#here we can plot the WMG for each group in each block\n", "sns.boxplot(data=grouped02, x=\"halve\", y=\"Weighted_Mean_Gradient\", hue='feedback')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "33LrXxIRowAC" }, "outputs": [], "source": [ "# SKIP THIS TOO, UNLESS YOU WANT TO REMOVE A PARTICIPANT FROM THE RESULT_DF\n", "\n", "# we create a subset of the data, excluding participant with incomplete experiment (SKIP THIS AT FIRST)\n", "grouped02 = grouped02[grouped02['participant'] != 201266]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "22a5TXSBowAC", "outputId": "7381bfc5-a034-4501-89da-8f7b8869cda4" }, "outputs": [ { "data": { "text/html": [ "
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SourceSSddof1ddof2MSFp-uncp-GG-corrnp2eps
0halve0.0176861290.0176860.2423950.6261880.6261880.0082891.0
1feedback0.0443041290.0443040.5833300.4511790.4511790.0197181.0
2halve * feedback0.0460941290.04609410.0053770.0036460.0036460.2565131.0
\n", "
" ], "text/plain": [ " Source SS ddof1 ddof2 MS F p-unc \\\n", "0 halve 0.017686 1 29 0.017686 0.242395 0.626188 \n", "1 feedback 0.044304 1 29 0.044304 0.583330 0.451179 \n", "2 halve * feedback 0.046094 1 29 0.046094 10.005377 0.003646 \n", "\n", " p-GG-corr np2 eps \n", "0 0.626188 0.008289 1.0 \n", "1 0.451179 0.019718 1.0 \n", "2 0.003646 0.256513 1.0 " ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pg.rm_anova(data=grouped02, within=['halve', 'feedback'], dv='Weighted_Mean_Gradient', subject='participant')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YbBAPVLIowAC", "outputId": "b9b743f8-7f5f-41ce-adda-742c10d734f5" }, "outputs": [ { "data": { "text/html": [ "
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Tdoftailp-valCI95%cohen-dBF10power
T-test-0.01461358two-sided0.988391[-0.11, 0.1]0.0037730.2620.050024
\n", "
" ], "text/plain": [ " T dof tail p-val CI95% cohen-d BF10 \\\n", "T-test -0.014613 58 two-sided 0.988391 [-0.11, 0.1] 0.003773 0.262 \n", "\n", " power \n", "T-test 0.050024 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "WMG under no-feedback condition: 0.9988255589726179\n", "WMG under feedback condition: 0.9995942303883478\n", "mean difference: -0.0007686714157298935\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#ttest for within factor\n", "data = grouped02[grouped02['halve'] == 1]\n", "\n", "x = data[(data[\"feedback\"]==0)][\"Weighted_Mean_Gradient\"]\n", "y = data[(data[\"feedback\"]==1)][\"Weighted_Mean_Gradient\"]\n", "display(pg.ttest(x,y, paired=False))\n", "sns.boxplot(data=data, x=\"feedback\", y=\"Weighted_Mean_Gradient\")\n", "print(\"WMG under no-feedback condition:\", x.mean())\n", "print(\"WMG under feedback condition:\", y.mean())\n", "mean_diff = x.mean() - y.mean()\n", "print(\"mean difference:\", mean_diff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1BqpZartowAC", "outputId": "d09278d6-ce3b-48a2-d87f-7368dcd2435d" }, "outputs": [ { "data": { "text/html": [ "
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Tdoftailp-valCI95%cohen-dBF10power
T-test1.53033458two-sided0.13137[-0.02, 0.18]0.3951310.6970.324841
\n", "
" ], "text/plain": [ " T dof tail p-val CI95% cohen-d BF10 \\\n", "T-test 1.530334 58 two-sided 0.13137 [-0.02, 0.18] 0.395131 0.697 \n", "\n", " power \n", "T-test 0.324841 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "WMG under no-feedback condition: 1.0623034759358287\n", "WMG under feedback condition: 0.984676364485188\n", "mean difference: 0.07762711145064072\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#ttest for within factor\n", "data = grouped02[grouped02['halve'] == 2]\n", "\n", "x = data[(data[\"feedback\"]==0)][\"Weighted_Mean_Gradient\"]\n", "y = data[(data[\"feedback\"]==1)][\"Weighted_Mean_Gradient\"]\n", "display(pg.ttest(x,y, paired=False))\n", "sns.boxplot(data=data, x=\"feedback\", y=\"Weighted_Mean_Gradient\")\n", "print(\"WMG under no-feedback condition:\", x.mean())\n", "print(\"WMG under feedback condition:\", y.mean())\n", "mean_diff = x.mean() - y.mean()\n", "print(\"mean difference:\", mean_diff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "13PJke9dowAC", "outputId": "25a05b2a-f2a2-441b-9680-527fc9527544" }, "outputs": [ { "data": { "text/html": [ "
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ContrasthalveABPairedParametricTdofTailp-uncBF10cohen
0halve-12TrueTrue-0.49233729.0two-sided0.6261880.217-0.172075
1feedback-01TrueTrue0.76376129.0two-sided0.4511790.2540.267380
2halve * feedback101TrueTrue-0.01488429.0two-sided0.9882270.194-0.003773
3halve * feedback201TrueTrue1.49299029.0two-sided0.1462410.5270.395131
\n", "
" ], "text/plain": [ " Contrast halve A B Paired Parametric T dof Tail \\\n", "0 halve - 1 2 True True -0.492337 29.0 two-sided \n", "1 feedback - 0 1 True True 0.763761 29.0 two-sided \n", "2 halve * feedback 1 0 1 True True -0.014884 29.0 two-sided \n", "3 halve * feedback 2 0 1 True True 1.492990 29.0 two-sided \n", "\n", " p-unc BF10 cohen \n", "0 0.626188 0.217 -0.172075 \n", "1 0.451179 0.254 0.267380 \n", "2 0.988227 0.194 -0.003773 \n", "3 0.146241 0.527 0.395131 " ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pg.pairwise_ttests(data=grouped02, within=['halve', 'feedback'], dv='Weighted_Mean_Gradient', subject='participant', effsize='cohen')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vfpdj7VbowAD", "outputId": "f700daa0-fd3d-4268-b17b-f80b828d900a" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#here we can plot the WMG for each group in each block\n", "sns.boxplot(data=grouped02, x=\"halve\", y=\"Weighted_Mean_Gradient\", hue='stress')" ] } ], "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": "3.8.5" }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }