├── .gitignore ├── ANOVA_in_R_Repeated_Measures_Example.ipynb ├── Descriptive_Statistics_in_Python.csv ├── Jupyter_extension_rmagic_rpy2_tutorial.ipynb ├── MANOVA_Test_in_Python_Statsmodels_Example.ipynb ├── NamesAndAges.xlsx ├── Pandas Scatter Plot Tutorial.ipynb ├── Pandas_Dataframe_Tutorial_Example_Code.ipynb ├── Pandas_read_html_example_code.ipynb ├── Python Pandas CSV.ipynb ├── Python_ANOVA ├── One_Way_Python_ANOVA.ipynb ├── Python repeated measures ANOVA.ipynb ├── Python_ANOVA_Factorial_Using_Statsmodels.ipynb ├── Repeated measures ANOVA using R and afex.ipynb ├── Two_Way_ANOVA_in_Python_Tutorial.ipynb ├── Two_Way_ANOVA_in_Python_using_Pingouin_Stats.ipynb ├── pingouin-code-ANOVA-in-Python.ipynb ├── rmAOV1way.csv └── rmAOV2way.csv ├── README.md ├── R_Notebooks ├── concat_columns_in_R.ipynb ├── reading_and_writing_stata_files_in_R.ipynb ├── reverse_coding_in_R.ipynb └── scatter_plot_in_R_tutorial.ipynb ├── Raincloud_Plots_in_Python.ipynb ├── Reading Multiple Spreadsheets using Pandas.ipynb ├── Rename_Columns_in_Pandas_Dataframe.ipynb ├── Rpy2 and R plots in a Jupyter Notebook!.ipynb ├── Scatterplot_example.ipynb ├── Seaborn_Scatterplot_Tutorial_Example_Code.ipynb ├── Selecting Specific Rows & Columns of Pandas Dataframe Objects.ipynb ├── SimData ├── DF_NA_Janitor.csv ├── Data_to_Transform.csv ├── FifthDayData.csv ├── FifthDayData.dta ├── FirstDayData.csv ├── FirstDayData.dta ├── FirstDayData.sav ├── FourthDayData.csv ├── MissingData.csv ├── NewFifthDayData.dta ├── SecondDayData.csv ├── SixthDayData.csv ├── ThirdDayData.csv ├── add_column.xlsx ├── add_column2.xlsx ├── correlationMatrixPython.csv ├── example_concat.dta ├── example_concat.xlsx ├── example_concat1.xlsx ├── example_concat3.xlsx ├── example_sheets.xlsx ├── example_sheets1.xlsx ├── example_sheets2.xlsx ├── exfample_concat.sav ├── mannwu.csv ├── paired_samples_data.csv ├── play_data.csv ├── play_data.xlsx ├── play_data2.dta ├── python_absolute_value.csv ├── skiprow.csv ├── survey_1.sav └── survey_2.sav ├── YT ├── Pandas_Groupby_Tutorial_Part_I.ipynb └── paired_samples_t-test_python_scipy_pingouin.ipynb ├── convert_html_jupyter_notebook_tutorial.ipynb ├── convert_numpy_float_array_to_integer_array_Python.ipynb ├── correlation_matrix_in_python.ipynb ├── descriptive_statistics_example_code.ipynb ├── descriptive_stats_using_numpy_python.ipynb ├── example_sheets2.xlsx ├── flanks.csv ├── getting_the_highest_value_from_dictionary.ipynb ├── how-to-get-column-names-Pandas-python.ipynb ├── how-to-make-histogram-in-pandas-python.ipynb ├── how_to_convert_a_dictionary_to_pandas_dataframe.ipynb ├── how_to_read_SPSS_sav_files_in_Python.ipynb ├── how_to_read_xlsx_files_in_Python_using_pylightxl.ipynb ├── how_to_use_pandas_groupby_method.ipynb ├── json_in_python_and_pandas.ipynb ├── kde-cdf-delta-caf-plots.ipynb ├── mann_whitney_u_test_Python.ipynb ├── multiple_Sheets.xlsx ├── names_ages.xlsx ├── newfilename.xlsx ├── pairplot.eps ├── pandas_dataframe_tutorial.ipynb ├── pandas_get_dummies_how_to_create_dummy_variables.ipynb ├── pandas_groupby_tutorial.ipynb ├── percentage_rank_per_disciplin.csv ├── pingouin_repeated_measures_anova_python.ipynb ├── pyjanitor_data_cleaning_adding_columns_removing_columns_pandas.ipynb ├── pyreadstat_and_pandas_read_stata_tutorial_code.ipynb ├── python_descriptive_statistics_sample.ipynb ├── reading_STATA_files_using_Pandas.ipynb ├── reverse_pandas_dataframe.ipynb ├── rpy2 tutorial example code.ipynb ├── scatter_matrix.ipynb ├── scatter_plot_in_Python_with_ggplot.ipynb ├── seaborn_line_graphs_guide_code_examples.ipynb └── two_sample_t-test_Python.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | -------------------------------------------------------------------------------- /Descriptive_Statistics_in_Python.csv: -------------------------------------------------------------------------------- 1 | iv1,iv2,median,std,mean,trimmed_mean 2 | noise,1,962.4748045284091,142.87145681209597,979.242534213332,977.6071917797902 3 | noise,2,1180.3526031136053,112.85542102711946,1173.7510775386322,1175.6839204283096 4 | noise,3,1293.4332963142926,128.05319406285818,1298.5035528918638,1293.735855849951 5 | quiet,1,477.3728301826787,109.92102618993482,496.2728667850206,500.21628409947743 6 | quiet,2,588.6391119296707,75.52068470037284,586.9715252257087,581.2882267649697 7 | quiet,3,791.8927469418701,132.92534730335424,819.0129315952987,814.8878002369454 8 | -------------------------------------------------------------------------------- /Jupyter_extension_rmagic_rpy2_tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Display R Plots in Python: the rmagic Method \n", 8 | "\n", 9 | "In this Jupyter Notebook you will find an rpy2 code example in which we use the Python package rpy2 to create R plots. We use the r-packages 'graphics' and 'ggplot2' and display them here in this Jupyter Notebook.\n", 10 | "\n", 11 | "This is an alternative method, compared to the one used in the [YouTube Video about using Rpy2 and plotting in Jupyter notebooks](https://youtu.be/RK-n78ZOXUg)" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "%load_ext rpy2.ipython\n", 21 | "%Rdevice png" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 2, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "image/png": 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32 | }, 33 | "metadata": {}, 34 | "output_type": "display_data" 35 | } 36 | ], 37 | "source": [ 38 | "%%R\n", 39 | "barplot(c(1,3,2,5,4), ylab=\"value\")\n" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 3, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "image/png": 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50 | }, 51 | "metadata": {}, 52 | "output_type": "display_data" 53 | } 54 | ], 55 | "source": [ 56 | "%%R\n", 57 | "require(ggplot2)\n", 58 | "gg <- ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point(aes(colour='qsec')) + theme_bw()\n", 59 | "\n", 60 | "gg" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": null, 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [] 69 | } 70 | ], 71 | "metadata": { 72 | "kernelspec": { 73 | "display_name": "Python 3", 74 | "language": "python", 75 | "name": "python3" 76 | }, 77 | "language_info": { 78 | "codemirror_mode": { 79 | "name": "ipython", 80 | "version": 3 81 | }, 82 | "file_extension": ".py", 83 | "mimetype": "text/x-python", 84 | "name": "python", 85 | "nbconvert_exporter": "python", 86 | "pygments_lexer": "ipython3", 87 | "version": "3.7.3" 88 | } 89 | }, 90 | "nbformat": 4, 91 | "nbformat_minor": 2 92 | } 93 | -------------------------------------------------------------------------------- /MANOVA_Test_in_Python_Statsmodels_Example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## How to Carry out MANOVA in Python\n", 8 | "Step one is to import Pandas and MANOVA:" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "from statsmodels.multivariate.manova import MANOVA" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "Step two is to load data and rename columns:" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [ 33 | { 34 | "data": { 35 | "text/html": [ 36 | "
\n", 37 | "\n", 50 | "\n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | "
Sepal_LengthSepal_WidthPetal_LengthPetal_WidthSpecies
15.13.51.40.2setosa
24.93.01.40.2setosa
34.73.21.30.2setosa
44.63.11.50.2setosa
55.03.61.40.2setosa
\n", 104 | "
" 105 | ], 106 | "text/plain": [ 107 | " Sepal_Length Sepal_Width Petal_Length Petal_Width Species\n", 108 | "1 5.1 3.5 1.4 0.2 setosa\n", 109 | "2 4.9 3.0 1.4 0.2 setosa\n", 110 | "3 4.7 3.2 1.3 0.2 setosa\n", 111 | "4 4.6 3.1 1.5 0.2 setosa\n", 112 | "5 5.0 3.6 1.4 0.2 setosa" 113 | ] 114 | }, 115 | "execution_count": 2, 116 | "metadata": {}, 117 | "output_type": "execute_result" 118 | } 119 | ], 120 | "source": [ 121 | "url = 'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv'\n", 122 | "df = pd.read_csv(url, index_col=0)\n", 123 | "df.columns = df.columns.str.replace(\".\", \"_\") \n", 124 | "df.head()" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "### Python MANOVA Example\n", 132 | "Third step is to carry out the MANOVA using *from_formula*:" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 3, 138 | "metadata": {}, 139 | "outputs": [], 140 | "source": [ 141 | "maov = MANOVA.from_formula('Sepal_Length + Sepal_Width + \\\n", 142 | " Petal_Length + Petal_Width ~ Species', data=df)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "Final step is to print the results using *mv_test*:" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 4, 155 | "metadata": {}, 156 | "outputs": [ 157 | { 158 | "name": "stdout", 159 | "output_type": "stream", 160 | "text": [ 161 | " Multivariate linear model\n", 162 | "================================================================\n", 163 | " \n", 164 | "----------------------------------------------------------------\n", 165 | " Intercept Value Num DF Den DF F Value Pr > F\n", 166 | "----------------------------------------------------------------\n", 167 | " Wilks' lambda 0.0170 4.0000 144.0000 2086.7720 0.0000\n", 168 | " Pillai's trace 0.9830 4.0000 144.0000 2086.7720 0.0000\n", 169 | " Hotelling-Lawley trace 57.9659 4.0000 144.0000 2086.7720 0.0000\n", 170 | " Roy's greatest root 57.9659 4.0000 144.0000 2086.7720 0.0000\n", 171 | "----------------------------------------------------------------\n", 172 | " \n", 173 | "----------------------------------------------------------------\n", 174 | " Species Value Num DF Den DF F Value Pr > F\n", 175 | "----------------------------------------------------------------\n", 176 | " Wilks' lambda 0.0234 8.0000 288.0000 199.1453 0.0000\n", 177 | " Pillai's trace 1.1919 8.0000 290.0000 53.4665 0.0000\n", 178 | " Hotelling-Lawley trace 32.4773 8.0000 203.4024 582.1970 0.0000\n", 179 | " Roy's greatest root 32.1919 4.0000 145.0000 1166.9574 0.0000\n", 180 | "================================================================\n", 181 | "\n" 182 | ] 183 | } 184 | ], 185 | "source": [ 186 | "print(maov.mv_test())" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": null, 192 | "metadata": {}, 193 | "outputs": [], 194 | "source": [] 195 | } 196 | ], 197 | "metadata": { 198 | "kernelspec": { 199 | "display_name": "Python 3", 200 | "language": "python", 201 | "name": "python3" 202 | }, 203 | "language_info": { 204 | "codemirror_mode": { 205 | "name": "ipython", 206 | "version": 3 207 | }, 208 | "file_extension": ".py", 209 | "mimetype": "text/x-python", 210 | "name": "python", 211 | "nbconvert_exporter": "python", 212 | "pygments_lexer": "ipython3", 213 | "version": "3.7.4" 214 | } 215 | }, 216 | "nbformat": 4, 217 | "nbformat_minor": 2 218 | } 219 | -------------------------------------------------------------------------------- /NamesAndAges.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/NamesAndAges.xlsx -------------------------------------------------------------------------------- /Pandas_read_html_example_code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "\n", 11 | "url = 'https://en.wikipedia.org/wiki/List_of_Capsicum_cultivars'\n", 12 | "data = pd.read_html(url, flavor='bs4', header=0, encoding='UTF8')" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "metadata": {}, 19 | "outputs": [], 20 | "source": [ 21 | "# Let's remove the last table \n", 22 | "del data[-1]" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "species = ['Capsicum annum', 'Capsicum baccatum', 'Capsicum chinense',\n", 32 | " 'Capsicum frutescens', 'Capsicum pubescens']\n", 33 | "\n", 34 | "\n", 35 | "for i in range(len(species)):\n", 36 | " data[i]['Species'] = species[i]" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 5, 42 | "metadata": {}, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/html": [ 47 | "
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ImageNameTypeOriginHeatPod sizeDescriptionSpecies
0NaNAleppoNaNSyria and Turkey15,000 SRNaNGrown in Syria and Turkey and used, in coarsel...Capsicum annum
1NaNAnaheim[14]AnaheimUnited States500–2,500 SR15 cm (5.9 in)A mild variety of New Mexico chile. It was lat...Capsicum annum
2NaNBananaWaxyNaN0–500 SR15 cm (5.9 in)Often it is pickled and used as an ingredient ...Capsicum annum
3NaNBird's EyeSmall hotSoutheast Asia50,000–100,000[15] SR4 cm (1.6 in)A Southeast Asian cultivar known by many local...Capsicum annum
4NaNBlack Hungarian[16]Ornamental/ CulinaryHungary5,000–10,000 SR5–7 cm (≈ 2–3 in)Grows in a conical shape with a slight curve n...Capsicum annum
\n", 133 | "
" 134 | ], 135 | "text/plain": [ 136 | " Image Name Type Origin \\\n", 137 | "0 NaN Aleppo NaN Syria and Turkey \n", 138 | "1 NaN Anaheim[14] Anaheim United States \n", 139 | "2 NaN Banana Waxy NaN \n", 140 | "3 NaN Bird's Eye Small hot Southeast Asia \n", 141 | "4 NaN Black Hungarian[16] Ornamental/ Culinary Hungary \n", 142 | "\n", 143 | " Heat Pod size \\\n", 144 | "0 15,000 SR NaN \n", 145 | "1 500–2,500 SR 15 cm (5.9 in) \n", 146 | "2 0–500 SR 15 cm (5.9 in) \n", 147 | "3 50,000–100,000[15] SR 4 cm (1.6 in) \n", 148 | "4 5,000–10,000 SR 5–7 cm (≈ 2–3 in) \n", 149 | "\n", 150 | " Description Species \n", 151 | "0 Grown in Syria and Turkey and used, in coarsel... Capsicum annum \n", 152 | "1 A mild variety of New Mexico chile. It was lat... Capsicum annum \n", 153 | "2 Often it is pickled and used as an ingredient ... Capsicum annum \n", 154 | "3 A Southeast Asian cultivar known by many local... Capsicum annum \n", 155 | "4 Grows in a conical shape with a slight curve n... Capsicum annum " 156 | ] 157 | }, 158 | "execution_count": 5, 159 | "metadata": {}, 160 | "output_type": "execute_result" 161 | } 162 | ], 163 | "source": [ 164 | "df = pd.concat(data, sort=False) \n", 165 | "df.head()" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": 8, 171 | "metadata": {}, 172 | "outputs": [], 173 | "source": [ 174 | "import re\n", 175 | "import numpy as np\n", 176 | "\n", 177 | "# Remove brackets and whats between them (e.g. [14])\n", 178 | "df['Name'] = df['Name'].map(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x)\n", 179 | " if isinstance(x, str) else np.NaN)\n", 180 | "\n", 181 | "# Pod Size get cm\n", 182 | "df['Pod size'] = df['Pod size'].map(lambda x: x.split(' ', 1)[0].rstrip('cm') \n", 183 | " if isinstance(x, str) else np.NaN)\n", 184 | "\n", 185 | "# Taking the largest number in a range and convert all values to float\n", 186 | "df['Pod size'] = df['Pod size'].map(lambda x: x.split('–', 1)[-1]\n", 187 | " if isinstance(x, str) else np.NaN)\n", 188 | "# Convert to float\n", 189 | "df['Pod size'] = df['Pod size'].map(lambda x: float(x))\n", 190 | "\n", 191 | "# Taking the largest SHU\n", 192 | "df['Heat'] = df['Heat'].map(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x) \n", 193 | " if isinstance(x, str) else np.NaN)\n", 194 | "df['Heat'] = df['Heat'].str.replace(',', '')\n", 195 | "df['Heat'] = df['Heat'].map(lambda x: float(re.findall(r'\\d+(?:,\\d+)?', x)[-1])\n", 196 | " if isinstance(x, str) else np.NaN)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 9, 202 | "metadata": {}, 203 | "outputs": [ 204 | { 205 | "data": { 206 | "text/plain": [ 207 | "Image 65\n", 208 | "Name 0\n", 209 | "Type 39\n", 210 | "Origin 28\n", 211 | "Heat 9\n", 212 | "Pod size 33\n", 213 | "Description 8\n", 214 | "Species 0\n", 215 | "dtype: int64" 216 | ] 217 | }, 218 | "execution_count": 9, 219 | "metadata": {}, 220 | "output_type": "execute_result" 221 | } 222 | ], 223 | "source": [ 224 | "df.isna().sum()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 10, 230 | "metadata": {}, 231 | "outputs": [ 232 | { 233 | "data": { 234 | "text/plain": [ 235 | "Capsicum annum 39\n", 236 | "Capsicum chinense 17\n", 237 | "Capsicum frutescens 4\n", 238 | "Capsicum baccatum 3\n", 239 | "Capsicum pubescens 2\n", 240 | "Name: Species, dtype: int64" 241 | ] 242 | }, 243 | "execution_count": 10, 244 | "metadata": {}, 245 | "output_type": "execute_result" 246 | } 247 | ], 248 | "source": [ 249 | "df['Species'].value_counts()" 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": null, 255 | "metadata": {}, 256 | "outputs": [], 257 | "source": [] 258 | } 259 | ], 260 | "metadata": { 261 | "kernelspec": { 262 | "display_name": "Python 3", 263 | "language": "python", 264 | "name": "python3" 265 | }, 266 | "language_info": { 267 | "codemirror_mode": { 268 | "name": "ipython", 269 | "version": 3 270 | }, 271 | "file_extension": ".py", 272 | "mimetype": "text/x-python", 273 | "name": "python", 274 | "nbconvert_exporter": "python", 275 | "pygments_lexer": "ipython3", 276 | "version": "3.7.1" 277 | } 278 | }, 279 | "nbformat": 4, 280 | "nbformat_minor": 2 281 | } 282 | -------------------------------------------------------------------------------- /Python_ANOVA/Python repeated measures ANOVA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Python Repeated Measures ANOVA using Statsmodels AnovaRM\n", 8 | "This code examples show you how to carry out a repeated measures ANOVA using Statsmodels AnovaRM. This notbooke contains the code for the YouTube [Tutorial on how to carry out repeated measures anova](https://youtu.be/_X3g-dvlMF0) in Python. You will learn both one-way and two-way anova for repeated measures by watching the mentioned video.", 9 | "\n\n There are also more Python ANOVA guides found on [https://www.marsja.se](https://www.marsja.se])"] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 6, 14 | "metadata": {}, 15 | "outputs": [ 16 | { 17 | "name": "stdout", 18 | "output_type": "stream", 19 | "text": [ 20 | "Help on class AnovaRM in module statsmodels.stats.anova:\n", 21 | "\n", 22 | "class AnovaRM(builtins.object)\n", 23 | " | Repeated measures Anova using least squares regression\n", 24 | " | \n", 25 | " | The full model regression residual sum of squares is\n", 26 | " | used to compare with the reduced model for calculating the\n", 27 | " | within-subject effect sum of squares [1].\n", 28 | " | \n", 29 | " | Currently, only fully balanced within-subject designs are supported.\n", 30 | " | Calculation of between-subject effects and corrections for violation of\n", 31 | " | sphericity are not yet implemented.\n", 32 | " | \n", 33 | " | Parameters\n", 34 | " | ----------\n", 35 | " | data : DataFrame\n", 36 | " | depvar : string\n", 37 | " | The dependent variable in `data`\n", 38 | " | subject : string\n", 39 | " | Specify the subject id\n", 40 | " | within : a list of string(s)\n", 41 | " | The within-subject factors\n", 42 | " | between : a list of string(s)\n", 43 | " | The between-subject factors, this is not yet implemented\n", 44 | " | aggregate_func : None, 'mean', or function\n", 45 | " | If the data set contains more than a single observation per subject\n", 46 | " | and cell of the specified model, this function will be used to\n", 47 | " | aggregate the data before running the Anova. `None` (the default) will\n", 48 | " | not perform any aggregation; 'mean' is s shortcut to `numpy.mean`.\n", 49 | " | An exception will be raised if aggregation is required, but no\n", 50 | " | aggregation function was specified.\n", 51 | " | \n", 52 | " | Returns\n", 53 | " | -------\n", 54 | " | results: AnovaResults instance\n", 55 | " | \n", 56 | " | Raises\n", 57 | " | ------\n", 58 | " | ValueError\n", 59 | " | If the data need to be aggregated, but `aggregate_func` was not\n", 60 | " | specified.\n", 61 | " | \n", 62 | " | Notes\n", 63 | " | -----\n", 64 | " | This implementation currently only supports fully balanced designs. If the\n", 65 | " | data contain more than one observation per subject and cell of the design,\n", 66 | " | these observations need to be aggregated into a single observation\n", 67 | " | before the Anova is calculated, either manually or by passing an aggregation\n", 68 | " | function via the `aggregate_func` keyword argument.\n", 69 | " | Note that if the input data set was not balanced before performing the\n", 70 | " | aggregation, the implied heteroscedasticity of the data is ignored.\n", 71 | " | \n", 72 | " | References\n", 73 | " | ----------\n", 74 | " | .. [*] Rutherford, Andrew. Anova and ANCOVA: a GLM approach. John Wiley & Sons, 2011.\n", 75 | " | \n", 76 | " | Methods defined here:\n", 77 | " | \n", 78 | " | __init__(self, data, depvar, subject, within=None, between=None, aggregate_func=None)\n", 79 | " | Initialize self. See help(type(self)) for accurate signature.\n", 80 | " | \n", 81 | " | fit(self)\n", 82 | " | estimate the model and compute the Anova table\n", 83 | " | \n", 84 | " | Returns\n", 85 | " | -------\n", 86 | " | AnovaResults instance\n", 87 | " | \n", 88 | " | ----------------------------------------------------------------------\n", 89 | " | Data descriptors defined here:\n", 90 | " | \n", 91 | " | __dict__\n", 92 | " | dictionary for instance variables (if defined)\n", 93 | " | \n", 94 | " | __weakref__\n", 95 | " | list of weak references to the object (if defined)\n", 96 | "\n" 97 | ] 98 | } 99 | ], 100 | "source": [ 101 | "import pandas as pd\n", 102 | "from statsmodels.stats.anova import AnovaRM\n", 103 | "\n", 104 | "help(AnovaRM)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 7, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "name": "stdout", 114 | "output_type": "stream", 115 | "text": [ 116 | " Anova\n", 117 | "===================================\n", 118 | " Num DF Den DF F Value Pr > F\n", 119 | "-----------------------------------\n", 120 | "cond 1.0000 59.0000 499.1549 0.0000\n", 121 | "===================================\n", 122 | "\n" 123 | ] 124 | } 125 | ], 126 | "source": [ 127 | "df = pd.read_csv('rmAOV1way.csv')\n", 128 | "\n", 129 | "aovrm = AnovaRM(df, 'rt', 'Sub_id', within=['cond'])\n", 130 | "res = aovrm.fit()\n", 131 | "\n", 132 | "print(res)" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 9, 138 | "metadata": {}, 139 | "outputs": [ 140 | { 141 | "name": "stdout", 142 | "output_type": "stream", 143 | "text": [ 144 | " Anova\n", 145 | "========================================\n", 146 | " Num DF Den DF F Value Pr > F\n", 147 | "----------------------------------------\n", 148 | "iv1 1.0000 59.0000 2207.0162 0.0000\n", 149 | "iv2 2.0000 118.0000 275.4144 0.0000\n", 150 | "iv1:iv2 2.0000 118.0000 1.8651 0.1594\n", 151 | "========================================\n", 152 | "\n" 153 | ] 154 | } 155 | ], 156 | "source": [ 157 | "df2way = pd.read_csv('rmAOV2way.csv')\n", 158 | "aovrm2way = AnovaRM(df2way, 'rt', 'Sub_id', within=['iv1', 'iv2'])\n", 159 | "res2way = aovrm2way.fit()\n", 160 | "\n", 161 | "print(res2way)" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": null, 167 | "metadata": {}, 168 | "outputs": [], 169 | "source": [] 170 | } 171 | ], 172 | "metadata": { 173 | "kernelspec": { 174 | "display_name": "Python 3", 175 | "language": "python", 176 | "name": "python3" 177 | }, 178 | "language_info": { 179 | "codemirror_mode": { 180 | "name": "ipython", 181 | "version": 3 182 | }, 183 | "file_extension": ".py", 184 | "mimetype": "text/x-python", 185 | "name": "python", 186 | "nbconvert_exporter": "python", 187 | "pygments_lexer": "ipython3", 188 | "version": "3.6.6" 189 | } 190 | }, 191 | "nbformat": 4, 192 | "nbformat_minor": 2 193 | } 194 | -------------------------------------------------------------------------------- /Python_ANOVA/Repeated measures ANOVA using R and afex.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## How to Carry out Repeated Measures ANOVA using R and afex\n", 8 | "\n", 9 | "Here you will learn how to carry out an one-way and a two-way ANOVA for repeated measures using the R-package afex. \n", 10 | "This notebook is, furthermore, for the YouTube video on how to carry out a [within-subjects ANOVA in Python and R](https://youtu.be/_X3g-dvlMF0). Make sure to watch that Python statsmodels tutorial to learn more about R vs Python when it comes to repeated measures ANOVA." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "metadata": {}, 17 | "outputs": [ 18 | { 19 | "name": "stderr", 20 | "output_type": "stream", 21 | "text": [ 22 | "Loading required package: lme4\n", 23 | "Loading required package: Matrix\n", 24 | "************\n", 25 | "Welcome to afex. For support visit: http://afex.singmann.science/\n", 26 | "- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()\n", 27 | "- Methods for calculating p-values with mixed(): 'KR', 'S', 'LRT', and 'PB'\n", 28 | "- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests\n", 29 | "- NEWS: library('emmeans') now needs to be called explicitly!\n", 30 | "- Get and set global package options with: afex_options()\n", 31 | "- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()\n", 32 | "- For example analyses see: browseVignettes(\"afex\")\n", 33 | "************\n", 34 | "\n", 35 | "Attaching package: 'afex'\n", 36 | "\n", 37 | "The following object is masked from 'package:lme4':\n", 38 | "\n", 39 | " lmer\n", 40 | "\n" 41 | ] 42 | } 43 | ], 44 | "source": [ 45 | "library(afex)" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 2, 51 | "metadata": {}, 52 | "outputs": [], 53 | "source": [ 54 | "help(aov_ez)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 3, 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "df <- read.csv('rmAOV1way.csv')" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 4, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "data": { 73 | "text/plain": [ 74 | "Anova Table (Type 3 tests)\n", 75 | "\n", 76 | "Response: rt\n", 77 | " Effect df MSE F ges p.value\n", 78 | "1 cond 1, 59 7753.97 499.15 *** .77 <.0001\n", 79 | "---\n", 80 | "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1" 81 | ] 82 | }, 83 | "metadata": {}, 84 | "output_type": "display_data" 85 | } 86 | ], 87 | "source": [ 88 | "aov <- aov_ez('Sub_id', 'rt', df, within = 'cond')\n", 89 | "aov" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 5, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "data": { 99 | "text/plain": [ 100 | "Anova Table (Type 3 tests)\n", 101 | "\n", 102 | "Response: rt\n", 103 | " Effect df MSE F ges p.value\n", 104 | "1 iv1 1, 59 10958.55 2207.02 *** .87 <.0001\n", 105 | "2 iv2 1.98, 116.79 8871.20 275.41 *** .58 <.0001\n", 106 | "3 iv1:iv2 1.97, 116.37 10668.36 1.87 .01 .16\n", 107 | "---\n", 108 | "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1\n", 109 | "\n", 110 | "Sphericity correction method: GG " 111 | ] 112 | }, 113 | "metadata": {}, 114 | "output_type": "display_data" 115 | } 116 | ], 117 | "source": [ 118 | "df2way <- read.csv('rmAOV2way.csv')\n", 119 | "\n", 120 | "aov2way <- aov_ez('Sub_id', 'rt', df2way, within = c('iv1', 'iv2'))\n", 121 | "aov2way" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [] 130 | } 131 | ], 132 | "metadata": { 133 | "kernelspec": { 134 | "display_name": "R", 135 | "language": "R", 136 | "name": "ir" 137 | }, 138 | "language_info": { 139 | "codemirror_mode": "r", 140 | "file_extension": ".r", 141 | "mimetype": "text/x-r-source", 142 | "name": "R", 143 | "pygments_lexer": "r", 144 | "version": "3.6.0" 145 | } 146 | }, 147 | "nbformat": 4, 148 | "nbformat_minor": 2 149 | } 150 | -------------------------------------------------------------------------------- /Python_ANOVA/Two_Way_ANOVA_in_Python_using_Pingouin_Stats.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
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lensuppdose
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" 68 | ], 69 | "text/plain": [ 70 | " len supp dose\n", 71 | "1 4.2 VC 0.5\n", 72 | "2 11.5 VC 0.5\n", 73 | "3 7.3 VC 0.5\n", 74 | "4 5.8 VC 0.5\n", 75 | "5 6.4 VC 0.5" 76 | ] 77 | }, 78 | "execution_count": 1, 79 | "metadata": {}, 80 | "output_type": "execute_result" 81 | } 82 | ], 83 | "source": [ 84 | "import pandas as pd\n", 85 | "import pingouin as pg\n", 86 | "\n", 87 | "data = 'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/ToothGrowth.csv'\n", 88 | "\n", 89 | "df = pd.read_csv(data, index_col=0)\n", 90 | "df.head()" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 2, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | " Source SS DF MS F p-unc np2\n", 103 | "0 supp 205.350 1 205.350 15.571979 2.311828e-04 0.223825\n", 104 | "1 dose 2426.434 2 1213.217 91.999952 4.046303e-18 0.773109\n", 105 | "2 supp * dose 108.319 2 54.160 4.107004 2.186003e-02 0.132028\n", 106 | "3 residual 712.106 54 13.187 NaN NaN NaN\n" 107 | ] 108 | } 109 | ], 110 | "source": [ 111 | "aov = pg.anova(dv='len', between=['supp', 'dose'], data=df,\n", 112 | " detailed=True)\n", 113 | "\n", 114 | "print(aov)" 115 | ] 116 | } 117 | ], 118 | "metadata": { 119 | "kernelspec": { 120 | "display_name": "Python 3", 121 | "language": "python", 122 | "name": "python3" 123 | }, 124 | "language_info": { 125 | "codemirror_mode": { 126 | "name": "ipython", 127 | "version": 3 128 | }, 129 | "file_extension": ".py", 130 | "mimetype": "text/x-python", 131 | "name": "python", 132 | "nbconvert_exporter": "python", 133 | "pygments_lexer": "ipython3", 134 | "version": "3.7.1" 135 | } 136 | }, 137 | "nbformat": 4, 138 | "nbformat_minor": 2 139 | } 140 | -------------------------------------------------------------------------------- /Python_ANOVA/pingouin-code-ANOVA-in-Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 4, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
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" 62 | ], 63 | "text/plain": [ 64 | " weight group\n", 65 | "1 4.17 ctrl\n", 66 | "2 5.58 ctrl\n", 67 | "3 5.18 ctrl\n", 68 | "4 6.11 ctrl\n", 69 | "5 4.50 ctrl" 70 | ] 71 | }, 72 | "execution_count": 4, 73 | "metadata": {}, 74 | "output_type": "execute_result" 75 | } 76 | ], 77 | "source": [ 78 | "import pandas as pd\n", 79 | "import pingouin as pg\n", 80 | "\n", 81 | "data = \"https://vincentarelbundock.github.io/Rdatasets/csv/datasets/PlantGrowth.csv\"\n", 82 | "\n", 83 | "df = pd.read_csv(data, index_col=0)\n", 84 | "df.head()" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 5, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "name": "stdout", 94 | "output_type": "stream", 95 | "text": [ 96 | " Source SS DF MS F p-unc np2\n", 97 | "0 group 3.766 2 1.883 4.846 0.01591 0.264\n", 98 | "1 Within 10.492 27 0.389 - - -\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "aov = pg.anova(data=df, dv='weight', between='group', detailed=True)\n", 104 | "print(aov)" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 7, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "name": "stdout", 114 | "output_type": "stream", 115 | "text": [ 116 | " A B mean(A) mean(B) diff SE tail T p-tukey \\\n", 117 | "0 ctrl trt1 5.032 4.661 0.371 0.279 two-sided 1.330 0.380686 \n", 118 | "1 ctrl trt2 5.032 5.526 -0.494 0.279 two-sided -1.771 0.182907 \n", 119 | "2 trt1 trt2 4.661 5.526 -0.865 0.279 two-sided -3.101 0.006347 \n", 120 | "\n", 121 | " efsize eftype \n", 122 | "0 0.570 hedges \n", 123 | "1 -0.759 hedges \n", 124 | "2 -1.328 hedges \n" 125 | ] 126 | } 127 | ], 128 | "source": [ 129 | "pt = pg.pairwise_tukey(dv='weight', between='group', data=df)\n", 130 | "print(pt)" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 9, 136 | "metadata": {}, 137 | "outputs": [ 138 | { 139 | "name": "stdout", 140 | "output_type": "stream", 141 | "text": [ 142 | " A B mean(A) mean(B) diff SE tail T p-tukey \\\n", 143 | "0 ctrl trt1 5.032 4.661 0.371 0.279 two-sided 1.330 0.380686 \n", 144 | "1 ctrl trt2 5.032 5.526 -0.494 0.279 two-sided -1.771 0.182907 \n", 145 | "2 trt1 trt2 4.661 5.526 -0.865 0.279 two-sided -3.101 0.006347 \n", 146 | "\n", 147 | " efsize eftype \n", 148 | "0 0.595 cohen \n", 149 | "1 -0.792 cohen \n", 150 | "2 -1.387 cohen \n" 151 | ] 152 | } 153 | ], 154 | "source": [ 155 | "pt = pg.pairwise_tukey(dv='weight', between='group', effsize='cohen', data=df)\n", 156 | "print(pt)" 157 | ] 158 | } 159 | ], 160 | "metadata": { 161 | "kernelspec": { 162 | "display_name": "Python 3", 163 | "language": "python", 164 | "name": "python3" 165 | }, 166 | "language_info": { 167 | "codemirror_mode": { 168 | "name": "ipython", 169 | "version": 3 170 | }, 171 | "file_extension": ".py", 172 | "mimetype": "text/x-python", 173 | "name": "python", 174 | "nbconvert_exporter": "python", 175 | "pygments_lexer": "ipython3", 176 | "version": "3.7.1" 177 | } 178 | }, 179 | "nbformat": 4, 180 | "nbformat_minor": 2 181 | } 182 | -------------------------------------------------------------------------------- /Python_ANOVA/rmAOV1way.csv: -------------------------------------------------------------------------------- 1 | Sub_id,rt,cond 2 | 1,971.8265632087922,noise 3 | 2,857.6081665372951,noise 4 | 3,1035.3547583022873,noise 5 | 4,867.6208707594525,noise 6 | 5,928.3818313279531,noise 7 | 6,925.6146127265147,noise 8 | 7,1021.1715184399466,noise 9 | 8,1021.1445203023742,noise 10 | 9,1153.8435030155763,noise 11 | 10,1256.0521124105512,noise 12 | 11,1123.3607149851873,noise 13 | 12,1042.1812156235676,noise 14 | 13,763.5502095684848,noise 15 | 14,1035.0208763443406,noise 16 | 15,1028.4994225524808,noise 17 | 16,989.6639760469989,noise 18 | 17,1092.6373053971492,noise 19 | 18,1043.7295230300795,noise 20 | 19,1038.9305716948277,noise 21 | 20,908.6886912581376,noise 22 | 21,932.5560583939308,noise 23 | 22,876.1058831160847,noise 24 | 23,965.2362523707013,noise 25 | 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56,562.5756625441718,quiet 118 | 57,672.5129375773448,quiet 119 | 58,583.3081652538318,quiet 120 | 59,762.6003823679389,quiet 121 | 60,754.486405555377,quiet 122 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Jupyter notebooks 2 | In this folder you will find Jupyter Notebooks containing code examples for many of my blogposts ([https://www.marsja.se](https://www.marsja.se)). There are also notebooks containing the code for some of my Videos on my [YouTube Channel](https://www.youtube.com/channel/UCFHeY1aOt-Y4FLZeG_IpJCA). The main theme of the blog posts, right now, is data visualization, manipulation, and analysis using Python. For instance, you will find Pandas tutorials, Seaborn tutorials, and Statsmodels tutorials (e.g., Python ANOVA). 3 | 4 | ### Pandas DataFrame Tutorial: Jupyter Notebooks 5 | 6 | Here are different Notebooks containing the code examples for some of the Pandas Tutorials: 7 | 8 | - [Reversing Pandas Dataframe](https://github.com/marsja/jupyter/blob/master/reverse_pandas_dataframe.ipynb) 9 | 10 | - [Pandas Dataframe Tutorial: Working with Dataframes](https://github.com/marsja/jupyter/blob/master/Pandas_Dataframe_Tutorial_Example_Code.ipynb) 11 | 12 | - [Python Pandas JSON Tutorial](https://github.com/marsja/jupyter/blob/master/json_in_python_and_pandas.ipynb) 13 | 14 | - [Data Cleansing With Pandas and Pyjanitor](https://github.com/marsja/jupyter/blob/master/pyjanitor_data_cleaning_adding_columns_removing_columns_pandas.ipynb) - In this notebook code examples on how to delete missing values, remove empty columns, clean column names (i.e., rename columns) and so on can be found. 15 | 16 | - [Read SPSS files in Python](https://github.com/marsja/jupyter/blob/master/how_to_read_SPSS_sav_files_in_Python.ipynb) - Learn how to load .sav files into Pandas dataframes. 17 | 18 | - [Converting a Dictionary to a Pandas Dataframe](https://github.com/marsja/jupyter/blob/master/how_to_convert_a_dictionary_to_pandas_dataframe.ipynb) 19 | - [How to Print the Columns Names in a Pandas dataframe](https://github.com/marsja/jupyter/blob/master/how-to-get-column-names-Pandas-python.ipynb) 20 | 21 | ### Working with DataFrame 22 | Here you will find Jupyter notebooks, both R and Python right now, that focuses on how to work with data in specific ways. 23 | 24 | - [How to Reverse Code in R](https://github.com/marsja/jupyter/blob/master/R_Notebooks/reverse_coding_in_R.ipynb) - An R note book for learning how to reverse scores from a questionnaire using R and the package psych. 25 | -[Create Dummy Variables in Python with Pandas](https://github.com/marsja/jupyter/blob/master/pandas_get_dummies_how_to_create_dummy_variables.ipynb) 26 | 27 | ### Data Visualization in Python: Code Examples 28 | Note books for different Data Visualization Tutorials: 29 | 30 | - [Kernel Estimation, Cumulative Distribution Function, Delta Plot, & Conditional Accuracy Function](https://github.com/marsja/jupyter/blob/master/kde-cdf-delta-caf-plots.ipynb): Different data visualization techniques for response time data 31 | 32 | - [Python Data Visualization Tutorial: How to Create a Scatter Plot](https://github.com/marsja/jupyter/blob/master/Pandas%20Scatter%20Plot%20Tutorial.ipynb) 33 | - [How to Make Scatter Plots using Python](https://github.com/marsja/jupyter/blob/master/Seaborn_Scatterplot_Tutorial_Example_Code.ipynb) - Here we work with the following Seaborn methods: *scatterplot*, *regplot*, *lmpot*, and *pairplot*. We learn how to make scatter plots with regression lines, change the color and markers of Seaborn plots, rotate the ticks labels, and much more. 34 | - [Raincloud Plots in Python](https://github.com/marsja/jupyter/blob/master/Raincloud_Plots_in_Python.ipynb) Here's the example code from the Python data visualization tutorial on how to make raincloud plots in Python (YouTube Video) 35 | - [How to make a Histogram in Python with Pandas](https://github.com/marsja/jupyter/blob/master/how-to-make-histogram-in-pandas-python.ipynb) 36 | 37 | 38 | ### Data Visualization in R: Code examples 39 | 40 | - [Scatterplots in R using ggplot2](https://github.com/marsja/jupyter/blob/master/R_Notebooks/scatter_plot_in_R_tutorial.ipynb) - Notebook containing code examples on how to create scatter plots using ggplot2. 41 | 42 | ## Rpy2 Tutorial: Code Examples 43 | For some data analysis techniques we may want to use R. This because it may not yet be implemented in Python. 44 | Here are some Jupyter Notebooks for one Python package called Rpy2: 45 | 46 | - [Rpy2 & R-plots in Jupyter Notebooks](https://github.com/marsja/jupyter/blob/master/Rpy2%20and%20R%20plots%20in%20a%20Jupyter%20Notebook!.ipynb) (See the [YouTube Video](https://www.youtube.com/watch?v=RK-n78ZOXUg)) 47 | 48 | - [Rpy2 Tutorial: Example](https://github.com/marsja/jupyter/blob/master/rpy2%20tutorial%20example%20code.ipynb) 49 | 50 | ## Python Data Analysis Tutorials 51 | In this section, we find the code examples, in the form of Jupyter Notebooks, for the Python data analysis tutorials; 52 | 53 | ### Descriptive Statistics in Python 54 | Here, there are some Jupyter Notebooks for carrying out summary statistics using 55 | Python, Pandas, Scipy, and other useful Python packages 56 | 57 | - [Descriptive Statistics in Python](https://github.com/marsja/jupyter/blob/master/descriptive_statistics_example_code.ipynb) 58 | - [Correlation Matrix in Python](https://github.com/marsja/jupyter/blob/master/correlation_matrix_in_python.ipynb) 59 | 60 | ### T-test 61 | - [Two-Sample T-test in Python](https://github.com/marsja/jupyter/blob/master/two_sample_t-test_Python.ipynb) 62 | 63 | 64 | ### ANOVA in Python and R 65 | Here we have Python ANOVA Notebooks. There is also one Notebook for carrying out ANOVA using R. 66 | 67 | #### Python ANOVA (Between Groups) 68 | 69 | - [One-Way ANOVA using Pingouin Stats](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/pingouin-code-ANOVA-in-Python.ipynb) 70 | 71 | - [One-Way ANOVA using Scipy and Statsmodels](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/One_Way_Python_ANOVA.ipynb) 72 | 73 | - [Two-Way ANOVA using Python and SciPy](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/Two_Way_ANOVA_in_Python_Tutorial.ipynb) 74 | 75 | - [Two-Way ANOVA using Pingouin Stats](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/Two_Way_ANOVA_in_Python_Tutorial.ipynb) 76 | 77 | - [Two-Way ANOVA using Statsmodels](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/Python_ANOVA_Factorial_Using_Statsmodels.ipynb) 78 | 79 | #### Python ANOVA (Within Group) 80 | - [Repeated Measures ANOVA using Statsmodels](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/Python%20repeated%20measures%20ANOVA.ipynb) 81 | - [Repeated Measures ANOVA in R: afex code example](https://github.com/marsja/jupyter/blob/master/Python_ANOVA/Repeated%20measures%20ANOVA%20using%20R%20and%20afex.ipynb) 82 | 83 | ### MANOVA in Python 84 | - [One-Way MANOVA using Statsmodels](https://github.com/marsja/jupyter/blob/master/MANOVA_Test_in_Python_Statsmodels_Example.ipynb) - This notebook contains the multivariate data analysis Multivariate Analysis of Variance (MANOVA) 85 | 86 | ### Other 87 | Tutorials, guides, and how tos (both Python and R) that doesn't fit into the above categories 88 | 89 | - [Get Max Value in Dictionary in Python](https://github.com/marsja/jupyter/blob/master/getting_the_highest_value_from_dictionary.ipynb) 90 | 91 | - [Convert HTML to Jupyter notebook](https://github.com/marsja/jupyter/blob/master/convert_html_jupyter_notebook_tutorial.ipynb) - Code to convert HTML (e.g., code within code tags) to .ipynb files that can be run 92 | 93 | - [Convert Float Array to Integer Array Python](https://github.com/marsja/jupyter/blob/master/convert_numpy_float_array_to_integer_array_Python.ipynb) - Code to convert a NumPy float Array to an Integer Array 94 | -------------------------------------------------------------------------------- /R_Notebooks/reverse_coding_in_R.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Reverse Scoring Items in a Questionnaire\n", 8 | "In this notebook we will learn another methods for reverse coding of questionnaire items using the R statistical programming environment. Here you will learn reverse coding using a function from the psych package in R. This notebook is for the blog post [https://www.marsja.se/reverse-scoring-using-r/](https://www.marsja.se/reverse-scoring-using-r/) in which you will learn more about this." 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "### Data to Reverse\n", 16 | "As example data, with some columns to reverse, we create a dataframe:" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/html": [ 27 | "\n", 28 | "\n", 29 | "\n", 30 | "\t\n", 31 | "\t\n", 32 | "\t\n", 33 | "\t\n", 34 | "\t\n", 35 | "\t\n", 36 | "\n", 37 | "
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\n" 38 | ], 39 | "text/latex": [ 40 | "\\begin{tabular}{r|llllll}\n", 41 | " Q1 & Q2 & Q3 & Q4 & Q5 & Q6\\\\\n", 42 | "\\hline\n", 43 | "\t 3 & 1 & 4 & 2 & 4 & 5\\\\\n", 44 | "\t 2 & 1 & 3 & 3 & 1 & 1\\\\\n", 45 | "\t 3 & 4 & 1 & 3 & 5 & 4\\\\\n", 46 | "\t 1 & 3 & 4 & 2 & 1 & 1\\\\\n", 47 | "\t 5 & 1 & 1 & 2 & 4 & 1\\\\\n", 48 | "\t 2 & 5 & 5 & 4 & 1 & 1\\\\\n", 49 | "\\end{tabular}\n" 50 | ], 51 | "text/markdown": [ 52 | "\n", 53 | "| Q1 | Q2 | Q3 | Q4 | Q5 | Q6 |\n", 54 | "|---|---|---|---|---|---|\n", 55 | "| 3 | 1 | 4 | 2 | 4 | 5 |\n", 56 | "| 2 | 1 | 3 | 3 | 1 | 1 |\n", 57 | "| 3 | 4 | 1 | 3 | 5 | 4 |\n", 58 | "| 1 | 3 | 4 | 2 | 1 | 1 |\n", 59 | "| 5 | 1 | 1 | 2 | 4 | 1 |\n", 60 | "| 2 | 5 | 5 | 4 | 1 | 1 |\n", 61 | "\n" 62 | ], 63 | "text/plain": [ 64 | " Q1 Q2 Q3 Q4 Q5 Q6\n", 65 | "1 3 1 4 2 4 5 \n", 66 | "2 2 1 3 3 1 1 \n", 67 | "3 3 4 1 3 5 4 \n", 68 | "4 1 3 4 2 1 1 \n", 69 | "5 5 1 1 2 4 1 \n", 70 | "6 2 5 5 4 1 1 " 71 | ] 72 | }, 73 | "metadata": {}, 74 | "output_type": "display_data" 75 | } 76 | ], 77 | "source": [ 78 | "df <- as.data.frame(replicate(6, replicate(100, sample(1:5,1))))\n", 79 | "\n", 80 | "colnames(df) <- c('Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6')\n", 81 | "\n", 82 | "head(df)" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "### Working with the reverse.code function\n", 90 | "First, we are going to install the r-package called \"psych\" and then we are going to sue the reverse.code function to switch the coding of some items." 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 2, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stderr", 100 | "output_type": "stream", 101 | "text": [ 102 | "Installing package into 'C:/Users/erima96/Documents/R/win-library/3.6'\n", 103 | "(as 'lib' is unspecified)\n" 104 | ] 105 | }, 106 | { 107 | "name": "stdout", 108 | "output_type": "stream", 109 | "text": [ 110 | "package 'psych' successfully unpacked and MD5 sums checked\n", 111 | "\n", 112 | "The downloaded binary packages are in\n", 113 | "\tC:\\Users\\erima96\\AppData\\Local\\Temp\\Rtmpea2YXQ\\downloaded_packages\n" 114 | ] 115 | } 116 | ], 117 | "source": [ 118 | "install.packages(\"psych\")" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "#### Reverse-Code Variables in R using reverse.code\n", 126 | "Now, we're ready to reverse the items using R:" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 3, 132 | "metadata": {}, 133 | "outputs": [ 134 | { 135 | "name": "stderr", 136 | "output_type": "stream", 137 | "text": [ 138 | "Loading required package: psych\n", 139 | "Warning message:\n", 140 | "\"package 'psych' was built under R version 3.6.1\"" 141 | ] 142 | }, 143 | { 144 | "data": { 145 | "text/html": [ 146 | "\n", 147 | "\n", 148 | "\n", 149 | "\t\n", 150 | "\t\n", 151 | "\t\n", 152 | "\n", 153 | "
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\n" 194 | ], 195 | "text/latex": [ 196 | "\\begin{tabular}{llllll}\n", 197 | " Q1 & Q2- & Q3- & Q4- & Q5 & Q6\\\\\n", 198 | "\\hline\n", 199 | "\t 3 & 5 & 2 & 4 & 4 & 5\\\\\n", 200 | "\t 2 & 5 & 3 & 3 & 1 & 1\\\\\n", 201 | "\t 3 & 2 & 5 & 3 & 5 & 4\\\\\n", 202 | "\\end{tabular}\n" 203 | ], 204 | "text/markdown": [ 205 | "\n", 206 | "| Q1 | Q2- | Q3- | Q4- | Q5 | Q6 |\n", 207 | "|---|---|---|---|---|---|\n", 208 | "| 3 | 5 | 2 | 4 | 4 | 5 |\n", 209 | "| 2 | 5 | 3 | 3 | 1 | 1 |\n", 210 | "| 3 | 2 | 5 | 3 | 5 | 4 |\n", 211 | "\n" 212 | ], 213 | "text/plain": [ 214 | " Q1 Q2- Q3- Q4- Q5 Q6\n", 215 | "[1,] 3 5 2 4 4 5 \n", 216 | "[2,] 2 5 3 3 1 1 \n", 217 | "[3,] 3 2 5 3 5 4 " 218 | ] 219 | }, 220 | "metadata": {}, 221 | "output_type": "display_data" 222 | } 223 | ], 224 | "source": [ 225 | "require(psych)\n", 226 | "\n", 227 | "\n", 228 | "#Reversing scores in columns 'Q2', 'Q3', and 'Q4'\n", 229 | "keys <- c(1, -1, -1, -1, 1, 1)\n", 230 | "\n", 231 | "new <- reverse.code(keys, df)\n", 232 | "df[1:3,]\n", 233 | "new[1:3,]" 234 | ] 235 | } 236 | ], 237 | "metadata": { 238 | "kernelspec": { 239 | "display_name": "R", 240 | "language": "R", 241 | "name": "ir" 242 | }, 243 | "language_info": { 244 | "codemirror_mode": "r", 245 | "file_extension": ".r", 246 | "mimetype": "text/x-r-source", 247 | "name": "R", 248 | "pygments_lexer": "r", 249 | "version": "3.6.0" 250 | } 251 | }, 252 | "nbformat": 4, 253 | "nbformat_minor": 2 254 | } 255 | -------------------------------------------------------------------------------- /SimData/DF_NA_Janitor.csv: -------------------------------------------------------------------------------- 1 | ,Subject ID,First Name,Day,Age,Response Time,Gender,Miss\Col 2 | 0,1,John,Sixth,23,0.5627187141917855,0, 3 | 1,2,Billie,Sixth,22,,0, 4 | 2,3,Robert,Sixth,20,,0, 5 | 3,4,Don,Sixth,27,0.5229607643652396,0, 6 | 4,5,Joseph,Sixth,21,,0, 7 | 5,6,James,Sixth,25,0.4571030819121562,0, 8 | 6,7,Delbert,Sixth,26,,0, 9 | 7,8,Gary,Sixth,24,,0, 10 | 8,9,Scott,Sixth,27,,0, 11 | 9,10,Steve,Sixth,27,,0, 12 | 10,11,Frank,Sixth,18,,0, 13 | 11,12,Eric,Sixth,23,0.512687736538734,0, 14 | 12,13,Wendell,Sixth,27,,0, 15 | 13,14,James,Sixth,21,0.4517822116966478,0, 16 | 14,15,Paul,Sixth,20,,0, 17 | 15,16,Juan,Sixth,26,0.511309398445967,0, 18 | 16,17,Walter,Sixth,30,0.4876374026809741,0, 19 | 17,18,Richard,Sixth,23,0.5248170458806685,0, 20 | 18,19,Felix,Sixth,21,0.6719892697762015,0, 21 | 19,20,Andre,Sixth,21,0.4449211650987591,0, 22 | 20,21,Jeffrey,Sixth,26,0.4035001434778803,0, 23 | 21,22,Dave,Sixth,21,0.5538101525320848,0, 24 | 22,23,James,Sixth,29,0.3699416770202328,0, 25 | 23,24,Lemuel,Sixth,27,0.29449460162573504,0, 26 | 24,25,Tommy,Sixth,23,0.7125960371855942,0, 27 | 25,26,Carlos,Sixth,22,0.560689934295333,0, 28 | 26,27,Willie,Sixth,19,0.4426790378672592,0, 29 | 27,28,Joseph,Sixth,19,0.467343626114986,0, 30 | 28,29,Everette,Sixth,25,0.6954712644704699,0, 31 | 29,30,Marvin,Sixth,28,0.5338449267566893,0, 32 | 30,31,Javier,Sixth,30,0.5053801595683928,0, 33 | 31,32,Henry,Sixth,27,0.4386438165903696,0, 34 | 32,33,Danial,Sixth,19,0.443953740251032,0, 35 | 33,34,Nathan,Sixth,28,0.6104137051112386,0, 36 | 34,35,Ted,Sixth,29,0.4387538730184034,0, 37 | 35,36,Terry,Sixth,29,0.5828116958700524,0, 38 | 36,37,Steve,Sixth,28,0.4883025967736782,0, 39 | 37,38,Thomas,Sixth,24,0.5708048197461699,0, 40 | 38,39,Roger,Sixth,24,0.5903570754206436,0, 41 | 39,40,Mohammed,Sixth,26,0.5710369057808238,0, 42 | 40,41,Willie,Sixth,19,0.3545339060940288,0, 43 | 41,42,Kirk,Sixth,20,0.5931082193103651,0, 44 | 42,43,Michael,Sixth,20,0.4278974215619132,0, 45 | 43,44,Michael,Sixth,24,0.7627312850675907,0, 46 | 44,45,Eric,Sixth,20,0.3609865803366528,0, 47 | 45,46,Clarence,Sixth,19,0.2723403918403283,0, 48 | 46,47,Carlton,Sixth,23,0.4830924544938338,0, 49 | 47,48,Luis,Sixth,18,0.5978956173958363,0, 50 | 48,49,John,Sixth,30,0.4619513788658993,0, 51 | 49,50,Charles,Sixth,23,0.4479710238937036,0, 52 | 50,51,Larry,Sixth,30,0.6005101847929761,0, 53 | 51,52,Kennith,Sixth,22,0.6407673041409321,0, 54 | 52,53,John,Sixth,24,0.3166798757885672,0, 55 | 53,54,David,Sixth,20,0.5460591953743089,0, 56 | 54,55,Taylor,Sixth,20,0.4202468709201124,0, 57 | 55,56,Michael,Sixth,23,0.503040391617558,0, 58 | 56,57,Wesley,Sixth,21,0.5252979188424935,0, 59 | 57,58,Jack,Sixth,20,0.4456059188027668,0, 60 | 58,59,Drew,Sixth,22,0.4963736106787016,0, 61 | 59,60,Gary,Sixth,20,0.4475585006721042,0, 62 | 60,61,Robert,Sixth,26,0.433021422000792,0, 63 | 61,62,Charles,Sixth,29,0.4821278711793452,0, 64 | 62,63,Lawrence,Sixth,24,0.4054221747322013,0, 65 | 63,64,Billy,Sixth,25,0.5459887509348277,0, 66 | 64,65,Travis,Sixth,19,0.4482537127438954,0, 67 | 65,66,Kelly,Sixth,21,0.4947167997604914,0, 68 | 66,67,Johnnie,Sixth,23,0.4946061853363077,0, 69 | 67,68,Kenneth,Sixth,25,0.5652839716526309,0, 70 | 68,69,John,Sixth,19,0.34318786073239826,0, 71 | 69,70,Tony,Sixth,22,0.5142301178099664,0, 72 | 70,71,Salvatore,Sixth,30,0.5009923942795725,0, 73 | 71,72,Jack,Sixth,23,0.45398884881089896,0, 74 | 72,73,Jose,Sixth,28,0.4821673612287138,0, 75 | 73,74,John,Sixth,24,0.4722656940541789,0, 76 | 74,75,Richard,Sixth,18,0.6197608040669679,0, 77 | 75,76,Alan,Sixth,24,0.5335412407017036,0, 78 | 76,77,Edward,Sixth,30,0.5938522409655508,0, 79 | 77,78,Kenny,Sixth,30,0.41093795718540826,0, 80 | 78,79,Ian,Sixth,24,0.4959098187308255,0, 81 | 79,80,Brian,Sixth,29,0.5308543203711171,0, 82 | 80,81,Chad,Sixth,24,0.3173498201434641,0, 83 | 81,82,Lowell,Sixth,19,0.4248495932603885,0, 84 | 82,83,Jonathon,Sixth,24,0.4720788961850801,0, 85 | 83,84,Chester,Sixth,25,0.5393488384028237,0, 86 | 84,85,Harold,Sixth,28,0.4663681827526402,0, 87 | 85,86,Michael,Sixth,28,0.564364490050406,0, 88 | 86,87,James,Sixth,23,0.6633255597766218,0, 89 | 87,88,Charles,Sixth,18,0.6406938401660549,0, 90 | 88,89,Arthur,Sixth,21,0.6547226491561557,0, 91 | 89,90,Robert,Sixth,21,0.4650420803037942,0, 92 | 90,91,William,Sixth,30,0.5665519641804087,0, 93 | 91,92,Raymond,Sixth,25,0.6234046756685127,0, 94 | 92,93,Michael,Sixth,18,0.4054389794121322,0, 95 | 93,94,James,Sixth,27,0.4832817739075767,0, 96 | 94,95,James,Sixth,22,0.4623935581996443,0, 97 | 95,96,Jayson,Sixth,30,0.6183163685997498,0, 98 | 96,97,Don,Sixth,24,0.5733711927951894,0, 99 | 97,98,Joseph,Sixth,29,0.4873175435671538,0, 100 | 98,99,Paul,Sixth,22,0.688110681439846,0, 101 | 99,100,Roberto,Sixth,27,0.434629408779397,0, 102 | 100,101,Vanessa,Sixth,25,0.5111038562077044,1, 103 | 101,102,Alice,Sixth,23,0.4972015920269251,1, 104 | 102,103,Josephine,Sixth,27,0.6222947266717184,1, 105 | 103,104,Annie,Sixth,28,0.4729998858439837,1, 106 | 104,105,Veronica,Sixth,18,0.4992901882731911,1, 107 | 105,106,Elizabeth,Sixth,23,0.3895004576945725,1, 108 | 106,107,Marie,Sixth,29,0.4726102218311762,1, 109 | 107,108,Barbara,Sixth,20,0.465480091459964,1, 110 | 108,109,Donna,Sixth,29,0.4993503685979966,1, 111 | 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128,129,Wilma,Sixth,25,0.41292421225187,1, 131 | 129,130,Alyssa,Sixth,30,0.5691490519310829,1, 132 | 130,131,Samira,Sixth,24,0.5983022594122839,1, 133 | 131,132,Danielle,Sixth,26,0.6119405745912091,1, 134 | 132,133,Rachal,Sixth,30,0.4502686799797943,1, 135 | 133,134,Candice,Sixth,30,0.4958386248306951,1, 136 | 134,135,Yvonne,Sixth,30,0.5707502520010675,1, 137 | 135,136,Lorraine,Sixth,19,0.4435393854940667,1, 138 | 136,137,Minnie,Sixth,25,0.4677632036651698,1, 139 | 137,138,Katherine,Sixth,27,0.4042972425845818,1, 140 | 138,139,Teresa,Sixth,24,0.6421220921833651,1, 141 | 139,140,Suzanne,Sixth,21,0.5422837619955311,1, 142 | 140,141,Maria,Sixth,30,0.39329161255034334,1, 143 | 141,142,Laura,Sixth,21,0.5375873055372921,1, 144 | 142,143,Georgette,Sixth,20,0.392442361268742,1, 145 | 143,144,Marcia,Sixth,19,0.5829517088661433,1, 146 | 144,145,Susie,Sixth,21,0.4800001615471727,1, 147 | 145,146,Deanna,Sixth,18,0.4949746972751772,1, 148 | 146,147,Harriet,Sixth,22,0.4651620538037444,1, 149 | 147,148,Laverne,Sixth,23,0.21852240343463564,1, 150 | 148,149,Evelyn,Sixth,27,0.5256522259273075,1, 151 | 149,150,Jessica,Sixth,22,0.4626756113213673,1, 152 | 150,151,Mary,Sixth,28,0.5971399636524906,1, 153 | 151,152,Maurine,Sixth,23,0.3862647280799457,1, 154 | 152,153,Millicent,Sixth,21,0.41539557943222,1, 155 | 153,154,Twila,Sixth,22,0.4434321643778448,1, 156 | 154,155,Kim,Sixth,27,0.5783197667230191,1, 157 | 155,156,Elida,Sixth,27,0.4595253229609275,1, 158 | 156,157,Robin,Sixth,21,0.406894345549398,1, 159 | 157,158,Katrina,Sixth,23,0.574720890916179,1, 160 | 158,159,Christina,Sixth,23,0.4471695747522068,1, 161 | 159,160,Ashley,Sixth,21,0.3658777537307304,1, 162 | 160,161,Katherine,Sixth,19,0.5443646468409017,1, 163 | 161,162,Stella,Sixth,18,0.3811908133421368,1, 164 | 162,163,Lillian,Sixth,29,0.5827037820459395,1, 165 | 163,164,Megan,Sixth,26,0.6712950290615973,1, 166 | 164,165,Martha,Sixth,30,0.5999854536629398,1, 167 | 165,166,Tamra,Sixth,22,0.5426967026078454,1, 168 | 166,167,Barbara,Sixth,23,0.5266036117738924,1, 169 | 167,168,Helen,Sixth,23,0.3827441141401961,1, 170 | 168,169,Delores,Sixth,20,0.5125524640036196,1, 171 | 169,170,Cassandra,Sixth,19,0.5445426304713614,1, 172 | 170,171,Shawn,Sixth,20,0.5415660559256122,1, 173 | 171,172,Diane,Sixth,23,0.31139991061482986,1, 174 | 172,173,Lena,Sixth,26,0.4249519659560316,1, 175 | 173,174,Renee,Sixth,29,0.4841828832501326,1, 176 | 174,175,Linda,Sixth,20,0.4267961009247867,1, 177 | 175,176,Brooke,Sixth,21,0.5665981844310021,1, 178 | 176,177,Angela,Sixth,24,0.4877586266951791,1, 179 | 177,178,Bridget,Sixth,21,0.4569162017466531,1, 180 | 178,179,Betty,Sixth,24,0.534980963105169,1, 181 | 179,180,Lola,Sixth,30,0.5152307543228434,1, 182 | 180,181,Debra,Sixth,21,0.40830678550869137,1, 183 | 181,182,Debra,Sixth,29,0.6112997110674216,1, 184 | 182,183,Diana,Sixth,19,0.41919925141276265,1, 185 | 183,184,Mary,Sixth,28,0.5751100677634319,1, 186 | 184,185,Mable,Sixth,23,0.4904157402345066,1, 187 | 185,186,Jeanne,Sixth,30,0.3049416329938862,1, 188 | 186,187,Sharon,Sixth,26,0.4326362856392755,1, 189 | 187,188,Lucia,Sixth,18,0.3895300791317597,1, 190 | 188,189,Kimberly,Sixth,24,0.5165075984515924,1, 191 | 189,190,Crystal,Sixth,19,0.3799857945139376,1, 192 | 190,191,Esther,Sixth,25,0.3598757061536821,1, 193 | 191,192,Della,Sixth,28,0.5487373450086704,1, 194 | 192,193,Joy,Sixth,20,0.5711191682655571,1, 195 | 193,194,Benita,Sixth,24,0.5812982298969092,1, 196 | 194,195,Doris,Sixth,18,0.5366558449616664,1, 197 | 195,196,Francisca,Sixth,27,0.4946573817954197,1, 198 | 196,197,Nia,Sixth,20,0.4877552435335766,1, 199 | 197,198,Christina,Sixth,29,0.4994693732595509,1, 200 | 198,199,Marta,Sixth,26,0.4278674824865973,1, 201 | 199,200,Julia,Sixth,25,0.4036795449925428,1, 202 | -------------------------------------------------------------------------------- /SimData/FifthDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,Fifth,23,0.45373296299551025,0 3 | 2,Billie,Fifth,22,0.25735967887773664,0 4 | 3,Robert,Fifth,20,0.4433932346537235,0 5 | 4,Don,Fifth,27,0.42359205860097654,0 6 | 5,Joseph,Fifth,21,0.5713553855245412,0 7 | 6,James,Fifth,25,0.5577922012116372,0 8 | 7,Delbert,Fifth,26,0.3864622293050664,0 9 | 8,Gary,Fifth,24,0.5153159712551302,0 10 | 9,Scott,Fifth,27,0.2608930105995283,0 11 | 10,Steve,Fifth,27,0.29787530176726096,0 12 | 11,Frank,Fifth,18,0.391164156182838,0 13 | 12,Eric,Fifth,23,0.3443803944464628,0 14 | 13,Wendell,Fifth,27,0.4718281632921879,0 15 | 14,James,Fifth,21,0.42329786553819276,0 16 | 15,Paul,Fifth,20,0.37005230002598904,0 17 | 16,Juan,Fifth,26,0.5482615795824617,0 18 | 17,Walter,Fifth,30,0.4078488401454476,0 19 | 18,Richard,Fifth,23,0.33040885620460014,0 20 | 19,Felix,Fifth,21,0.2984548110759699,0 21 | 20,Andre,Fifth,21,0.3909972498950065,0 22 | 21,Jeffrey,Fifth,26,0.3631563436461584,0 23 | 22,Dave,Fifth,21,0.5704688745841278,0 24 | 23,James,Fifth,29,0.4407569614878759,0 25 | 24,Lemuel,Fifth,27,0.38596293335978743,0 26 | 25,Tommy,Fifth,23,0.45112653223105575,0 27 | 26,Carlos,Fifth,22,0.15410928303560273,0 28 | 27,Willie,Fifth,19,0.5882946272402179,0 29 | 28,Joseph,Fifth,19,0.4247489157849507,0 30 | 29,Everette,Fifth,25,0.45523146646140916,0 31 | 30,Marvin,Fifth,28,0.47087038435588485,0 32 | 31,Javier,Fifth,30,0.4146333217905207,0 33 | 32,Henry,Fifth,27,0.31609695570870033,0 34 | 33,Danial,Fifth,19,0.264539801115214,0 35 | 34,Nathan,Fifth,28,0.5666326248666169,0 36 | 35,Ted,Fifth,29,0.5287781654016532,0 37 | 36,Terry,Fifth,29,0.36008779146029807,0 38 | 37,Steve,Fifth,28,0.5850967105206835,0 39 | 38,Thomas,Fifth,24,0.5162706106184481,0 40 | 39,Roger,Fifth,24,0.4955931497351022,0 41 | 40,Mohammed,Fifth,26,0.41962238823216436,0 42 | 41,Willie,Fifth,19,0.40660584948701456,0 43 | 42,Kirk,Fifth,20,0.4633507452412541,0 44 | 43,Michael,Fifth,20,0.36464576791707587,0 45 | 44,Michael,Fifth,24,0.40822625873812274,0 46 | 45,Eric,Fifth,20,0.3179800518993563,0 47 | 46,Clarence,Fifth,19,0.41276014968064084,0 48 | 47,Carlton,Fifth,23,0.562001710432737,0 49 | 48,Luis,Fifth,18,0.37575671519751463,0 50 | 49,John,Fifth,30,0.6060155241568055,0 51 | 50,Charles,Fifth,23,0.4446042669400221,0 52 | 51,Larry,Fifth,30,0.476775463243786,0 53 | 52,Kennith,Fifth,22,0.5923523157152484,0 54 | 53,John,Fifth,24,0.3390585493824726,0 55 | 54,David,Fifth,20,0.4781668404046945,0 56 | 55,Taylor,Fifth,20,0.492333496151051,0 57 | 56,Michael,Fifth,23,0.34266879155444324,0 58 | 57,Wesley,Fifth,21,0.3515514977803989,0 59 | 58,Jack,Fifth,20,0.3892373050444082,0 60 | 59,Drew,Fifth,22,0.3877900960755566,0 61 | 60,Gary,Fifth,20,0.42373411732156624,0 62 | 61,Robert,Fifth,26,0.6122989930008309,0 63 | 62,Charles,Fifth,29,0.6327686900361291,0 64 | 63,Lawrence,Fifth,24,0.36769426520741866,0 65 | 64,Billy,Fifth,25,0.4897967438724099,0 66 | 65,Travis,Fifth,19,0.6208327670153768,0 67 | 66,Kelly,Fifth,21,0.3287750096989917,0 68 | 67,Johnnie,Fifth,23,0.41601403953084176,0 69 | 68,Kenneth,Fifth,25,0.48914909502465487,0 70 | 69,John,Fifth,19,0.4456015577066491,0 71 | 70,Tony,Fifth,22,0.5207180043199187,0 72 | 71,Salvatore,Fifth,30,0.22485131718846757,0 73 | 72,Jack,Fifth,23,0.3594394287686675,0 74 | 73,Jose,Fifth,28,0.5363833335252903,0 75 | 74,John,Fifth,24,0.3541826308817994,0 76 | 75,Richard,Fifth,18,0.411699628897766,0 77 | 76,Alan,Fifth,24,0.4660467706949534,0 78 | 77,Edward,Fifth,30,0.34060550470610557,0 79 | 78,Kenny,Fifth,30,0.5871353488381698,0 80 | 79,Ian,Fifth,24,0.4535917248314896,0 81 | 80,Brian,Fifth,29,0.33011807668462756,0 82 | 81,Chad,Fifth,24,0.24663482374588327,0 83 | 82,Lowell,Fifth,19,0.4274773441395094,0 84 | 83,Jonathon,Fifth,24,0.25606595975845636,0 85 | 84,Chester,Fifth,25,0.46493919042049,0 86 | 85,Harold,Fifth,28,0.513422887269918,0 87 | 86,Michael,Fifth,28,0.39755884644820494,0 88 | 87,James,Fifth,23,0.6530244663048064,0 89 | 88,Charles,Fifth,18,0.1343891209496355,0 90 | 89,Arthur,Fifth,21,0.54633648215451,0 91 | 90,Robert,Fifth,21,0.4861460227694609,0 92 | 91,William,Fifth,30,0.3921417409441929,0 93 | 92,Raymond,Fifth,25,0.4480145122152221,0 94 | 93,Michael,Fifth,18,0.2895322553535547,0 95 | 94,James,Fifth,27,0.5556006321756004,0 96 | 95,James,Fifth,22,0.33298530592953224,0 97 | 96,Jayson,Fifth,30,0.5534706204383935,0 98 | 97,Don,Fifth,24,0.39111908973703835,0 99 | 98,Joseph,Fifth,29,0.47608883985035955,0 100 | 99,Paul,Fifth,22,0.4917814435596114,0 101 | 100,Roberto,Fifth,27,0.2864726835208149,0 102 | 101,Vanessa,Fifth,25,0.32240557084025245,1 103 | 102,Alice,Fifth,23,0.3873879922664489,1 104 | 103,Josephine,Fifth,27,0.3282728336658397,1 105 | 104,Annie,Fifth,28,0.4292354851015388,1 106 | 105,Veronica,Fifth,18,0.5130379751461882,1 107 | 106,Elizabeth,Fifth,23,0.6200069282960567,1 108 | 107,Marie,Fifth,29,0.5305440752698534,1 109 | 108,Barbara,Fifth,20,0.581230146764938,1 110 | 109,Donna,Fifth,29,0.5061012557099496,1 111 | 110,Shannon,Fifth,29,0.4003092993878432,1 112 | 111,Miranda,Fifth,28,0.3966579923998648,1 113 | 112,Brianna,Fifth,22,0.4044932844985302,1 114 | 113,Willie,Fifth,25,0.4219500163027606,1 115 | 114,Rachel,Fifth,21,0.46196010449935565,1 116 | 115,Maria,Fifth,21,0.38262880190792703,1 117 | 116,Barbara,Fifth,23,0.6605327215948222,1 118 | 117,Mattie,Fifth,26,0.37250587324950624,1 119 | 118,Barbara,Fifth,22,0.45841455222613636,1 120 | 119,Nicole,Fifth,22,0.48452856368709596,1 121 | 120,Kimberly,Fifth,25,0.464215401871029,1 122 | 121,Maya,Fifth,27,0.49119148321320344,1 123 | 122,Tammy,Fifth,21,0.49778661180359024,1 124 | 123,Marilyn,Fifth,24,0.2746206067171284,1 125 | 124,Karen,Fifth,26,0.34019782551435035,1 126 | 125,Sherri,Fifth,20,0.5539030801360647,1 127 | 126,Lisa,Fifth,22,0.3267431688611382,1 128 | 127,Ruth,Fifth,23,0.3979089673009449,1 129 | 128,Beatriz,Fifth,22,0.34650654217180127,1 130 | 129,Wilma,Fifth,25,0.5879885635010755,1 131 | 130,Alyssa,Fifth,30,0.3110453360265941,1 132 | 131,Samira,Fifth,24,0.4966999425640137,1 133 | 132,Danielle,Fifth,26,0.491412904918258,1 134 | 133,Rachal,Fifth,30,0.3745208278233588,1 135 | 134,Candice,Fifth,30,0.4693496343903035,1 136 | 135,Yvonne,Fifth,30,0.5183966208486301,1 137 | 136,Lorraine,Fifth,19,0.22125380850421314,1 138 | 137,Minnie,Fifth,25,0.4017895402362002,1 139 | 138,Katherine,Fifth,27,0.3111834904558713,1 140 | 139,Teresa,Fifth,24,0.4792189919803444,1 141 | 140,Suzanne,Fifth,21,0.29194342172276844,1 142 | 141,Maria,Fifth,30,0.3549492541600732,1 143 | 142,Laura,Fifth,21,0.5095189627626867,1 144 | 143,Georgette,Fifth,20,0.3185687995452637,1 145 | 144,Marcia,Fifth,19,0.4910251050280039,1 146 | 145,Susie,Fifth,21,0.3780270031378614,1 147 | 146,Deanna,Fifth,18,0.4875711143677395,1 148 | 147,Harriet,Fifth,22,0.43594402443028124,1 149 | 148,Laverne,Fifth,23,0.3811352664825599,1 150 | 149,Evelyn,Fifth,27,0.5694106941686307,1 151 | 150,Jessica,Fifth,22,0.40757243385825653,1 152 | 151,Mary,Fifth,28,0.2728677075643247,1 153 | 152,Maurine,Fifth,23,0.20588619260498486,1 154 | 153,Millicent,Fifth,21,0.38524610641214285,1 155 | 154,Twila,Fifth,22,0.34390712901976256,1 156 | 155,Kim,Fifth,27,0.3666235979455144,1 157 | 156,Elida,Fifth,27,0.28124188528000604,1 158 | 157,Robin,Fifth,21,0.4640272080065959,1 159 | 158,Katrina,Fifth,23,0.35779050163100723,1 160 | 159,Christina,Fifth,23,0.36892688462174056,1 161 | 160,Ashley,Fifth,21,0.22507669643361258,1 162 | 161,Katherine,Fifth,19,0.4042319267681077,1 163 | 162,Stella,Fifth,18,0.4436891065207518,1 164 | 163,Lillian,Fifth,29,0.527608507368262,1 165 | 164,Megan,Fifth,26,0.40422084740205466,1 166 | 165,Martha,Fifth,30,0.3591413524101543,1 167 | 166,Tamra,Fifth,22,0.19236452938490317,1 168 | 167,Barbara,Fifth,23,0.32698403622155636,1 169 | 168,Helen,Fifth,23,0.44411393579508646,1 170 | 169,Delores,Fifth,20,0.5139960240547053,1 171 | 170,Cassandra,Fifth,19,0.42676143420152535,1 172 | 171,Shawn,Fifth,20,0.5378242547602982,1 173 | 172,Diane,Fifth,23,0.4125629736011582,1 174 | 173,Lena,Fifth,26,0.5246445275778342,1 175 | 174,Renee,Fifth,29,0.486077194665446,1 176 | 175,Linda,Fifth,20,0.5951980718027695,1 177 | 176,Brooke,Fifth,21,0.3795568408245569,1 178 | 177,Angela,Fifth,24,0.2999944261889942,1 179 | 178,Bridget,Fifth,21,0.4964390907069347,1 180 | 179,Betty,Fifth,24,0.28765443889862635,1 181 | 180,Lola,Fifth,30,0.5549320942544064,1 182 | 181,Debra,Fifth,21,0.4119099845598452,1 183 | 182,Debra,Fifth,29,0.4681995090686376,1 184 | 183,Diana,Fifth,19,0.47562711490226456,1 185 | 184,Mary,Fifth,28,0.3889339115570191,1 186 | 185,Mable,Fifth,23,0.46852170434057483,1 187 | 186,Jeanne,Fifth,30,0.3453372689435743,1 188 | 187,Sharon,Fifth,26,0.4852001046690406,1 189 | 188,Lucia,Fifth,18,0.4097657078090752,1 190 | 189,Kimberly,Fifth,24,0.3905678965833977,1 191 | 190,Crystal,Fifth,19,0.27434052289694083,1 192 | 191,Esther,Fifth,25,0.4463634593916836,1 193 | 192,Della,Fifth,28,0.1970736352537894,1 194 | 193,Joy,Fifth,20,0.4042112850508673,1 195 | 194,Benita,Fifth,24,0.3406655139226564,1 196 | 195,Doris,Fifth,18,0.4074515634660152,1 197 | 196,Francisca,Fifth,27,0.260849048442554,1 198 | 197,Nia,Fifth,20,0.4311054263973579,1 199 | 198,Christina,Fifth,29,0.2313163263033352,1 200 | 199,Marta,Fifth,26,0.42494782392298514,1 201 | 200,Julia,Fifth,25,0.2804744065097556,1 202 | -------------------------------------------------------------------------------- /SimData/FifthDayData.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/FifthDayData.dta -------------------------------------------------------------------------------- /SimData/FirstDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,First,23,0.10897646124769803,0 3 | 2,Billie,First,22,0.038128151990411085,0 4 | 3,Robert,First,20,0.09470610657553061,0 5 | 4,Don,First,27,0.05763294955454182,0 6 | 5,Joseph,First,21,-0.17539269887811798,0 7 | 6,James,First,25,-0.028602783349194333,0 8 | 7,Delbert,First,26,0.030843062854017972,0 9 | 8,Gary,First,24,0.024686720860376106,0 10 | 9,Scott,First,27,-0.09831861219062818,0 11 | 10,Steve,First,27,-0.14282220080393762,0 12 | 11,Frank,First,18,-0.014448817873279195,0 13 | 12,Eric,First,23,0.11771528270325082,0 14 | 13,Wendell,First,27,0.11174575427756667,0 15 | 14,James,First,21,0.2814516262378201,0 16 | 15,Paul,First,20,0.034195710771368765,0 17 | 16,Juan,First,26,-0.0722285882641357,0 18 | 17,Walter,First,30,-0.046102937603886,0 19 | 18,Richard,First,23,0.06643682866597922,0 20 | 19,Felix,First,21,0.0589116112546299,0 21 | 20,Andre,First,21,0.05222609553945291,0 22 | 21,Jeffrey,First,26,-0.02202696908730138,0 23 | 22,Dave,First,21,-0.005605375859106315,0 24 | 23,James,First,29,-0.013009178763003683,0 25 | 24,Lemuel,First,27,-0.11241145401820794,0 26 | 25,Tommy,First,23,0.32418689762983804,0 27 | 26,Carlos,First,22,-0.05907451376366729,0 28 | 27,Willie,First,19,-0.06228547589623609,0 29 | 28,Joseph,First,19,0.18942228042387974,0 30 | 29,Everette,First,25,0.15980703110673106,0 31 | 30,Marvin,First,28,-0.030924208119599547,0 32 | 31,Javier,First,30,0.021551906357636118,0 33 | 32,Henry,First,27,-0.10883151725633844,0 34 | 33,Danial,First,19,0.1053417842300837,0 35 | 34,Nathan,First,28,-0.17895650906830057,0 36 | 35,Ted,First,29,-0.06071792752737895,0 37 | 36,Terry,First,29,0.045849020081357696,0 38 | 37,Steve,First,28,0.1824338878631989,0 39 | 38,Thomas,First,24,-0.04827982576667503,0 40 | 39,Roger,First,24,0.059559147825473446,0 41 | 40,Mohammed,First,26,-0.08094014331772345,0 42 | 41,Willie,First,19,-0.0691148568662879,0 43 | 42,Kirk,First,20,0.05914574022000262,0 44 | 43,Michael,First,20,-0.022022254298127486,0 45 | 44,Michael,First,24,0.07193417138914365,0 46 | 45,Eric,First,20,0.05536544839013602,0 47 | 46,Clarence,First,19,-0.12805228614852646,0 48 | 47,Carlton,First,23,0.057149533566528514,0 49 | 48,Luis,First,18,-0.11454446385116518,0 50 | 49,John,First,30,0.13888615554804137,0 51 | 50,Charles,First,23,-0.14812458423672684,0 52 | 51,Larry,First,30,-0.02370466303778498,0 53 | 52,Kennith,First,22,-0.13267816075931302,0 54 | 53,John,First,24,-0.05732636987241994,0 55 | 54,David,First,20,0.013012222406338633,0 56 | 55,Taylor,First,20,0.0037723135378083987,0 57 | 56,Michael,First,23,-0.11152904530045722,0 58 | 57,Wesley,First,21,-0.14765049910397657,0 59 | 58,Jack,First,20,0.0032421121217859673,0 60 | 59,Drew,First,22,-0.016197944161304004,0 61 | 60,Gary,First,20,0.1370971918776753,0 62 | 61,Robert,First,26,0.15778176925861376,0 63 | 62,Charles,First,29,-0.01703129547062726,0 64 | 63,Lawrence,First,24,0.10135507383230624,0 65 | 64,Billy,First,25,-0.05282943731014372,0 66 | 65,Travis,First,19,0.1665841829704349,0 67 | 66,Kelly,First,21,0.14152342255967254,0 68 | 67,Johnnie,First,23,0.03939829993269143,0 69 | 68,Kenneth,First,25,0.048702155177848414,0 70 | 69,John,First,19,0.10129436143297517,0 71 | 70,Tony,First,22,0.04928507591825808,0 72 | 71,Salvatore,First,30,0.181671822039305,0 73 | 72,Jack,First,23,-0.006638061006444093,0 74 | 73,Jose,First,28,0.020006118468201998,0 75 | 74,John,First,24,0.10880510496421424,0 76 | 75,Richard,First,18,-0.028965415058133978,0 77 | 76,Alan,First,24,0.19807095792769464,0 78 | 77,Edward,First,30,-0.09816041125958858,0 79 | 78,Kenny,First,30,-0.14121243364562486,0 80 | 79,Ian,First,24,-0.09267577361215006,0 81 | 80,Brian,First,29,0.0528982869623768,0 82 | 81,Chad,First,24,-0.20611141164925267,0 83 | 82,Lowell,First,19,-0.09492193715251877,0 84 | 83,Jonathon,First,24,0.10426417987279323,0 85 | 84,Chester,First,25,0.02917845392100705,0 86 | 85,Harold,First,28,-0.10464339014509766,0 87 | 86,Michael,First,28,-0.22976186113024336,0 88 | 87,James,First,23,0.06599296225043418,0 89 | 88,Charles,First,18,-0.00770720688238659,0 90 | 89,Arthur,First,21,0.15647973857320152,0 91 | 90,Robert,First,21,-0.1008416098308173,0 92 | 91,William,First,30,-0.011471030348396286,0 93 | 92,Raymond,First,25,0.0358306313660028,0 94 | 93,Michael,First,18,-0.1573367334927374,0 95 | 94,James,First,27,-0.10881507582071125,0 96 | 95,James,First,22,0.10508456243185203,0 97 | 96,Jayson,First,30,-0.10424170887262513,0 98 | 97,Don,First,24,-0.029247041139167703,0 99 | 98,Joseph,First,29,0.155309215083431,0 100 | 99,Paul,First,22,0.07013265275096053,0 101 | 100,Roberto,First,27,0.026080687595462004,0 102 | 101,Vanessa,First,25,-0.017763560454362774,1 103 | 102,Alice,First,23,-0.001140851661521542,1 104 | 103,Josephine,First,27,-0.09051008748414019,1 105 | 104,Annie,First,28,-0.0806197374583108,1 106 | 105,Veronica,First,18,0.07520860620917572,1 107 | 106,Elizabeth,First,23,0.060764503691354756,1 108 | 107,Marie,First,29,-0.03248236859726127,1 109 | 108,Barbara,First,20,0.0805670954402633,1 110 | 109,Donna,First,29,-0.21509492079707038,1 111 | 110,Shannon,First,29,-0.11568825899678066,1 112 | 111,Miranda,First,28,0.27262273365544515,1 113 | 112,Brianna,First,22,-0.12157350002724797,1 114 | 113,Willie,First,25,-0.029383437877644643,1 115 | 114,Rachel,First,21,0.22589854969656253,1 116 | 115,Maria,First,21,-0.23326506347347103,1 117 | 116,Barbara,First,23,0.054250916242555874,1 118 | 117,Mattie,First,26,0.13571426230076428,1 119 | 118,Barbara,First,22,-0.1965747561218218,1 120 | 119,Nicole,First,22,-0.1565476051347261,1 121 | 120,Kimberly,First,25,-0.09994651609813442,1 122 | 121,Maya,First,27,0.09358297511595336,1 123 | 122,Tammy,First,21,-0.004390824850492973,1 124 | 123,Marilyn,First,24,0.032457210771900696,1 125 | 124,Karen,First,26,-0.13459263434055646,1 126 | 125,Sherri,First,20,-0.015193550828251407,1 127 | 126,Lisa,First,22,-0.11347691288648531,1 128 | 127,Ruth,First,23,0.055865984819626915,1 129 | 128,Beatriz,First,22,0.06997910592959743,1 130 | 129,Wilma,First,25,-0.04945112990449184,1 131 | 130,Alyssa,First,30,-0.07662399865590172,1 132 | 131,Samira,First,24,0.01034151377865173,1 133 | 132,Danielle,First,26,0.04229332559630411,1 134 | 133,Rachal,First,30,0.1677837456534348,1 135 | 134,Candice,First,30,-0.05871712288473457,1 136 | 135,Yvonne,First,30,0.040667927715105884,1 137 | 136,Lorraine,First,19,-0.04440857235066666,1 138 | 137,Minnie,First,25,-0.021730420116221182,1 139 | 138,Katherine,First,27,0.03037502423027079,1 140 | 139,Teresa,First,24,-0.02726928364144648,1 141 | 140,Suzanne,First,21,-0.009089649633290826,1 142 | 141,Maria,First,30,0.03463482580551039,1 143 | 142,Laura,First,21,-0.04226816547776138,1 144 | 143,Georgette,First,20,0.15897675687859592,1 145 | 144,Marcia,First,19,0.06897554414780849,1 146 | 145,Susie,First,21,0.1091095129994258,1 147 | 146,Deanna,First,18,-0.15674172844015577,1 148 | 147,Harriet,First,22,-0.02334785971245261,1 149 | 148,Laverne,First,23,0.10551696917046237,1 150 | 149,Evelyn,First,27,-0.057886042613544055,1 151 | 150,Jessica,First,22,0.08216600943648418,1 152 | 151,Mary,First,28,0.20884647980984578,1 153 | 152,Maurine,First,23,-0.24112352659559627,1 154 | 153,Millicent,First,21,-0.015946727093739968,1 155 | 154,Twila,First,22,0.0306250320842089,1 156 | 155,Kim,First,27,0.024625046774610856,1 157 | 156,Elida,First,27,0.17342848177522874,1 158 | 157,Robin,First,21,0.21903069953082382,1 159 | 158,Katrina,First,23,0.03298483022471462,1 160 | 159,Christina,First,23,0.07357397675690168,1 161 | 160,Ashley,First,21,0.04239173563270782,1 162 | 161,Katherine,First,19,-0.03775795234057512,1 163 | 162,Stella,First,18,-0.07385708993679112,1 164 | 163,Lillian,First,29,0.00790146379428482,1 165 | 164,Megan,First,26,-0.03618388871549306,1 166 | 165,Martha,First,30,0.0909534592227706,1 167 | 166,Tamra,First,22,0.1550290612700058,1 168 | 167,Barbara,First,23,-0.0037969093865728946,1 169 | 168,Helen,First,23,0.023837324461743453,1 170 | 169,Delores,First,20,0.19452822900602051,1 171 | 170,Cassandra,First,19,-0.03859146362994846,1 172 | 171,Shawn,First,20,0.02893942004208489,1 173 | 172,Diane,First,23,-0.004807480384385637,1 174 | 173,Lena,First,26,-0.11684041991234509,1 175 | 174,Renee,First,29,0.049313895573948045,1 176 | 175,Linda,First,20,-0.03467974730723032,1 177 | 176,Brooke,First,21,0.008791157544219533,1 178 | 177,Angela,First,24,-0.023278936243868825,1 179 | 178,Bridget,First,21,0.09784220620296205,1 180 | 179,Betty,First,24,0.04852421377717461,1 181 | 180,Lola,First,30,-0.14883838016728165,1 182 | 181,Debra,First,21,-0.05556686524254254,1 183 | 182,Debra,First,29,0.01691808229559538,1 184 | 183,Diana,First,19,0.015041292008131533,1 185 | 184,Mary,First,28,0.00015435690567660825,1 186 | 185,Mable,First,23,0.13725388155749396,1 187 | 186,Jeanne,First,30,0.0724776055865148,1 188 | 187,Sharon,First,26,-0.134736692561259,1 189 | 188,Lucia,First,18,0.14554371530227145,1 190 | 189,Kimberly,First,24,-0.022699486096346328,1 191 | 190,Crystal,First,19,-0.2172864602215451,1 192 | 191,Esther,First,25,0.14421628760613936,1 193 | 192,Della,First,28,-0.043263232094207564,1 194 | 193,Joy,First,20,0.13578750453237243,1 195 | 194,Benita,First,24,-0.04105369294321446,1 196 | 195,Doris,First,18,0.026836633003138967,1 197 | 196,Francisca,First,27,0.11984932990251254,1 198 | 197,Nia,First,20,-0.02537296023962914,1 199 | 198,Christina,First,29,0.12829774867260377,1 200 | 199,Marta,First,26,-0.10920587961951617,1 201 | 200,Julia,First,25,0.07251166220746179,1 202 | -------------------------------------------------------------------------------- /SimData/FirstDayData.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/FirstDayData.dta -------------------------------------------------------------------------------- /SimData/FirstDayData.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/FirstDayData.sav -------------------------------------------------------------------------------- /SimData/FourthDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,Fourth,23,0.34856612838091916,0 3 | 2,Billie,Fourth,22,0.377046691834601,0 4 | 3,Robert,Fourth,20,0.2740935513422149,0 5 | 4,Don,Fourth,27,0.41566529975653754,0 6 | 5,Joseph,Fourth,21,0.5030523481793486,0 7 | 6,James,Fourth,25,0.6686813449276366,0 8 | 7,Delbert,Fourth,26,0.32226165937671875,0 9 | 8,Gary,Fourth,24,0.3916982393821837,0 10 | 9,Scott,Fourth,27,0.4201650459243951,0 11 | 10,Steve,Fourth,27,0.46766195412379336,0 12 | 11,Frank,Fourth,18,0.45898345582961153,0 13 | 12,Eric,Fourth,23,0.5479525573253385,0 14 | 13,Wendell,Fourth,27,0.3702964118645945,0 15 | 14,James,Fourth,21,0.35552427393773767,0 16 | 15,Paul,Fourth,20,0.4442014199214026,0 17 | 16,Juan,Fourth,26,0.3882999629906536,0 18 | 17,Walter,Fourth,30,0.32327223135129324,0 19 | 18,Richard,Fourth,23,0.3807334038765816,0 20 | 19,Felix,Fourth,21,0.300021601125039,0 21 | 20,Andre,Fourth,21,0.24178556581323463,0 22 | 21,Jeffrey,Fourth,26,0.3714903851225104,0 23 | 22,Dave,Fourth,21,0.41190612914430863,0 24 | 23,James,Fourth,29,0.43130600893768073,0 25 | 24,Lemuel,Fourth,27,0.34373427693833364,0 26 | 25,Tommy,Fourth,23,0.23971982401038122,0 27 | 26,Carlos,Fourth,22,0.497806755010102,0 28 | 27,Willie,Fourth,19,0.39412611375948853,0 29 | 28,Joseph,Fourth,19,0.319986738223184,0 30 | 29,Everette,Fourth,25,0.45940447673553014,0 31 | 30,Marvin,Fourth,28,0.37466522857887347,0 32 | 31,Javier,Fourth,30,0.28361624097694305,0 33 | 32,Henry,Fourth,27,0.2282030327489958,0 34 | 33,Danial,Fourth,19,0.3700762354109632,0 35 | 34,Nathan,Fourth,28,0.30766871044542515,0 36 | 35,Ted,Fourth,29,0.38120949919504077,0 37 | 36,Terry,Fourth,29,0.228549364957517,0 38 | 37,Steve,Fourth,28,0.5881994334164636,0 39 | 38,Thomas,Fourth,24,0.3856087348237665,0 40 | 39,Roger,Fourth,24,0.4426257213709829,0 41 | 40,Mohammed,Fourth,26,0.5443393058458627,0 42 | 41,Willie,Fourth,19,0.43791619951565414,0 43 | 42,Kirk,Fourth,20,0.3819867435847643,0 44 | 43,Michael,Fourth,20,0.42109534434084045,0 45 | 44,Michael,Fourth,24,0.28189379993491365,0 46 | 45,Eric,Fourth,20,0.34070465640095043,0 47 | 46,Clarence,Fourth,19,0.2763980207226272,0 48 | 47,Carlton,Fourth,23,0.5623088734650281,0 49 | 48,Luis,Fourth,18,0.31446654813259506,0 50 | 49,John,Fourth,30,0.4206397408356436,0 51 | 50,Charles,Fourth,23,0.42186817356912676,0 52 | 51,Larry,Fourth,30,0.20116435342623212,0 53 | 52,Kennith,Fourth,22,0.3788108610614584,0 54 | 53,John,Fourth,24,0.3591879104497363,0 55 | 54,David,Fourth,20,0.42387253343350634,0 56 | 55,Taylor,Fourth,20,0.3877910286193835,0 57 | 56,Michael,Fourth,23,0.14573824756388323,0 58 | 57,Wesley,Fourth,21,0.26104082671581763,0 59 | 58,Jack,Fourth,20,0.3668368751077959,0 60 | 59,Drew,Fourth,22,0.2243892795312477,0 61 | 60,Gary,Fourth,20,0.40632791512689026,0 62 | 61,Robert,Fourth,26,0.32293935462529455,0 63 | 62,Charles,Fourth,29,0.41404518665490153,0 64 | 63,Lawrence,Fourth,24,0.5737876910026611,0 65 | 64,Billy,Fourth,25,0.25672692751851567,0 66 | 65,Travis,Fourth,19,0.38254645695292794,0 67 | 66,Kelly,Fourth,21,0.24976670227368256,0 68 | 67,Johnnie,Fourth,23,0.4264878390845841,0 69 | 68,Kenneth,Fourth,25,0.37648218114143644,0 70 | 69,John,Fourth,19,0.38565431812985124,0 71 | 70,Tony,Fourth,22,0.5227296520871594,0 72 | 71,Salvatore,Fourth,30,0.353377740999313,0 73 | 72,Jack,Fourth,23,0.46564639107520617,0 74 | 73,Jose,Fourth,28,0.47926400031478067,0 75 | 74,John,Fourth,24,0.4936502087816483,0 76 | 75,Richard,Fourth,18,0.4916099257615466,0 77 | 76,Alan,Fourth,24,0.4680982718409892,0 78 | 77,Edward,Fourth,30,0.40179927912901464,0 79 | 78,Kenny,Fourth,30,0.5056629886231886,0 80 | 79,Ian,Fourth,24,0.3373081263161796,0 81 | 80,Brian,Fourth,29,0.3858918012764013,0 82 | 81,Chad,Fourth,24,0.3987378043345847,0 83 | 82,Lowell,Fourth,19,0.2775041697675872,0 84 | 83,Jonathon,Fourth,24,0.39468734802669114,0 85 | 84,Chester,Fourth,25,0.4846246091428523,0 86 | 85,Harold,Fourth,28,0.34391998854946815,0 87 | 86,Michael,Fourth,28,0.4396240563324852,0 88 | 87,James,Fourth,23,0.45944933276198535,0 89 | 88,Charles,Fourth,18,0.29736071948922327,0 90 | 89,Arthur,Fourth,21,0.3874817488644538,0 91 | 90,Robert,Fourth,21,0.27578311464729943,0 92 | 91,William,Fourth,30,0.41595100404870416,0 93 | 92,Raymond,Fourth,25,0.3319726165000659,0 94 | 93,Michael,Fourth,18,0.39970325921640615,0 95 | 94,James,Fourth,27,0.4470893622287899,0 96 | 95,James,Fourth,22,0.33236527804458765,0 97 | 96,Jayson,Fourth,30,0.18488006562531636,0 98 | 97,Don,Fourth,24,0.3563785233895987,0 99 | 98,Joseph,Fourth,29,0.31149720683196674,0 100 | 99,Paul,Fourth,22,0.34441795534048913,0 101 | 100,Roberto,Fourth,27,0.4024739479704382,0 102 | 101,Vanessa,Fourth,25,0.23084437245185016,1 103 | 102,Alice,Fourth,23,0.44482043300938245,1 104 | 103,Josephine,Fourth,27,0.35954439960470286,1 105 | 104,Annie,Fourth,28,0.4037623214927178,1 106 | 105,Veronica,Fourth,18,0.4616276208105221,1 107 | 106,Elizabeth,Fourth,23,0.2199452017950949,1 108 | 107,Marie,Fourth,29,0.5734469964253677,1 109 | 108,Barbara,Fourth,20,0.3452579184219273,1 110 | 109,Donna,Fourth,29,0.5101356717558677,1 111 | 110,Shannon,Fourth,29,0.4870772079053962,1 112 | 111,Miranda,Fourth,28,0.2809131292528238,1 113 | 112,Brianna,Fourth,22,0.36882768469414967,1 114 | 113,Willie,Fourth,25,0.2817898088297147,1 115 | 114,Rachel,Fourth,21,0.45570969255802446,1 116 | 115,Maria,Fourth,21,0.29253676504995946,1 117 | 116,Barbara,Fourth,23,0.29014639234537465,1 118 | 117,Mattie,Fourth,26,0.49931575605614054,1 119 | 118,Barbara,Fourth,22,0.38570075922708813,1 120 | 119,Nicole,Fourth,22,0.2550366801374672,1 121 | 120,Kimberly,Fourth,25,0.3346951068347461,1 122 | 121,Maya,Fourth,27,0.24178187224065179,1 123 | 122,Tammy,Fourth,21,0.346438019080797,1 124 | 123,Marilyn,Fourth,24,0.3324888987819297,1 125 | 124,Karen,Fourth,26,0.3395216825531733,1 126 | 125,Sherri,Fourth,20,0.34838312574813657,1 127 | 126,Lisa,Fourth,22,0.4975986556009898,1 128 | 127,Ruth,Fourth,23,0.3828730342575669,1 129 | 128,Beatriz,Fourth,22,0.45053342936626845,1 130 | 129,Wilma,Fourth,25,0.3236413786582404,1 131 | 130,Alyssa,Fourth,30,0.2657136686251437,1 132 | 131,Samira,Fourth,24,0.34298357210466884,1 133 | 132,Danielle,Fourth,26,0.2863774316486247,1 134 | 133,Rachal,Fourth,30,0.41931657931420674,1 135 | 134,Candice,Fourth,30,0.36197239860701375,1 136 | 135,Yvonne,Fourth,30,0.3396216205916616,1 137 | 136,Lorraine,Fourth,19,0.27536841443696525,1 138 | 137,Minnie,Fourth,25,0.2985729886390672,1 139 | 138,Katherine,Fourth,27,0.2033364988509848,1 140 | 139,Teresa,Fourth,24,0.2448128277571272,1 141 | 140,Suzanne,Fourth,21,0.46807416665804347,1 142 | 141,Maria,Fourth,30,0.29282989317411895,1 143 | 142,Laura,Fourth,21,0.40339958868431114,1 144 | 143,Georgette,Fourth,20,0.36845262157262815,1 145 | 144,Marcia,Fourth,19,0.32458549991370844,1 146 | 145,Susie,Fourth,21,0.36242385900246166,1 147 | 146,Deanna,Fourth,18,0.40347215560240307,1 148 | 147,Harriet,Fourth,22,0.12973622725941192,1 149 | 148,Laverne,Fourth,23,0.30074102785678153,1 150 | 149,Evelyn,Fourth,27,0.4318597243272191,1 151 | 150,Jessica,Fourth,22,0.24812710439459718,1 152 | 151,Mary,Fourth,28,0.4333845377349502,1 153 | 152,Maurine,Fourth,23,0.4110557673415939,1 154 | 153,Millicent,Fourth,21,0.27794261486105876,1 155 | 154,Twila,Fourth,22,0.4933606186011485,1 156 | 155,Kim,Fourth,27,0.3404094801363547,1 157 | 156,Elida,Fourth,27,0.40927430370998996,1 158 | 157,Robin,Fourth,21,0.32046152782851534,1 159 | 158,Katrina,Fourth,23,0.3531386689255173,1 160 | 159,Christina,Fourth,23,0.6109102060868049,1 161 | 160,Ashley,Fourth,21,0.4424437398309864,1 162 | 161,Katherine,Fourth,19,0.39826806720311575,1 163 | 162,Stella,Fourth,18,0.40856616373834237,1 164 | 163,Lillian,Fourth,29,0.17652259079012125,1 165 | 164,Megan,Fourth,26,0.270331784882343,1 166 | 165,Martha,Fourth,30,0.34443714107087897,1 167 | 166,Tamra,Fourth,22,0.40994029988195163,1 168 | 167,Barbara,Fourth,23,0.2252279642307069,1 169 | 168,Helen,Fourth,23,0.26291192088024945,1 170 | 169,Delores,Fourth,20,0.310787486782465,1 171 | 170,Cassandra,Fourth,19,0.3303517030285769,1 172 | 171,Shawn,Fourth,20,0.29707082871681184,1 173 | 172,Diane,Fourth,23,0.32431689740668906,1 174 | 173,Lena,Fourth,26,0.5990319613217561,1 175 | 174,Renee,Fourth,29,0.2757123422532532,1 176 | 175,Linda,Fourth,20,0.35600521709710803,1 177 | 176,Brooke,Fourth,21,0.3999378764760127,1 178 | 177,Angela,Fourth,24,0.34673102607993733,1 179 | 178,Bridget,Fourth,21,0.3271018468493462,1 180 | 179,Betty,Fourth,24,0.45402369893409966,1 181 | 180,Lola,Fourth,30,0.3103734801925171,1 182 | 181,Debra,Fourth,21,0.3816986053701544,1 183 | 182,Debra,Fourth,29,0.2818684237762095,1 184 | 183,Diana,Fourth,19,0.45616208444402906,1 185 | 184,Mary,Fourth,28,0.45392687542382165,1 186 | 185,Mable,Fourth,23,0.529700547264452,1 187 | 186,Jeanne,Fourth,30,0.2776421885175293,1 188 | 187,Sharon,Fourth,26,0.5174364814954343,1 189 | 188,Lucia,Fourth,18,0.2624853479564313,1 190 | 189,Kimberly,Fourth,24,0.49863004523501453,1 191 | 190,Crystal,Fourth,19,0.4031701809858398,1 192 | 191,Esther,Fourth,25,0.3893473345654394,1 193 | 192,Della,Fourth,28,0.28463410281318635,1 194 | 193,Joy,Fourth,20,0.25740728380089895,1 195 | 194,Benita,Fourth,24,0.377863984758393,1 196 | 195,Doris,Fourth,18,0.21280721727446952,1 197 | 196,Francisca,Fourth,27,0.480961160636833,1 198 | 197,Nia,Fourth,20,0.4193608044272194,1 199 | 198,Christina,Fourth,29,0.27334737234146533,1 200 | 199,Marta,Fourth,26,0.37572150100438095,1 201 | 200,Julia,Fourth,25,0.15106477452894013,1 202 | -------------------------------------------------------------------------------- /SimData/MissingData.csv: -------------------------------------------------------------------------------- 1 | ,ID,Name,Day,Age,Response,Gender 2 | 0,1,John,Sixth,23,0.5627187141917855,0 3 | 1,2,Billie,Sixth,22,Not Available,0 4 | 2,3,Robert,Sixth,20,Not Available,0 5 | 3,4,Don,Sixth,27,0.5229607643652396,0 6 | 4,5,Joseph,Sixth,21,Not Available,0 7 | 5,6,James,Sixth,25,0.4571030819121562,0 8 | 6,7,Delbert,Sixth,26,Not Available,0 9 | 7,8,Gary,Sixth,24,Not Available,0 10 | 8,9,Scott,Sixth,27,Not Available,0 11 | 9,10,Steve,Sixth,27,Not Available,0 12 | 10,11,Frank,Sixth,18,Not Available,0 13 | 11,12,Eric,Sixth,23,0.512687736538734,0 14 | 12,13,Wendell,Sixth,27,Not Available,0 15 | 13,14,James,Sixth,21,0.4517822116966478,0 16 | 14,15,Paul,Sixth,20,Not Available,0 17 | 15,16,Juan,Sixth,26,0.511309398445967,0 18 | 16,17,Walter,Sixth,30,0.4876374026809741,0 19 | 17,18,Richard,Sixth,23,0.5248170458806685,0 20 | 18,19,Felix,Sixth,21,0.6719892697762015,0 21 | 19,20,Andre,Sixth,21,0.4449211650987591,0 22 | 20,21,Jeffrey,Sixth,26,0.4035001434778803,0 23 | 21,22,Dave,Sixth,21,0.5538101525320848,0 24 | 22,23,James,Sixth,29,0.3699416770202328,0 25 | 23,24,Lemuel,Sixth,27,0.29449460162573504,0 26 | 24,25,Tommy,Sixth,23,0.7125960371855942,0 27 | 25,26,Carlos,Sixth,22,0.560689934295333,0 28 | 26,27,Willie,Sixth,19,0.4426790378672592,0 29 | 27,28,Joseph,Sixth,19,0.467343626114986,0 30 | 28,29,Everette,Sixth,25,0.6954712644704699,0 31 | 29,30,Marvin,Sixth,28,0.5338449267566893,0 32 | 30,31,Javier,Sixth,30,0.5053801595683928,0 33 | 31,32,Henry,Sixth,27,0.4386438165903696,0 34 | 32,33,Danial,Sixth,19,0.443953740251032,0 35 | 33,34,Nathan,Sixth,28,0.6104137051112386,0 36 | 34,35,Ted,Sixth,29,0.4387538730184034,0 37 | 35,36,Terry,Sixth,29,0.5828116958700524,0 38 | 36,37,Steve,Sixth,28,0.4883025967736782,0 39 | 37,38,Thomas,Sixth,24,0.5708048197461699,0 40 | 38,39,Roger,Sixth,24,0.5903570754206436,0 41 | 39,40,Mohammed,Sixth,26,0.5710369057808238,0 42 | 40,41,Willie,Sixth,19,0.3545339060940288,0 43 | 41,42,Kirk,Sixth,20,0.5931082193103651,0 44 | 42,43,Michael,Sixth,20,0.4278974215619132,0 45 | 43,44,Michael,Sixth,24,0.7627312850675907,0 46 | 44,45,Eric,Sixth,20,0.3609865803366528,0 47 | 45,46,Clarence,Sixth,19,0.2723403918403283,0 48 | 46,47,Carlton,Sixth,23,0.4830924544938338,0 49 | 47,48,Luis,Sixth,18,0.5978956173958363,0 50 | 48,49,John,Sixth,30,0.4619513788658993,0 51 | 49,50,Charles,Sixth,23,0.4479710238937036,0 52 | 50,51,Larry,Sixth,30,0.6005101847929761,0 53 | 51,52,Kennith,Sixth,22,0.6407673041409321,0 54 | 52,53,John,Sixth,24,0.3166798757885672,0 55 | 53,54,David,Sixth,20,0.5460591953743089,0 56 | 54,55,Taylor,Sixth,20,0.4202468709201124,0 57 | 55,56,Michael,Sixth,23,0.503040391617558,0 58 | 56,57,Wesley,Sixth,21,0.5252979188424935,0 59 | 57,58,Jack,Sixth,20,0.4456059188027668,0 60 | 58,59,Drew,Sixth,22,0.4963736106787016,0 61 | 59,60,Gary,Sixth,20,0.4475585006721042,0 62 | 60,61,Robert,Sixth,26,0.433021422000792,0 63 | 61,62,Charles,Sixth,29,0.4821278711793452,0 64 | 62,63,Lawrence,Sixth,24,0.4054221747322013,0 65 | 63,64,Billy,Sixth,25,0.5459887509348277,0 66 | 64,65,Travis,Sixth,19,0.4482537127438954,0 67 | 65,66,Kelly,Sixth,21,0.4947167997604914,0 68 | 66,67,Johnnie,Sixth,23,0.4946061853363077,0 69 | 67,68,Kenneth,Sixth,25,0.5652839716526309,0 70 | 68,69,John,Sixth,19,0.34318786073239826,0 71 | 69,70,Tony,Sixth,22,0.5142301178099664,0 72 | 70,71,Salvatore,Sixth,30,0.5009923942795725,0 73 | 71,72,Jack,Sixth,23,0.45398884881089896,0 74 | 72,73,Jose,Sixth,28,0.4821673612287138,0 75 | 73,74,John,Sixth,24,0.4722656940541789,0 76 | 74,75,Richard,Sixth,18,0.6197608040669679,0 77 | 75,76,Alan,Sixth,24,0.5335412407017036,0 78 | 76,77,Edward,Sixth,30,0.5938522409655508,0 79 | 77,78,Kenny,Sixth,30,0.41093795718540826,0 80 | 78,79,Ian,Sixth,24,0.4959098187308255,0 81 | 79,80,Brian,Sixth,29,0.5308543203711171,0 82 | 80,81,Chad,Sixth,24,0.3173498201434641,0 83 | 81,82,Lowell,Sixth,19,0.4248495932603885,0 84 | 82,83,Jonathon,Sixth,24,0.4720788961850801,0 85 | 83,84,Chester,Sixth,25,0.5393488384028237,0 86 | 84,85,Harold,Sixth,28,0.4663681827526402,0 87 | 85,86,Michael,Sixth,28,0.564364490050406,0 88 | 86,87,James,Sixth,23,0.6633255597766218,0 89 | 87,88,Charles,Sixth,18,0.6406938401660549,0 90 | 88,89,Arthur,Sixth,21,0.6547226491561557,0 91 | 89,90,Robert,Sixth,21,0.4650420803037942,0 92 | 90,91,William,Sixth,30,0.5665519641804087,0 93 | 91,92,Raymond,Sixth,25,0.6234046756685127,0 94 | 92,93,Michael,Sixth,18,0.4054389794121322,0 95 | 93,94,James,Sixth,27,0.4832817739075767,0 96 | 94,95,James,Sixth,22,0.4623935581996443,0 97 | 95,96,Jayson,Sixth,30,0.6183163685997498,0 98 | 96,97,Don,Sixth,24,0.5733711927951894,0 99 | 97,98,Joseph,Sixth,29,0.4873175435671538,0 100 | 98,99,Paul,Sixth,22,0.688110681439846,0 101 | 99,100,Roberto,Sixth,27,0.434629408779397,0 102 | 100,101,Vanessa,Sixth,25,0.5111038562077044,1 103 | 101,102,Alice,Sixth,23,0.4972015920269251,1 104 | 102,103,Josephine,Sixth,27,0.6222947266717184,1 105 | 103,104,Annie,Sixth,28,0.4729998858439837,1 106 | 104,105,Veronica,Sixth,18,0.4992901882731911,1 107 | 105,106,Elizabeth,Sixth,23,0.3895004576945725,1 108 | 106,107,Marie,Sixth,29,0.4726102218311762,1 109 | 107,108,Barbara,Sixth,20,0.465480091459964,1 110 | 108,109,Donna,Sixth,29,0.4993503685979966,1 111 | 109,110,Shannon,Sixth,29,0.5368721241098889,1 112 | 110,111,Miranda,Sixth,28,0.5454813912263198,1 113 | 111,112,Brianna,Sixth,22,0.452065775094302,1 114 | 112,113,Willie,Sixth,25,0.39099589041022575,1 115 | 113,114,Rachel,Sixth,21,0.4983379588870162,1 116 | 114,115,Maria,Sixth,21,0.6115318473116368,1 117 | 115,116,Barbara,Sixth,23,0.3798873826666749,1 118 | 116,117,Mattie,Sixth,26,0.6024999519165927,1 119 | 117,118,Barbara,Sixth,22,0.5365787533599118,1 120 | 118,119,Nicole,Sixth,22,0.3719803042196256,1 121 | 119,120,Kimberly,Sixth,25,0.3900251680666901,1 122 | 120,121,Maya,Sixth,27,0.5012211361474402,1 123 | 121,122,Tammy,Sixth,21,0.5900732351352194,1 124 | 122,123,Marilyn,Sixth,24,0.5111264805624623,1 125 | 123,124,Karen,Sixth,26,0.5719529873473642,1 126 | 124,125,Sherri,Sixth,20,0.5344687407956776,1 127 | 125,126,Lisa,Sixth,22,0.4285293196734672,1 128 | 126,127,Ruth,Sixth,23,0.4141276409539065,1 129 | 127,128,Beatriz,Sixth,22,0.580891276391413,1 130 | 128,129,Wilma,Sixth,25,0.41292421225187,1 131 | 129,130,Alyssa,Sixth,30,0.5691490519310829,1 132 | 130,131,Samira,Sixth,24,0.5983022594122839,1 133 | 131,132,Danielle,Sixth,26,0.6119405745912091,1 134 | 132,133,Rachal,Sixth,30,0.4502686799797943,1 135 | 133,134,Candice,Sixth,30,0.4958386248306951,1 136 | 134,135,Yvonne,Sixth,30,0.5707502520010675,1 137 | 135,136,Lorraine,Sixth,19,0.4435393854940667,1 138 | 136,137,Minnie,Sixth,25,0.4677632036651698,1 139 | 137,138,Katherine,Sixth,27,0.4042972425845818,1 140 | 138,139,Teresa,Sixth,24,0.6421220921833651,1 141 | 139,140,Suzanne,Sixth,21,0.5422837619955311,1 142 | 140,141,Maria,Sixth,30,0.39329161255034334,1 143 | 141,142,Laura,Sixth,21,0.5375873055372921,1 144 | 142,143,Georgette,Sixth,20,0.392442361268742,1 145 | 143,144,Marcia,Sixth,19,0.5829517088661433,1 146 | 144,145,Susie,Sixth,21,0.4800001615471727,1 147 | 145,146,Deanna,Sixth,18,0.4949746972751772,1 148 | 146,147,Harriet,Sixth,22,0.4651620538037444,1 149 | 147,148,Laverne,Sixth,23,0.21852240343463564,1 150 | 148,149,Evelyn,Sixth,27,0.5256522259273075,1 151 | 149,150,Jessica,Sixth,22,0.4626756113213673,1 152 | 150,151,Mary,Sixth,28,0.5971399636524906,1 153 | 151,152,Maurine,Sixth,23,0.3862647280799457,1 154 | 152,153,Millicent,Sixth,21,0.41539557943222,1 155 | 153,154,Twila,Sixth,22,0.4434321643778448,1 156 | 154,155,Kim,Sixth,27,0.5783197667230191,1 157 | 155,156,Elida,Sixth,27,0.4595253229609275,1 158 | 156,157,Robin,Sixth,21,0.406894345549398,1 159 | 157,158,Katrina,Sixth,23,0.574720890916179,1 160 | 158,159,Christina,Sixth,23,0.4471695747522068,1 161 | 159,160,Ashley,Sixth,21,0.3658777537307304,1 162 | 160,161,Katherine,Sixth,19,0.5443646468409017,1 163 | 161,162,Stella,Sixth,18,0.3811908133421368,1 164 | 162,163,Lillian,Sixth,29,0.5827037820459395,1 165 | 163,164,Megan,Sixth,26,0.6712950290615973,1 166 | 164,165,Martha,Sixth,30,0.5999854536629398,1 167 | 165,166,Tamra,Sixth,22,0.5426967026078454,1 168 | 166,167,Barbara,Sixth,23,0.5266036117738924,1 169 | 167,168,Helen,Sixth,23,0.3827441141401961,1 170 | 168,169,Delores,Sixth,20,0.5125524640036196,1 171 | 169,170,Cassandra,Sixth,19,0.5445426304713614,1 172 | 170,171,Shawn,Sixth,20,0.5415660559256122,1 173 | 171,172,Diane,Sixth,23,0.31139991061482986,1 174 | 172,173,Lena,Sixth,26,0.4249519659560316,1 175 | 173,174,Renee,Sixth,29,0.4841828832501326,1 176 | 174,175,Linda,Sixth,20,0.4267961009247867,1 177 | 175,176,Brooke,Sixth,21,0.5665981844310021,1 178 | 176,177,Angela,Sixth,24,0.4877586266951791,1 179 | 177,178,Bridget,Sixth,21,0.4569162017466531,1 180 | 178,179,Betty,Sixth,24,0.534980963105169,1 181 | 179,180,Lola,Sixth,30,0.5152307543228434,1 182 | 180,181,Debra,Sixth,21,0.40830678550869137,1 183 | 181,182,Debra,Sixth,29,0.6112997110674216,1 184 | 182,183,Diana,Sixth,19,0.41919925141276265,1 185 | 183,184,Mary,Sixth,28,0.5751100677634319,1 186 | 184,185,Mable,Sixth,23,0.4904157402345066,1 187 | 185,186,Jeanne,Sixth,30,0.3049416329938862,1 188 | 186,187,Sharon,Sixth,26,0.4326362856392755,1 189 | 187,188,Lucia,Sixth,18,0.3895300791317597,1 190 | 188,189,Kimberly,Sixth,24,0.5165075984515924,1 191 | 189,190,Crystal,Sixth,19,0.3799857945139376,1 192 | 190,191,Esther,Sixth,25,0.3598757061536821,1 193 | 191,192,Della,Sixth,28,0.5487373450086704,1 194 | 192,193,Joy,Sixth,20,0.5711191682655571,1 195 | 193,194,Benita,Sixth,24,0.5812982298969092,1 196 | 194,195,Doris,Sixth,18,0.5366558449616664,1 197 | 195,196,Francisca,Sixth,27,0.4946573817954197,1 198 | 196,197,Nia,Sixth,20,0.4877552435335766,1 199 | 197,198,Christina,Sixth,29,0.4994693732595509,1 200 | 198,199,Marta,Sixth,26,0.4278674824865973,1 201 | 199,200,Julia,Sixth,25,0.4036795449925428,1 202 | -------------------------------------------------------------------------------- /SimData/NewFifthDayData.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/NewFifthDayData.dta -------------------------------------------------------------------------------- /SimData/SecondDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,Second,23,0.47822531504260707,0 3 | 2,Billie,Second,22,0.0029382753674734863,0 4 | 3,Robert,Second,20,0.12093402273657071,0 5 | 4,Don,Second,27,0.12828046963419742,0 6 | 5,Joseph,Second,21,0.2973830583071586,0 7 | 6,James,Second,25,0.2513542799820886,0 8 | 7,Delbert,Second,26,0.1466088272262309,0 9 | 8,Gary,Second,24,0.32510380256426413,0 10 | 9,Scott,Second,27,0.26625948649179465,0 11 | 10,Steve,Second,27,0.3154149257193317,0 12 | 11,Frank,Second,18,0.2748699932350343,0 13 | 12,Eric,Second,23,0.1382858842203156,0 14 | 13,Wendell,Second,27,0.2500004515092824,0 15 | 14,James,Second,21,0.13995013589836036,0 16 | 15,Paul,Second,20,0.2687264168534552,0 17 | 16,Juan,Second,26,-0.16580057356484224,0 18 | 17,Walter,Second,30,0.32830966478067386,0 19 | 18,Richard,Second,23,0.0959963811339728,0 20 | 19,Felix,Second,21,0.10472906285667613,0 21 | 20,Andre,Second,21,0.14336077546911746,0 22 | 21,Jeffrey,Second,26,0.17670106328943663,0 23 | 22,Dave,Second,21,0.15960216640255903,0 24 | 23,James,Second,29,-0.07992788053560806,0 25 | 24,Lemuel,Second,27,0.20484350712407703,0 26 | 25,Tommy,Second,23,0.3183249390042885,0 27 | 26,Carlos,Second,22,0.3623935896829245,0 28 | 27,Willie,Second,19,0.239495133890413,0 29 | 28,Joseph,Second,19,0.13339905245557093,0 30 | 29,Everette,Second,25,-0.05247099359328755,0 31 | 30,Marvin,Second,28,-0.19573939958729408,0 32 | 31,Javier,Second,30,0.05633173752678569,0 33 | 32,Henry,Second,27,0.27337697150232154,0 34 | 33,Danial,Second,19,0.035732654475504416,0 35 | 34,Nathan,Second,28,0.16710933246405404,0 36 | 35,Ted,Second,29,0.11803075686892277,0 37 | 36,Terry,Second,29,-0.04292625914687434,0 38 | 37,Steve,Second,28,0.095790452791044,0 39 | 38,Thomas,Second,24,0.24672889334284212,0 40 | 39,Roger,Second,24,0.283840873548118,0 41 | 40,Mohammed,Second,26,0.051228487707243225,0 42 | 41,Willie,Second,19,0.20917767367408258,0 43 | 42,Kirk,Second,20,0.19942904917556722,0 44 | 43,Michael,Second,20,0.15422585211393533,0 45 | 44,Michael,Second,24,0.28393200392724266,0 46 | 45,Eric,Second,20,0.09745891695603548,0 47 | 46,Clarence,Second,19,0.31664524842743774,0 48 | 47,Carlton,Second,23,0.1773202254698855,0 49 | 48,Luis,Second,18,0.27839082801769,0 50 | 49,John,Second,30,0.054660561401091684,0 51 | 50,Charles,Second,23,0.2650851301971685,0 52 | 51,Larry,Second,30,0.2332012629598002,0 53 | 52,Kennith,Second,22,0.09145689840254737,0 54 | 53,John,Second,24,0.07367423085546251,0 55 | 54,David,Second,20,0.38788183919220665,0 56 | 55,Taylor,Second,20,-0.09013516583105652,0 57 | 56,Michael,Second,23,0.14929253040151808,0 58 | 57,Wesley,Second,21,0.11829259099025831,0 59 | 58,Jack,Second,20,0.2508762605696313,0 60 | 59,Drew,Second,22,0.338413335092722,0 61 | 60,Gary,Second,20,0.22522207500283975,0 62 | 61,Robert,Second,26,0.23633529611823184,0 63 | 62,Charles,Second,29,0.2795933656881275,0 64 | 63,Lawrence,Second,24,0.24236170132965745,0 65 | 64,Billy,Second,25,0.1899003709475807,0 66 | 65,Travis,Second,19,0.1874615315100216,0 67 | 66,Kelly,Second,21,0.30235002459123894,0 68 | 67,Johnnie,Second,23,0.3163114940981212,0 69 | 68,Kenneth,Second,25,0.2740643666829032,0 70 | 69,John,Second,19,0.012181977400741478,0 71 | 70,Tony,Second,22,0.09835934812516556,0 72 | 71,Salvatore,Second,30,0.4520136386999405,0 73 | 72,Jack,Second,23,0.25539564991327174,0 74 | 73,Jose,Second,28,0.0786106136179638,0 75 | 74,John,Second,24,0.12241580786522864,0 76 | 75,Richard,Second,18,0.40178677716250855,0 77 | 76,Alan,Second,24,0.4372059809366845,0 78 | 77,Edward,Second,30,0.24097122388336403,0 79 | 78,Kenny,Second,30,0.06445127757212021,0 80 | 79,Ian,Second,24,0.15513922981670242,0 81 | 80,Brian,Second,29,0.11010173446673013,0 82 | 81,Chad,Second,24,0.23685904372932107,0 83 | 82,Lowell,Second,19,0.27445529812093933,0 84 | 83,Jonathon,Second,24,0.3479436738157262,0 85 | 84,Chester,Second,25,0.30325852598656555,0 86 | 85,Harold,Second,28,-0.05647148083115222,0 87 | 86,Michael,Second,28,0.2746306882996265,0 88 | 87,James,Second,23,0.28062428042451926,0 89 | 88,Charles,Second,18,0.2194949800913744,0 90 | 89,Arthur,Second,21,0.3179884384595796,0 91 | 90,Robert,Second,21,0.11480878950641295,0 92 | 91,William,Second,30,0.27293437931701964,0 93 | 92,Raymond,Second,25,0.16910338931169927,0 94 | 93,Michael,Second,18,0.1308264205042977,0 95 | 94,James,Second,27,0.2221826008685691,0 96 | 95,James,Second,22,0.18958412720210643,0 97 | 96,Jayson,Second,30,0.15033256868867007,0 98 | 97,Don,Second,24,0.037542972657397755,0 99 | 98,Joseph,Second,29,0.16773969746312756,0 100 | 99,Paul,Second,22,0.3207062846500329,0 101 | 100,Roberto,Second,27,0.24018886173854176,0 102 | 101,Vanessa,Second,25,0.09528604933894898,1 103 | 102,Alice,Second,23,0.35652906143600593,1 104 | 103,Josephine,Second,27,0.23005189692973765,1 105 | 104,Annie,Second,28,0.23758159696237785,1 106 | 105,Veronica,Second,18,-0.015878682547588063,1 107 | 106,Elizabeth,Second,23,0.1801679870121067,1 108 | 107,Marie,Second,29,0.09205007645882486,1 109 | 108,Barbara,Second,20,0.37477524058994466,1 110 | 109,Donna,Second,29,0.3977660558165214,1 111 | 110,Shannon,Second,29,0.12222493339484668,1 112 | 111,Miranda,Second,28,0.35677413548241904,1 113 | 112,Brianna,Second,22,0.032906435688219854,1 114 | 113,Willie,Second,25,0.07333384254232847,1 115 | 114,Rachel,Second,21,-0.13513376055241383,1 116 | 115,Maria,Second,21,0.21243863271006194,1 117 | 116,Barbara,Second,23,0.05104709786353265,1 118 | 117,Mattie,Second,26,0.35652516032014075,1 119 | 118,Barbara,Second,22,0.17856569899742927,1 120 | 119,Nicole,Second,22,0.11565731434005809,1 121 | 120,Kimberly,Second,25,0.16475079684553856,1 122 | 121,Maya,Second,27,0.20817392472459725,1 123 | 122,Tammy,Second,21,0.4659621073459406,1 124 | 123,Marilyn,Second,24,0.058704401624141145,1 125 | 124,Karen,Second,26,0.2657062800649837,1 126 | 125,Sherri,Second,20,-0.14887138583291587,1 127 | 126,Lisa,Second,22,0.2051120660924029,1 128 | 127,Ruth,Second,23,0.3831243915509889,1 129 | 128,Beatriz,Second,22,0.3124600441888724,1 130 | 129,Wilma,Second,25,0.09463966222429995,1 131 | 130,Alyssa,Second,30,0.14358200116549735,1 132 | 131,Samira,Second,24,0.23312851820496788,1 133 | 132,Danielle,Second,26,0.21597688241527108,1 134 | 133,Rachal,Second,30,0.06783799651820446,1 135 | 134,Candice,Second,30,0.23355654022467168,1 136 | 135,Yvonne,Second,30,0.18922628195182462,1 137 | 136,Lorraine,Second,19,0.17728153493121623,1 138 | 137,Minnie,Second,25,0.3492333981148308,1 139 | 138,Katherine,Second,27,0.18958710343574606,1 140 | 139,Teresa,Second,24,0.1262278337326905,1 141 | 140,Suzanne,Second,21,0.13989189213931089,1 142 | 141,Maria,Second,30,-0.0015909873319689682,1 143 | 142,Laura,Second,21,0.2334011707479643,1 144 | 143,Georgette,Second,20,0.35302209254378053,1 145 | 144,Marcia,Second,19,0.5226521787068581,1 146 | 145,Susie,Second,21,0.36329810589236516,1 147 | 146,Deanna,Second,18,0.2708931557358139,1 148 | 147,Harriet,Second,22,0.225303105712023,1 149 | 148,Laverne,Second,23,0.2507592828697621,1 150 | 149,Evelyn,Second,27,0.12194354809915783,1 151 | 150,Jessica,Second,22,0.2058315931315564,1 152 | 151,Mary,Second,28,0.007210535525405459,1 153 | 152,Maurine,Second,23,-0.019741463652366897,1 154 | 153,Millicent,Second,21,0.17019239577663078,1 155 | 154,Twila,Second,22,0.15963143828374393,1 156 | 155,Kim,Second,27,0.2916459521214245,1 157 | 156,Elida,Second,27,0.24632515194807486,1 158 | 157,Robin,Second,21,0.32019531307941457,1 159 | 158,Katrina,Second,23,0.1786902612289475,1 160 | 159,Christina,Second,23,0.25749564959441507,1 161 | 160,Ashley,Second,21,0.18779526658565132,1 162 | 161,Katherine,Second,19,0.1342275752281948,1 163 | 162,Stella,Second,18,0.09943435495786426,1 164 | 163,Lillian,Second,29,0.3417119938200113,1 165 | 164,Megan,Second,26,0.06784530180928128,1 166 | 165,Martha,Second,30,0.21525819802859544,1 167 | 166,Tamra,Second,22,0.021557032672541637,1 168 | 167,Barbara,Second,23,0.13108084474542808,1 169 | 168,Helen,Second,23,0.11891207621232601,1 170 | 169,Delores,Second,20,0.33798670232487327,1 171 | 170,Cassandra,Second,19,0.10828015460027368,1 172 | 171,Shawn,Second,20,0.22800169919130298,1 173 | 172,Diane,Second,23,0.3677471825878867,1 174 | 173,Lena,Second,26,0.11473052278419268,1 175 | 174,Renee,Second,29,0.3483514665374877,1 176 | 175,Linda,Second,20,0.23109904142017587,1 177 | 176,Brooke,Second,21,0.1584449775181626,1 178 | 177,Angela,Second,24,0.11253400451571867,1 179 | 178,Bridget,Second,21,0.21473137093091682,1 180 | 179,Betty,Second,24,-0.04756645107718965,1 181 | 180,Lola,Second,30,0.06458587795355406,1 182 | 181,Debra,Second,21,0.19182946688755703,1 183 | 182,Debra,Second,29,0.280133485322458,1 184 | 183,Diana,Second,19,0.11740521576704997,1 185 | 184,Mary,Second,28,0.35378213442810325,1 186 | 185,Mable,Second,23,0.21679690354764067,1 187 | 186,Jeanne,Second,30,0.18813859200852834,1 188 | 187,Sharon,Second,26,0.444073904719296,1 189 | 188,Lucia,Second,18,0.030800102024488796,1 190 | 189,Kimberly,Second,24,0.08286181591434122,1 191 | 190,Crystal,Second,19,0.3617175612493313,1 192 | 191,Esther,Second,25,0.32482186789216494,1 193 | 192,Della,Second,28,0.19985402582295458,1 194 | 193,Joy,Second,20,0.4014913213450044,1 195 | 194,Benita,Second,24,0.3715649470597647,1 196 | 195,Doris,Second,18,0.2321835805444617,1 197 | 196,Francisca,Second,27,0.28965269009393924,1 198 | 197,Nia,Second,20,0.22308344443480868,1 199 | 198,Christina,Second,29,0.06911288315541994,1 200 | 199,Marta,Second,26,0.2913960636127055,1 201 | 200,Julia,Second,25,0.20249291104486722,1 202 | -------------------------------------------------------------------------------- /SimData/SixthDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,Sixth,23,0.5627187141917855,0 3 | 2,Billie,Sixth,22,0.6011122996634237,0 4 | 3,Robert,Sixth,20,0.37624096163859233,0 5 | 4,Don,Sixth,27,0.5229607643652396,0 6 | 5,Joseph,Sixth,21,0.44213240419590205,0 7 | 6,James,Sixth,25,0.4571030819121562,0 8 | 7,Delbert,Sixth,26,0.5381738662221578,0 9 | 8,Gary,Sixth,24,0.45091858446320987,0 10 | 9,Scott,Sixth,27,0.5559681620837437,0 11 | 10,Steve,Sixth,27,0.5525724860353819,0 12 | 11,Frank,Sixth,18,0.34157597865694245,0 13 | 12,Eric,Sixth,23,0.512687736538734,0 14 | 13,Wendell,Sixth,27,0.4933996370284516,0 15 | 14,James,Sixth,21,0.4517822116966478,0 16 | 15,Paul,Sixth,20,0.28884645180163937,0 17 | 16,Juan,Sixth,26,0.511309398445967,0 18 | 17,Walter,Sixth,30,0.48763740268097405,0 19 | 18,Richard,Sixth,23,0.5248170458806685,0 20 | 19,Felix,Sixth,21,0.6719892697762015,0 21 | 20,Andre,Sixth,21,0.44492116509875906,0 22 | 21,Jeffrey,Sixth,26,0.4035001434778803,0 23 | 22,Dave,Sixth,21,0.5538101525320848,0 24 | 23,James,Sixth,29,0.36994167702023284,0 25 | 24,Lemuel,Sixth,27,0.29449460162573504,0 26 | 25,Tommy,Sixth,23,0.7125960371855942,0 27 | 26,Carlos,Sixth,22,0.560689934295333,0 28 | 27,Willie,Sixth,19,0.44267903786725915,0 29 | 28,Joseph,Sixth,19,0.46734362611498603,0 30 | 29,Everette,Sixth,25,0.6954712644704699,0 31 | 30,Marvin,Sixth,28,0.5338449267566893,0 32 | 31,Javier,Sixth,30,0.5053801595683928,0 33 | 32,Henry,Sixth,27,0.4386438165903696,0 34 | 33,Danial,Sixth,19,0.44395374025103196,0 35 | 34,Nathan,Sixth,28,0.6104137051112386,0 36 | 35,Ted,Sixth,29,0.4387538730184034,0 37 | 36,Terry,Sixth,29,0.5828116958700524,0 38 | 37,Steve,Sixth,28,0.4883025967736782,0 39 | 38,Thomas,Sixth,24,0.5708048197461699,0 40 | 39,Roger,Sixth,24,0.5903570754206436,0 41 | 40,Mohammed,Sixth,26,0.5710369057808238,0 42 | 41,Willie,Sixth,19,0.3545339060940288,0 43 | 42,Kirk,Sixth,20,0.5931082193103651,0 44 | 43,Michael,Sixth,20,0.4278974215619132,0 45 | 44,Michael,Sixth,24,0.7627312850675907,0 46 | 45,Eric,Sixth,20,0.36098658033665276,0 47 | 46,Clarence,Sixth,19,0.2723403918403283,0 48 | 47,Carlton,Sixth,23,0.48309245449383376,0 49 | 48,Luis,Sixth,18,0.5978956173958363,0 50 | 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71,Salvatore,Sixth,30,0.5009923942795725,0 73 | 72,Jack,Sixth,23,0.453988848810899,0 74 | 73,Jose,Sixth,28,0.4821673612287138,0 75 | 74,John,Sixth,24,0.47226569405417884,0 76 | 75,Richard,Sixth,18,0.6197608040669679,0 77 | 76,Alan,Sixth,24,0.5335412407017036,0 78 | 77,Edward,Sixth,30,0.5938522409655508,0 79 | 78,Kenny,Sixth,30,0.41093795718540826,0 80 | 79,Ian,Sixth,24,0.4959098187308255,0 81 | 80,Brian,Sixth,29,0.5308543203711171,0 82 | 81,Chad,Sixth,24,0.3173498201434641,0 83 | 82,Lowell,Sixth,19,0.42484959326038846,0 84 | 83,Jonathon,Sixth,24,0.4720788961850801,0 85 | 84,Chester,Sixth,25,0.5393488384028237,0 86 | 85,Harold,Sixth,28,0.46636818275264025,0 87 | 86,Michael,Sixth,28,0.564364490050406,0 88 | 87,James,Sixth,23,0.6633255597766218,0 89 | 88,Charles,Sixth,18,0.6406938401660549,0 90 | 89,Arthur,Sixth,21,0.6547226491561557,0 91 | 90,Robert,Sixth,21,0.46504208030379424,0 92 | 91,William,Sixth,30,0.5665519641804087,0 93 | 92,Raymond,Sixth,25,0.6234046756685127,0 94 | 93,Michael,Sixth,18,0.4054389794121322,0 95 | 94,James,Sixth,27,0.4832817739075767,0 96 | 95,James,Sixth,22,0.4623935581996443,0 97 | 96,Jayson,Sixth,30,0.6183163685997498,0 98 | 97,Don,Sixth,24,0.5733711927951894,0 99 | 98,Joseph,Sixth,29,0.4873175435671538,0 100 | 99,Paul,Sixth,22,0.688110681439846,0 101 | 100,Roberto,Sixth,27,0.434629408779397,0 102 | 101,Vanessa,Sixth,25,0.5111038562077044,1 103 | 102,Alice,Sixth,23,0.4972015920269251,1 104 | 103,Josephine,Sixth,27,0.6222947266717184,1 105 | 104,Annie,Sixth,28,0.4729998858439837,1 106 | 105,Veronica,Sixth,18,0.49929018827319105,1 107 | 106,Elizabeth,Sixth,23,0.3895004576945725,1 108 | 107,Marie,Sixth,29,0.4726102218311762,1 109 | 108,Barbara,Sixth,20,0.46548009145996394,1 110 | 109,Donna,Sixth,29,0.4993503685979966,1 111 | 110,Shannon,Sixth,29,0.536872124109889,1 112 | 111,Miranda,Sixth,28,0.5454813912263198,1 113 | 112,Brianna,Sixth,22,0.45206577509430196,1 114 | 113,Willie,Sixth,25,0.39099589041022575,1 115 | 114,Rachel,Sixth,21,0.49833795888701615,1 116 | 115,Maria,Sixth,21,0.6115318473116368,1 117 | 116,Barbara,Sixth,23,0.3798873826666749,1 118 | 117,Mattie,Sixth,26,0.6024999519165927,1 119 | 118,Barbara,Sixth,22,0.5365787533599118,1 120 | 119,Nicole,Sixth,22,0.3719803042196256,1 121 | 120,Kimberly,Sixth,25,0.3900251680666901,1 122 | 121,Maya,Sixth,27,0.5012211361474402,1 123 | 122,Tammy,Sixth,21,0.5900732351352194,1 124 | 123,Marilyn,Sixth,24,0.5111264805624623,1 125 | 124,Karen,Sixth,26,0.5719529873473642,1 126 | 125,Sherri,Sixth,20,0.5344687407956776,1 127 | 126,Lisa,Sixth,22,0.4285293196734672,1 128 | 127,Ruth,Sixth,23,0.4141276409539065,1 129 | 128,Beatriz,Sixth,22,0.580891276391413,1 130 | 129,Wilma,Sixth,25,0.41292421225187,1 131 | 130,Alyssa,Sixth,30,0.5691490519310829,1 132 | 131,Samira,Sixth,24,0.5983022594122839,1 133 | 132,Danielle,Sixth,26,0.6119405745912091,1 134 | 133,Rachal,Sixth,30,0.4502686799797943,1 135 | 134,Candice,Sixth,30,0.4958386248306951,1 136 | 135,Yvonne,Sixth,30,0.5707502520010675,1 137 | 136,Lorraine,Sixth,19,0.4435393854940667,1 138 | 137,Minnie,Sixth,25,0.4677632036651698,1 139 | 138,Katherine,Sixth,27,0.40429724258458183,1 140 | 139,Teresa,Sixth,24,0.6421220921833651,1 141 | 140,Suzanne,Sixth,21,0.542283761995531,1 142 | 141,Maria,Sixth,30,0.39329161255034334,1 143 | 142,Laura,Sixth,21,0.5375873055372921,1 144 | 143,Georgette,Sixth,20,0.392442361268742,1 145 | 144,Marcia,Sixth,19,0.5829517088661433,1 146 | 145,Susie,Sixth,21,0.48000016154717273,1 147 | 146,Deanna,Sixth,18,0.4949746972751772,1 148 | 147,Harriet,Sixth,22,0.46516205380374437,1 149 | 148,Laverne,Sixth,23,0.21852240343463564,1 150 | 149,Evelyn,Sixth,27,0.5256522259273075,1 151 | 150,Jessica,Sixth,22,0.46267561132136725,1 152 | 151,Mary,Sixth,28,0.5971399636524906,1 153 | 152,Maurine,Sixth,23,0.3862647280799457,1 154 | 153,Millicent,Sixth,21,0.41539557943221994,1 155 | 154,Twila,Sixth,22,0.4434321643778448,1 156 | 155,Kim,Sixth,27,0.5783197667230191,1 157 | 156,Elida,Sixth,27,0.4595253229609275,1 158 | 157,Robin,Sixth,21,0.40689434554939796,1 159 | 158,Katrina,Sixth,23,0.574720890916179,1 160 | 159,Christina,Sixth,23,0.44716957475220676,1 161 | 160,Ashley,Sixth,21,0.36587775373073034,1 162 | 161,Katherine,Sixth,19,0.5443646468409017,1 163 | 162,Stella,Sixth,18,0.38119081334213684,1 164 | 163,Lillian,Sixth,29,0.5827037820459395,1 165 | 164,Megan,Sixth,26,0.6712950290615973,1 166 | 165,Martha,Sixth,30,0.5999854536629398,1 167 | 166,Tamra,Sixth,22,0.5426967026078454,1 168 | 167,Barbara,Sixth,23,0.5266036117738924,1 169 | 168,Helen,Sixth,23,0.38274411414019605,1 170 | 169,Delores,Sixth,20,0.5125524640036196,1 171 | 170,Cassandra,Sixth,19,0.5445426304713614,1 172 | 171,Shawn,Sixth,20,0.5415660559256122,1 173 | 172,Diane,Sixth,23,0.31139991061482986,1 174 | 173,Lena,Sixth,26,0.4249519659560316,1 175 | 174,Renee,Sixth,29,0.4841828832501326,1 176 | 175,Linda,Sixth,20,0.4267961009247867,1 177 | 176,Brooke,Sixth,21,0.566598184431002,1 178 | 177,Angela,Sixth,24,0.48775862669517905,1 179 | 178,Bridget,Sixth,21,0.4569162017466531,1 180 | 179,Betty,Sixth,24,0.534980963105169,1 181 | 180,Lola,Sixth,30,0.5152307543228434,1 182 | 181,Debra,Sixth,21,0.40830678550869137,1 183 | 182,Debra,Sixth,29,0.6112997110674216,1 184 | 183,Diana,Sixth,19,0.41919925141276265,1 185 | 184,Mary,Sixth,28,0.5751100677634319,1 186 | 185,Mable,Sixth,23,0.49041574023450657,1 187 | 186,Jeanne,Sixth,30,0.3049416329938862,1 188 | 187,Sharon,Sixth,26,0.4326362856392755,1 189 | 188,Lucia,Sixth,18,0.38953007913175974,1 190 | 189,Kimberly,Sixth,24,0.5165075984515924,1 191 | 190,Crystal,Sixth,19,0.37998579451393755,1 192 | 191,Esther,Sixth,25,0.35987570615368214,1 193 | 192,Della,Sixth,28,0.5487373450086704,1 194 | 193,Joy,Sixth,20,0.5711191682655571,1 195 | 194,Benita,Sixth,24,0.5812982298969092,1 196 | 195,Doris,Sixth,18,0.5366558449616664,1 197 | 196,Francisca,Sixth,27,0.4946573817954197,1 198 | 197,Nia,Sixth,20,0.48775524353357663,1 199 | 198,Christina,Sixth,29,0.49946937325955093,1 200 | 199,Marta,Sixth,26,0.42786748248659734,1 201 | 200,Julia,Sixth,25,0.40367954499254277,1 202 | -------------------------------------------------------------------------------- /SimData/ThirdDayData.csv: -------------------------------------------------------------------------------- 1 | ID,Name,Day,Age,Response,Gender 2 | 1,John,Third,23,0.40099450001692183,0 3 | 2,Billie,Third,22,0.2651030211212087,0 4 | 3,Robert,Third,20,0.3934713155214702,0 5 | 4,Don,Third,27,0.3787219484401598,0 6 | 5,Joseph,Third,21,0.2412362638754793,0 7 | 6,James,Third,25,0.18754160281591353,0 8 | 7,Delbert,Third,26,0.17759525036805815,0 9 | 8,Gary,Third,24,0.2823216654409193,0 10 | 9,Scott,Third,27,0.2715643824984445,0 11 | 10,Steve,Third,27,0.007035479172166403,0 12 | 11,Frank,Third,18,0.24131178636553074,0 13 | 12,Eric,Third,23,0.27542227505462846,0 14 | 13,Wendell,Third,27,0.12782686943106428,0 15 | 14,James,Third,21,0.06463197427610937,0 16 | 15,Paul,Third,20,0.293886077826389,0 17 | 16,Juan,Third,26,0.367367789760011,0 18 | 17,Walter,Third,30,0.14224958214557817,0 19 | 18,Richard,Third,23,0.32214838858372324,0 20 | 19,Felix,Third,21,0.23399598430215057,0 21 | 20,Andre,Third,21,0.1392840120896406,0 22 | 21,Jeffrey,Third,26,0.23206629227793835,0 23 | 22,Dave,Third,21,0.2549411491510197,0 24 | 23,James,Third,29,0.28214891447047447,0 25 | 24,Lemuel,Third,27,0.31139237907578526,0 26 | 25,Tommy,Third,23,0.2374548887378006,0 27 | 26,Carlos,Third,22,0.05930328670685148,0 28 | 27,Willie,Third,19,0.2614913493489208,0 29 | 28,Joseph,Third,19,0.4364128270365927,0 30 | 29,Everette,Third,25,0.26885848350337566,0 31 | 30,Marvin,Third,28,0.44306506201217455,0 32 | 31,Javier,Third,30,0.29939261233472636,0 33 | 32,Henry,Third,27,0.30661705166289227,0 34 | 33,Danial,Third,19,0.4747952419634716,0 35 | 34,Nathan,Third,28,0.4521864778232023,0 36 | 35,Ted,Third,29,0.41813704687207215,0 37 | 36,Terry,Third,29,0.25589377280021325,0 38 | 37,Steve,Third,28,0.22768553405418746,0 39 | 38,Thomas,Third,24,0.29952441747563846,0 40 | 39,Roger,Third,24,0.49841611020640886,0 41 | 40,Mohammed,Third,26,0.25914601720201685,0 42 | 41,Willie,Third,19,0.2736349340787063,0 43 | 42,Kirk,Third,20,0.19531973065286667,0 44 | 43,Michael,Third,20,0.35084896207265936,0 45 | 44,Michael,Third,24,0.38676074922947157,0 46 | 45,Eric,Third,20,0.27092578662801714,0 47 | 46,Clarence,Third,19,0.01098110777362965,0 48 | 47,Carlton,Third,23,0.22676598774260986,0 49 | 48,Luis,Third,18,0.32233442970067316,0 50 | 49,John,Third,30,0.43120859365897035,0 51 | 50,Charles,Third,23,0.5686955105331662,0 52 | 51,Larry,Third,30,0.4807976934906111,0 53 | 52,Kennith,Third,22,0.23056466292325006,0 54 | 53,John,Third,24,0.17932957832978297,0 55 | 54,David,Third,20,0.365902979653449,0 56 | 55,Taylor,Third,20,0.20784711602511152,0 57 | 56,Michael,Third,23,0.5203830691599985,0 58 | 57,Wesley,Third,21,0.29417339417647476,0 59 | 58,Jack,Third,20,0.4495707486803632,0 60 | 59,Drew,Third,22,0.3513554778490964,0 61 | 60,Gary,Third,20,0.18875735935160126,0 62 | 61,Robert,Third,26,0.08055405914894032,0 63 | 62,Charles,Third,29,0.25821838621774185,0 64 | 63,Lawrence,Third,24,0.3061542830576999,0 65 | 64,Billy,Third,25,0.31737566559001446,0 66 | 65,Travis,Third,19,0.2812295411119703,0 67 | 66,Kelly,Third,21,0.2845514095642269,0 68 | 67,Johnnie,Third,23,0.2882679023777756,0 69 | 68,Kenneth,Third,25,0.4221732276492527,0 70 | 69,John,Third,19,0.2741571309929128,0 71 | 70,Tony,Third,22,0.3501923990841354,0 72 | 71,Salvatore,Third,30,0.433984295684314,0 73 | 72,Jack,Third,23,0.403261423171382,0 74 | 73,Jose,Third,28,0.2372826486959767,0 75 | 74,John,Third,24,0.33605176895219085,0 76 | 75,Richard,Third,18,0.3229951010481258,0 77 | 76,Alan,Third,24,0.3398330676915337,0 78 | 77,Edward,Third,30,0.22263673999145328,0 79 | 78,Kenny,Third,30,0.45900270104494323,0 80 | 79,Ian,Third,24,0.31425691245324106,0 81 | 80,Brian,Third,29,0.33125333092310427,0 82 | 81,Chad,Third,24,0.1426084021342257,0 83 | 82,Lowell,Third,19,0.27615080684687426,0 84 | 83,Jonathon,Third,24,0.2803759992731195,0 85 | 84,Chester,Third,25,0.2397454114410601,0 86 | 85,Harold,Third,28,0.22858181066981265,0 87 | 86,Michael,Third,28,0.4489393220007064,0 88 | 87,James,Third,23,0.19639637129859414,0 89 | 88,Charles,Third,18,0.3212921041739039,0 90 | 89,Arthur,Third,21,0.23668318624177367,0 91 | 90,Robert,Third,21,0.3577155627158181,0 92 | 91,William,Third,30,0.39472227149384365,0 93 | 92,Raymond,Third,25,0.277671078544264,0 94 | 93,Michael,Third,18,0.26389649494785766,0 95 | 94,James,Third,27,0.13507528808468738,0 96 | 95,James,Third,22,0.2650233622366795,0 97 | 96,Jayson,Third,30,0.10328605742033237,0 98 | 97,Don,Third,24,0.3897570972467946,0 99 | 98,Joseph,Third,29,0.34675495780308835,0 100 | 99,Paul,Third,22,0.12196937996826587,0 101 | 100,Roberto,Third,27,0.3206500804901801,0 102 | 101,Vanessa,Third,25,0.2982698261263043,1 103 | 102,Alice,Third,23,0.163143512516671,1 104 | 103,Josephine,Third,27,0.2546435281845215,1 105 | 104,Annie,Third,28,0.1626793086510125,1 106 | 105,Veronica,Third,18,0.15894234898808557,1 107 | 106,Elizabeth,Third,23,0.13821001205483563,1 108 | 107,Marie,Third,29,0.4296772701722621,1 109 | 108,Barbara,Third,20,0.26983532525703535,1 110 | 109,Donna,Third,29,0.2655069673292091,1 111 | 110,Shannon,Third,29,0.40413085568694884,1 112 | 111,Miranda,Third,28,0.4800537578490202,1 113 | 112,Brianna,Third,22,0.337140978273721,1 114 | 113,Willie,Third,25,0.42419288301190494,1 115 | 114,Rachel,Third,21,0.48599986732070316,1 116 | 115,Maria,Third,21,0.3629540970920129,1 117 | 116,Barbara,Third,23,0.16852493317252487,1 118 | 117,Mattie,Third,26,0.23676172304585183,1 119 | 118,Barbara,Third,22,0.06152609938210449,1 120 | 119,Nicole,Third,22,0.3503871294979333,1 121 | 120,Kimberly,Third,25,0.2007687858885483,1 122 | 121,Maya,Third,27,0.10781467853414786,1 123 | 122,Tammy,Third,21,0.31248710828775866,1 124 | 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-------------------------------------------------------------------------------- 1 | X,Y 2 | 0.7964691855978616,0.8315513738418383 3 | 0.38613933495037944,0.8318275870968661 4 | 0.3268514535642031,0.9344009585513211 5 | 0.6513147690828912,1.1494317940777896 6 | 0.819468969785563,1.0244553248606352 7 | 0.5231064601244609,0.9110235106775828 8 | 1.0807641983846155,1.0224433825702215 9 | 0.7848297385848633,0.6229589138531781 10 | 0.5809319014843609,0.661788655622314 11 | 0.4921175181941505,0.5282632308789557 12 | 0.4431780161508694,0.5937140463888293 13 | 0.8290497073840416,0.9309761238544878 14 | 0.5385722446796244,0.39210493994507517 15 | 0.15967789660956835,0.7337011726795282 16 | 0.4980442553304314,0.7308627633296438 17 | 0.8379954057320357,0.7936850976503063 18 | 0.28249173045349996,0.725830290295828 19 | 0.2754517561474925,0.6122612229724653 20 | -------------------------------------------------------------------------------- /SimData/paired_samples_data.csv: -------------------------------------------------------------------------------- 1 | "","ids","test","score" 2 | "1","1","Pre","29.7974300770879" 3 | "2","2","Pre","42.5776151657522" 4 | "3","3","Pre","45.047165513436" 5 | "4","4","Pre","35.2705932334014" 6 | "5","5","Pre","28.5910699449006" 7 | "6","6","Pre","31.8410138175994" 8 | "7","7","Pre","34.0124734982038" 9 | "8","8","Pre","46.4892146463403" 10 | "9","9","Pre","39.9304542558338" 11 | "10","10","Pre","33.3516886942367" 12 | "11","11","Pre","42.8572053301595" 13 | "12","12","Pre","28.3413084483925" 14 | "13","13","Pre","38.6027703773846" 15 | "14","14","Pre","45.8101417162082" 16 | "15","15","Pre","38.1011318438518" 17 | "16","16","Pre","46.8308232133477" 18 | "17","17","Pre","49.4272165186362" 19 | "18","18","Pre","33.7224732943907" 20 | "19","19","Pre","33.8048755875953" 21 | "20","20","Pre","35.0653860124681" 22 | "21","21","Pre","42.7142585724282" 23 | "22","22","Pre","44.3379183133575" 24 | 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"72","22","Post","50.3113606235816" 74 | "73","23","Post","51.2933564431331" 75 | "74","24","Post","52.4449890295368" 76 | "75","25","Post","51.7916434435655" 77 | "76","26","Post","50.7164247581178" 78 | "77","27","Post","46.6061659557517" 79 | "78","28","Post","41.4060681829761" 80 | "79","29","Post","41.8073022923197" 81 | "80","30","Post","48.1956190960739" 82 | "81","31","Post","54.294759634335" 83 | "82","32","Post","54.4767154792721" 84 | "83","33","Post","35.08191076183" 85 | "84","34","Post","41.4391049884471" 86 | "85","35","Post","53.2867412968379" 87 | "86","36","Post","40.6585197988652" 88 | "87","37","Post","37.2665270636188" 89 | "88","38","Post","49.4488029682052" 90 | "89","39","Post","55.6457987927848" 91 | "90","40","Post","55.9261047562448" 92 | "91","41","Post","48.7815702478923" 93 | "92","42","Post","49.3729900268841" 94 | "93","43","Post","49.039161029472" 95 | "94","44","Post","45.386944771065" 96 | "95","45","Post","27.6298212109051" 97 | "96","46","Post","49.9571229703613" 98 | "97","47","Post","43.7878660637685" 99 | "98","48","Post","51.2694088215512" 100 | "99","49","Post","42.5314652313058" 101 | "100","50","Post","36.9977765306218" 102 | -------------------------------------------------------------------------------- /SimData/play_data.csv: -------------------------------------------------------------------------------- 1 | ,Subject ID,First Name,Day,Age,Response Time,Gender,Miss\Col 2 | 0,1,John,Sixth,23,0.562718714,0, 3 | 1,2,Billie,Sixth,22,,0, 4 | 2,3,Robert,Sixth,20,,0, 5 | 3,4,Don,Sixth,27,0.522960764,0, 6 | 4,5,Joseph,Sixth,21,,0, 7 | 5,6,James,Sixth,25,0.457103082,0, 8 | 6,7,Delbert,Sixth,26,,0, 9 | 7,8,Gary,Sixth,24,,0, 10 | 8,9,Scott,Sixth,27,,0, 11 | 9,10,Steve,Sixth,27,,0, 12 | 10,11,Frank,Sixth,18,,0, 13 | 11,12,Eric,Sixth,23,0.512687737,0, 14 | 12,13,Wendell,Sixth,27,,0, 15 | 13,14,James,Sixth,21,0.451782212,0, 16 | 14,15,Paul,Sixth,20,,0, 17 | 15,16,Juan,Sixth,26,0.511309398,0, 18 | 16,17,Walter,Sixth,30,0.487637403,0, 19 | 17,18,Richard,Sixth,23,0.524817046,0, 20 | 18,19,Felix,Sixth,21,0.67198927,0, 21 | 19,20,Andre,Sixth,21,0.444921165,0, 22 | 20,21,Jeffrey,Sixth,26,0.403500143,0, 23 | 21,22,Dave,Sixth,21,0.553810153,0, 24 | 22,23,James,Sixth,29,0.369941677,0, 25 | 23,24,Lemuel,Sixth,27,0.294494602,0, 26 | 24,25,Tommy,Sixth,23,0.712596037,0, 27 | 25,26,Carlos,Sixth,22,0.560689934,0, 28 | 26,27,Willie,Sixth,19,0.442679038,0, 29 | 27,28,Joseph,Sixth,19,0.467343626,0, 30 | 28,29,Everette,Sixth,25,0.695471264,0, 31 | 29,30,Marvin,Sixth,28,0.533844927,0, 32 | 30,31,Javier,Sixth,30,0.50538016,0, 33 | 31,32,Henry,Sixth,27,0.438643817,0, 34 | 32,33,Danial,Sixth,19,0.44395374,0, 35 | 33,34,Nathan,Sixth,28,0.610413705,0, 36 | 34,35,Ted,Sixth,29,0.438753873,0, 37 | 35,36,Terry,Sixth,29,0.582811696,0, 38 | 36,37,Steve,Sixth,28,0.488302597,0, 39 | 37,38,Thomas,Sixth,24,0.57080482,0, 40 | 38,39,Roger,Sixth,24,0.590357075,0, 41 | 39,40,Mohammed,Sixth,26,0.571036906,0, 42 | 40,41,Willie,Sixth,19,0.354533906,0, 43 | 41,42,Kirk,Sixth,20,0.593108219,0, 44 | 42,43,Michael,Sixth,20,0.427897422,0, 45 | 43,44,Michael,Sixth,24,0.762731285,0, 46 | 44,45,Eric,Sixth,20,0.36098658,0, 47 | 45,46,Clarence,Sixth,19,0.272340392,0, 48 | 46,47,Carlton,Sixth,23,0.483092454,0, 49 | 47,48,Luis,Sixth,18,0.597895617,0, 50 | 48,49,John,Sixth,30,0.461951379,0, 51 | 49,50,Charles,Sixth,23,0.447971024,0, 52 | 50,51,Larry,Sixth,30,0.600510185,0, 53 | 51,52,Kennith,Sixth,22,0.640767304,0, 54 | 52,53,John,Sixth,24,0.316679876,0, 55 | 53,54,David,Sixth,20,0.546059195,0, 56 | 54,55,Taylor,Sixth,20,0.420246871,0, 57 | 55,56,Michael,Sixth,23,0.503040392,0, 58 | 56,57,Wesley,Sixth,21,0.525297919,0, 59 | 57,58,Jack,Sixth,20,0.445605919,0, 60 | 58,59,Drew,Sixth,22,0.496373611,0, 61 | 59,60,Gary,Sixth,20,0.447558501,0, 62 | 60,61,Robert,Sixth,26,0.433021422,0, 63 | 61,62,Charles,Sixth,29,0.482127871,0, 64 | 62,63,Lawrence,Sixth,24,0.405422175,0, 65 | 63,64,Billy,Sixth,25,0.545988751,0, 66 | 64,65,Travis,Sixth,19,0.448253713,0, 67 | 65,66,Kelly,Sixth,21,0.4947168,0, 68 | 66,67,Johnnie,Sixth,23,0.494606185,0, 69 | 67,68,Kenneth,Sixth,25,0.565283972,0, 70 | 68,69,John,Sixth,19,0.343187861,0, 71 | 69,70,Tony,Sixth,22,0.514230118,0, 72 | 70,71,Salvatore,Sixth,30,0.500992394,0, 73 | 71,72,Jack,Sixth,23,0.453988849,0, 74 | 72,73,Jose,Sixth,28,0.482167361,0, 75 | 73,74,John,Sixth,24,0.472265694,0, 76 | 74,75,Richard,Sixth,18,0.619760804,0, 77 | 75,76,Alan,Sixth,24,0.533541241,0, 78 | 76,77,Edward,Sixth,30,0.593852241,0, 79 | 77,78,Kenny,Sixth,30,0.410937957,0, 80 | 78,79,Ian,Sixth,24,0.495909819,0, 81 | 79,80,Brian,Sixth,29,0.53085432,0, 82 | 80,81,Chad,Sixth,24,0.31734982,0, 83 | 81,82,Lowell,Sixth,19,0.424849593,0, 84 | 82,83,Jonathon,Sixth,24,0.472078896,0, 85 | 83,84,Chester,Sixth,25,0.539348838,0, 86 | 84,85,Harold,Sixth,28,0.466368183,0, 87 | 85,86,Michael,Sixth,28,0.56436449,0, 88 | 86,87,James,Sixth,23,0.66332556,0, 89 | 87,88,Charles,Sixth,18,0.64069384,0, 90 | 88,89,Arthur,Sixth,21,0.654722649,0, 91 | 89,90,Robert,Sixth,21,0.46504208,0, 92 | 90,91,William,Sixth,30,0.566551964,0, 93 | 91,92,Raymond,Sixth,25,0.623404676,0, 94 | 92,93,Michael,Sixth,18,0.405438979,0, 95 | 93,94,James,Sixth,27,0.483281774,0, 96 | 94,95,James,Sixth,22,0.462393558,0, 97 | 95,96,Jayson,Sixth,30,0.618316369,0, 98 | 96,97,Don,Sixth,24,0.573371193,0, 99 | 97,98,Joseph,Sixth,29,0.487317544,0, 100 | 98,99,Paul,Sixth,22,0.688110681,0, 101 | 99,100,Roberto,Sixth,27,0.434629409,0, 102 | 100,101,Vanessa,Sixth,25,0.511103856,1, 103 | 101,102,Alice,Sixth,23,0.497201592,1, 104 | 102,103,Josephine,Sixth,27,0.622294727,1, 105 | 103,104,Annie,Sixth,28,0.472999886,1, 106 | 104,105,Veronica,Sixth,18,0.499290188,1, 107 | 105,106,Elizabeth,Sixth,23,0.389500458,1, 108 | 106,107,Marie,Sixth,29,0.472610222,1, 109 | 107,108,Barbara,Sixth,20,,1, 110 | 108,109,Donna,Sixth,29,0.499350369,1, 111 | 109,110,Shannon,Sixth,29,0.536872124,1, 112 | 110,111,Miranda,Sixth,28,0.545481391,1, 113 | 111,112,Brianna,Sixth,22,0.452065775,1, 114 | 112,113,Willie,Sixth,25,0.39099589,1, 115 | 113,114,Rachel,Sixth,21,0.498337959,1, 116 | 114,115,Maria,Sixth,21,0.611531847,1, 117 | 115,116,Barbara,Sixth,23,0.379887383,1, 118 | 116,117,Mattie,Sixth,26,0.602499952,1, 119 | 117,118,Barbara,Sixth,22,0.536578753,1, 120 | 118,119,Nicole,Sixth,22,0.371980304,1, 121 | 119,120,Kimberly,Sixth,25,0.390025168,1, 122 | 120,121,Maya,Sixth,27,0.501221136,1, 123 | 121,122,Tammy,Sixth,21,0.590073235,1, 124 | 122,123,Marilyn,Sixth,24,0.511126481,1, 125 | 123,124,Karen,Sixth,26,0.571952987,1, 126 | 124,125,Sherri,Sixth,20,0.534468741,1, 127 | 125,126,Lisa,Sixth,22,0.42852932,1, 128 | 126,127,Ruth,Sixth,23,0.414127641,1, 129 | 127,128,Beatriz,Sixth,22,0.580891276,1, 130 | 128,129,Wilma,Sixth,25,0.412924212,1, 131 | 129,130,Alyssa,Sixth,30,0.569149052,1, 132 | 130,131,Samira,Sixth,24,0.598302259,1, 133 | 131,132,Danielle,Sixth,26,0.611940575,1, 134 | 132,133,Rachal,Sixth,30,0.45026868,1, 135 | 133,134,Candice,Sixth,30,0.495838625,1, 136 | 134,135,Yvonne,Sixth,30,0.570750252,1, 137 | 135,136,Lorraine,Sixth,19,0.443539385,1, 138 | 136,137,Minnie,Sixth,25,0.467763204,1, 139 | 137,138,Katherine,Sixth,27,0.404297243,1, 140 | 138,139,Teresa,Sixth,24,0.642122092,1, 141 | 139,140,Suzanne,Sixth,21,0.542283762,1, 142 | 140,141,Maria,Sixth,30,0.393291613,1, 143 | 141,142,Laura,Sixth,21,0.537587306,1, 144 | 142,143,Georgette,Sixth,20,0.392442361,1, 145 | 143,144,Marcia,Sixth,19,0.582951709,1, 146 | 144,145,Susie,Sixth,21,0.480000162,1, 147 | 145,146,Deanna,Sixth,18,0.494974697,1, 148 | 146,147,Harriet,Sixth,22,0.465162054,1, 149 | 147,148,Laverne,Sixth,23,0.218522403,1, 150 | 148,149,Evelyn,Sixth,27,0.525652226,1, 151 | 149,150,Jessica,Sixth,22,0.462675611,1, 152 | 150,151,Mary,Sixth,28,0.597139964,1, 153 | 151,152,Maurine,Sixth,23,0.386264728,1, 154 | 152,153,Millicent,Sixth,21,0.415395579,1, 155 | 153,154,Twila,Sixth,22,0.443432164,1, 156 | 154,155,Kim,Sixth,27,0.578319767,1, 157 | 155,156,Elida,Sixth,27,0.459525323,1, 158 | 156,157,Robin,Sixth,21,0.406894346,1, 159 | 157,158,Katrina,Sixth,23,0.574720891,1, 160 | 158,159,Christina,Sixth,23,0.447169575,1, 161 | 159,160,Ashley,Sixth,21,0.365877754,1, 162 | 160,161,Katherine,Sixth,19,0.544364647,1, 163 | 161,162,Stella,Sixth,18,0.381190813,1, 164 | 162,163,Lillian,Sixth,29,0.582703782,1, 165 | 163,164,Megan,Sixth,26,0.671295029,1, 166 | 164,165,Martha,Sixth,30,0.599985454,1, 167 | 165,166,Tamra,Sixth,22,0.542696703,1, 168 | 166,167,Barbara,Sixth,23,0.526603612,1, 169 | 167,168,Helen,Sixth,23,0.382744114,1, 170 | 168,169,Delores,Sixth,20,0.512552464,1, 171 | 169,170,Cassandra,Sixth,19,0.54454263,1, 172 | 170,171,Shawn,Sixth,20,0.541566056,1, 173 | 171,172,Diane,Sixth,23,0.311399911,1, 174 | 172,173,Lena,Sixth,26,0.424951966,1, 175 | 173,174,Renee,Sixth,29,0.484182883,1, 176 | 174,175,Linda,Sixth,20,0.426796101,1, 177 | 175,176,Brooke,Sixth,21,0.566598184,1, 178 | 176,177,Angela,Sixth,24,0.487758627,1, 179 | 177,178,Bridget,Sixth,21,0.456916202,1, 180 | 178,179,Betty,Sixth,24,0.534980963,1, 181 | 179,180,Lola,Sixth,30,0.515230754,1, 182 | 180,181,Debra,Sixth,21,0.408306786,1, 183 | 181,182,Debra,Sixth,29,0.611299711,1, 184 | 182,183,Diana,Sixth,19,0.419199251,1, 185 | 183,184,Mary,Sixth,28,0.575110068,1, 186 | 184,185,Mable,Sixth,23,0.49041574,1, 187 | 185,186,Jeanne,Sixth,30,0.304941633,1, 188 | 186,187,Sharon,Sixth,26,,1, 189 | 187,188,Lucia,Sixth,18,0.389530079,1, 190 | 188,189,Kimberly,Sixth,24,0.516507598,1, 191 | 189,190,Crystal,Sixth,19,0.379985795,1, 192 | 190,191,Esther,Sixth,25,0.359875706,1, 193 | 191,192,Della,Sixth,28,0.548737345,1, 194 | 192,193,Joy,Sixth,20,,1, 195 | 193,194,Benita,Sixth,24,0.58129823,1, 196 | 194,195,Doris,Sixth,18,0.536655845,1, 197 | 195,196,Francisca,Sixth,27,0.494657382,1, 198 | 196,197,Nia,Sixth,20,0.487755244,1, 199 | 197,198,Christina,Sixth,29,,1, 200 | 198,199,Marta,Sixth,26,0.427867482,1, 201 | 199,200,Julia,Sixth,25,0.403679545,1, 202 | -------------------------------------------------------------------------------- /SimData/play_data.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/play_data.xlsx -------------------------------------------------------------------------------- /SimData/play_data2.dta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/play_data2.dta -------------------------------------------------------------------------------- /SimData/python_absolute_value.csv: -------------------------------------------------------------------------------- 1 | D,F,C,L,K 2 | -1.213999987,0.243000001,-0.145400003,-1.414999962,0.611999989 3 | -1.353999972,-0.25999999,0.218099996,1.383999944,0.559000015 4 | -1.569000006,-0.277999997,0.315699995,1.388000011,0.573000014 5 | -1.947999954,0.296999991,0.393999994,1.549999952,0.56400001 6 | -2.265000105,0.310000002,0.35589999,1.802000046,0.574000001 7 | 2.730999947,0.321999997,0.359299988,1.925999999,0.711000025 8 | -3.025000095,0.335000008,0.402500004,1.963999987,0.776000023 9 | 3.562000036,0.349999994,0.396100014,2.115999937,0.827000022 10 | 3.979000092,0.361000001,0.382200003,2.434999943,0.800000012 11 | -------------------------------------------------------------------------------- /SimData/skiprow.csv: -------------------------------------------------------------------------------- 1 | 2018-11-24 2 | Day Six Data 3 | Erik 4 | ,ID,Name,Day,Age,Response,Gender 5 | 0,1,John,Sixth,23,0.5627187141917855,0 6 | 1,2,Billie,Sixth,22,Not Available,0 7 | 2,3,Robert,Sixth,20,Not Available,0 8 | 3,4,Don,Sixth,27,0.5229607643652396,0 9 | 4,5,Joseph,Sixth,21,Not Available,0 10 | 5,6,James,Sixth,25,0.4571030819121562,0 11 | 6,7,Delbert,Sixth,26,Not Available,0 12 | 7,8,Gary,Sixth,24,Not Available,0 13 | 8,9,Scott,Sixth,27,Not Available,0 14 | 9,10,Steve,Sixth,27,Not Available,0 15 | 10,11,Frank,Sixth,18,Not Available,0 16 | 11,12,Eric,Sixth,23,0.512687736538734,0 17 | 12,13,Wendell,Sixth,27,Not Available,0 18 | 13,14,James,Sixth,21,0.4517822116966478,0 19 | 14,15,Paul,Sixth,20,Not Available,0 20 | 15,16,Juan,Sixth,26,0.511309398445967,0 21 | 16,17,Walter,Sixth,30,0.4876374026809741,0 22 | 17,18,Richard,Sixth,23,0.5248170458806685,0 23 | 18,19,Felix,Sixth,21,0.6719892697762015,0 24 | 19,20,Andre,Sixth,21,0.4449211650987591,0 25 | 20,21,Jeffrey,Sixth,26,0.4035001434778803,0 26 | 21,22,Dave,Sixth,21,0.5538101525320848,0 27 | 22,23,James,Sixth,29,0.3699416770202328,0 28 | 23,24,Lemuel,Sixth,27,0.29449460162573504,0 29 | 24,25,Tommy,Sixth,23,0.7125960371855942,0 30 | 25,26,Carlos,Sixth,22,0.560689934295333,0 31 | 26,27,Willie,Sixth,19,0.4426790378672592,0 32 | 27,28,Joseph,Sixth,19,0.467343626114986,0 33 | 28,29,Everette,Sixth,25,0.6954712644704699,0 34 | 29,30,Marvin,Sixth,28,0.5338449267566893,0 35 | 30,31,Javier,Sixth,30,0.5053801595683928,0 36 | 31,32,Henry,Sixth,27,0.4386438165903696,0 37 | 32,33,Danial,Sixth,19,0.443953740251032,0 38 | 33,34,Nathan,Sixth,28,0.6104137051112386,0 39 | 34,35,Ted,Sixth,29,0.4387538730184034,0 40 | 35,36,Terry,Sixth,29,0.5828116958700524,0 41 | 36,37,Steve,Sixth,28,0.4883025967736782,0 42 | 37,38,Thomas,Sixth,24,0.5708048197461699,0 43 | 38,39,Roger,Sixth,24,0.5903570754206436,0 44 | 39,40,Mohammed,Sixth,26,0.5710369057808238,0 45 | 40,41,Willie,Sixth,19,0.3545339060940288,0 46 | 41,42,Kirk,Sixth,20,0.5931082193103651,0 47 | 42,43,Michael,Sixth,20,0.4278974215619132,0 48 | 43,44,Michael,Sixth,24,0.7627312850675907,0 49 | 44,45,Eric,Sixth,20,0.3609865803366528,0 50 | 45,46,Clarence,Sixth,19,0.2723403918403283,0 51 | 46,47,Carlton,Sixth,23,0.4830924544938338,0 52 | 47,48,Luis,Sixth,18,0.5978956173958363,0 53 | 48,49,John,Sixth,30,0.4619513788658993,0 54 | 49,50,Charles,Sixth,23,0.4479710238937036,0 55 | 50,51,Larry,Sixth,30,0.6005101847929761,0 56 | 51,52,Kennith,Sixth,22,0.6407673041409321,0 57 | 52,53,John,Sixth,24,0.3166798757885672,0 58 | 53,54,David,Sixth,20,0.5460591953743089,0 59 | 54,55,Taylor,Sixth,20,0.4202468709201124,0 60 | 55,56,Michael,Sixth,23,0.503040391617558,0 61 | 56,57,Wesley,Sixth,21,0.5252979188424935,0 62 | 57,58,Jack,Sixth,20,0.4456059188027668,0 63 | 58,59,Drew,Sixth,22,0.4963736106787016,0 64 | 59,60,Gary,Sixth,20,0.4475585006721042,0 65 | 60,61,Robert,Sixth,26,0.433021422000792,0 66 | 61,62,Charles,Sixth,29,0.4821278711793452,0 67 | 62,63,Lawrence,Sixth,24,0.4054221747322013,0 68 | 63,64,Billy,Sixth,25,0.5459887509348277,0 69 | 64,65,Travis,Sixth,19,0.4482537127438954,0 70 | 65,66,Kelly,Sixth,21,0.4947167997604914,0 71 | 66,67,Johnnie,Sixth,23,0.4946061853363077,0 72 | 67,68,Kenneth,Sixth,25,0.5652839716526309,0 73 | 68,69,John,Sixth,19,0.34318786073239826,0 74 | 69,70,Tony,Sixth,22,0.5142301178099664,0 75 | 70,71,Salvatore,Sixth,30,0.5009923942795725,0 76 | 71,72,Jack,Sixth,23,0.45398884881089896,0 77 | 72,73,Jose,Sixth,28,0.4821673612287138,0 78 | 73,74,John,Sixth,24,0.4722656940541789,0 79 | 74,75,Richard,Sixth,18,0.6197608040669679,0 80 | 75,76,Alan,Sixth,24,0.5335412407017036,0 81 | 76,77,Edward,Sixth,30,0.5938522409655508,0 82 | 77,78,Kenny,Sixth,30,0.41093795718540826,0 83 | 78,79,Ian,Sixth,24,0.4959098187308255,0 84 | 79,80,Brian,Sixth,29,0.5308543203711171,0 85 | 80,81,Chad,Sixth,24,0.3173498201434641,0 86 | 81,82,Lowell,Sixth,19,0.4248495932603885,0 87 | 82,83,Jonathon,Sixth,24,0.4720788961850801,0 88 | 83,84,Chester,Sixth,25,0.5393488384028237,0 89 | 84,85,Harold,Sixth,28,0.4663681827526402,0 90 | 85,86,Michael,Sixth,28,0.564364490050406,0 91 | 86,87,James,Sixth,23,0.6633255597766218,0 92 | 87,88,Charles,Sixth,18,0.6406938401660549,0 93 | 88,89,Arthur,Sixth,21,0.6547226491561557,0 94 | 89,90,Robert,Sixth,21,0.4650420803037942,0 95 | 90,91,William,Sixth,30,0.5665519641804087,0 96 | 91,92,Raymond,Sixth,25,0.6234046756685127,0 97 | 92,93,Michael,Sixth,18,0.4054389794121322,0 98 | 93,94,James,Sixth,27,0.4832817739075767,0 99 | 94,95,James,Sixth,22,0.4623935581996443,0 100 | 95,96,Jayson,Sixth,30,0.6183163685997498,0 101 | 96,97,Don,Sixth,24,0.5733711927951894,0 102 | 97,98,Joseph,Sixth,29,0.4873175435671538,0 103 | 98,99,Paul,Sixth,22,0.688110681439846,0 104 | 99,100,Roberto,Sixth,27,0.434629408779397,0 105 | 100,101,Vanessa,Sixth,25,0.5111038562077044,1 106 | 101,102,Alice,Sixth,23,0.4972015920269251,1 107 | 102,103,Josephine,Sixth,27,0.6222947266717184,1 108 | 103,104,Annie,Sixth,28,0.4729998858439837,1 109 | 104,105,Veronica,Sixth,18,0.4992901882731911,1 110 | 105,106,Elizabeth,Sixth,23,0.3895004576945725,1 111 | 106,107,Marie,Sixth,29,0.4726102218311762,1 112 | 107,108,Barbara,Sixth,20,0.465480091459964,1 113 | 108,109,Donna,Sixth,29,0.4993503685979966,1 114 | 109,110,Shannon,Sixth,29,0.5368721241098889,1 115 | 110,111,Miranda,Sixth,28,0.5454813912263198,1 116 | 111,112,Brianna,Sixth,22,0.452065775094302,1 117 | 112,113,Willie,Sixth,25,0.39099589041022575,1 118 | 113,114,Rachel,Sixth,21,0.4983379588870162,1 119 | 114,115,Maria,Sixth,21,0.6115318473116368,1 120 | 115,116,Barbara,Sixth,23,0.3798873826666749,1 121 | 116,117,Mattie,Sixth,26,0.6024999519165927,1 122 | 117,118,Barbara,Sixth,22,0.5365787533599118,1 123 | 118,119,Nicole,Sixth,22,0.3719803042196256,1 124 | 119,120,Kimberly,Sixth,25,0.3900251680666901,1 125 | 120,121,Maya,Sixth,27,0.5012211361474402,1 126 | 121,122,Tammy,Sixth,21,0.5900732351352194,1 127 | 122,123,Marilyn,Sixth,24,0.5111264805624623,1 128 | 123,124,Karen,Sixth,26,0.5719529873473642,1 129 | 124,125,Sherri,Sixth,20,0.5344687407956776,1 130 | 125,126,Lisa,Sixth,22,0.4285293196734672,1 131 | 126,127,Ruth,Sixth,23,0.4141276409539065,1 132 | 127,128,Beatriz,Sixth,22,0.580891276391413,1 133 | 128,129,Wilma,Sixth,25,0.41292421225187,1 134 | 129,130,Alyssa,Sixth,30,0.5691490519310829,1 135 | 130,131,Samira,Sixth,24,0.5983022594122839,1 136 | 131,132,Danielle,Sixth,26,0.6119405745912091,1 137 | 132,133,Rachal,Sixth,30,0.4502686799797943,1 138 | 133,134,Candice,Sixth,30,0.4958386248306951,1 139 | 134,135,Yvonne,Sixth,30,0.5707502520010675,1 140 | 135,136,Lorraine,Sixth,19,0.4435393854940667,1 141 | 136,137,Minnie,Sixth,25,0.4677632036651698,1 142 | 137,138,Katherine,Sixth,27,0.4042972425845818,1 143 | 138,139,Teresa,Sixth,24,0.6421220921833651,1 144 | 139,140,Suzanne,Sixth,21,0.5422837619955311,1 145 | 140,141,Maria,Sixth,30,0.39329161255034334,1 146 | 141,142,Laura,Sixth,21,0.5375873055372921,1 147 | 142,143,Georgette,Sixth,20,0.392442361268742,1 148 | 143,144,Marcia,Sixth,19,0.5829517088661433,1 149 | 144,145,Susie,Sixth,21,0.4800001615471727,1 150 | 145,146,Deanna,Sixth,18,0.4949746972751772,1 151 | 146,147,Harriet,Sixth,22,0.4651620538037444,1 152 | 147,148,Laverne,Sixth,23,0.21852240343463564,1 153 | 148,149,Evelyn,Sixth,27,0.5256522259273075,1 154 | 149,150,Jessica,Sixth,22,0.4626756113213673,1 155 | 150,151,Mary,Sixth,28,0.5971399636524906,1 156 | 151,152,Maurine,Sixth,23,0.3862647280799457,1 157 | 152,153,Millicent,Sixth,21,0.41539557943222,1 158 | 153,154,Twila,Sixth,22,0.4434321643778448,1 159 | 154,155,Kim,Sixth,27,0.5783197667230191,1 160 | 155,156,Elida,Sixth,27,0.4595253229609275,1 161 | 156,157,Robin,Sixth,21,0.406894345549398,1 162 | 157,158,Katrina,Sixth,23,0.574720890916179,1 163 | 158,159,Christina,Sixth,23,0.4471695747522068,1 164 | 159,160,Ashley,Sixth,21,0.3658777537307304,1 165 | 160,161,Katherine,Sixth,19,0.5443646468409017,1 166 | 161,162,Stella,Sixth,18,0.3811908133421368,1 167 | 162,163,Lillian,Sixth,29,0.5827037820459395,1 168 | 163,164,Megan,Sixth,26,0.6712950290615973,1 169 | 164,165,Martha,Sixth,30,0.5999854536629398,1 170 | 165,166,Tamra,Sixth,22,0.5426967026078454,1 171 | 166,167,Barbara,Sixth,23,0.5266036117738924,1 172 | 167,168,Helen,Sixth,23,0.3827441141401961,1 173 | 168,169,Delores,Sixth,20,0.5125524640036196,1 174 | 169,170,Cassandra,Sixth,19,0.5445426304713614,1 175 | 170,171,Shawn,Sixth,20,0.5415660559256122,1 176 | 171,172,Diane,Sixth,23,0.31139991061482986,1 177 | 172,173,Lena,Sixth,26,0.4249519659560316,1 178 | 173,174,Renee,Sixth,29,0.4841828832501326,1 179 | 174,175,Linda,Sixth,20,0.4267961009247867,1 180 | 175,176,Brooke,Sixth,21,0.5665981844310021,1 181 | 176,177,Angela,Sixth,24,0.4877586266951791,1 182 | 177,178,Bridget,Sixth,21,0.4569162017466531,1 183 | 178,179,Betty,Sixth,24,0.534980963105169,1 184 | 179,180,Lola,Sixth,30,0.5152307543228434,1 185 | 180,181,Debra,Sixth,21,0.40830678550869137,1 186 | 181,182,Debra,Sixth,29,0.6112997110674216,1 187 | 182,183,Diana,Sixth,19,0.41919925141276265,1 188 | 183,184,Mary,Sixth,28,0.5751100677634319,1 189 | 184,185,Mable,Sixth,23,0.4904157402345066,1 190 | 185,186,Jeanne,Sixth,30,0.3049416329938862,1 191 | 186,187,Sharon,Sixth,26,0.4326362856392755,1 192 | 187,188,Lucia,Sixth,18,0.3895300791317597,1 193 | 188,189,Kimberly,Sixth,24,0.5165075984515924,1 194 | 189,190,Crystal,Sixth,19,0.3799857945139376,1 195 | 190,191,Esther,Sixth,25,0.3598757061536821,1 196 | 191,192,Della,Sixth,28,0.5487373450086704,1 197 | 192,193,Joy,Sixth,20,0.5711191682655571,1 198 | 193,194,Benita,Sixth,24,0.5812982298969092,1 199 | 194,195,Doris,Sixth,18,0.5366558449616664,1 200 | 195,196,Francisca,Sixth,27,0.4946573817954197,1 201 | 196,197,Nia,Sixth,20,0.4877552435335766,1 202 | 197,198,Christina,Sixth,29,0.4994693732595509,1 203 | 198,199,Marta,Sixth,26,0.4278674824865973,1 204 | 199,200,Julia,Sixth,25,0.4036795449925428,1 205 | -------------------------------------------------------------------------------- /SimData/survey_1.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/survey_1.sav -------------------------------------------------------------------------------- /SimData/survey_2.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/SimData/survey_2.sav -------------------------------------------------------------------------------- /YT/paired_samples_t-test_python_scipy_pingouin.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "This is the Jupyter Notebook for the [YouTube tutorial](https://youtu.be/16Sa31mbTWE) and blog post about how to carry out the paired/dependent samples t-test in Python using the two packages SciPy and Pingouin. Note if you fork the entire git repository you can run the script as it is as the example dataset is also found in this repo (i.e., here). " 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Import Data from CSV\n", 15 | "First, we start out by importing the .csv file using Pandas:" 16 | ] 17 | }, 18 | { 19 | "cell_type": "code", 20 | "execution_count": 1, 21 | "metadata": {}, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
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idstestscore
11Pre29.797430
22Pre42.577615
33Pre45.047166
44Pre35.270593
55Pre28.591070
\n", 82 | "
" 83 | ], 84 | "text/plain": [ 85 | " ids test score\n", 86 | "1 1 Pre 29.797430\n", 87 | "2 2 Pre 42.577615\n", 88 | "3 3 Pre 45.047166\n", 89 | "4 4 Pre 35.270593\n", 90 | "5 5 Pre 28.591070" 91 | ] 92 | }, 93 | "execution_count": 1, 94 | "metadata": {}, 95 | "output_type": "execute_result" 96 | } 97 | ], 98 | "source": [ 99 | "import pandas as pd\n", 100 | "\n", 101 | "\n", 102 | "df = pd.read_csv('./SimData/paired_samples_data.csv',\n", 103 | " index_col=0)\n", 104 | "\n", 105 | "df.head()" 106 | ] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "metadata": {}, 111 | "source": [ 112 | "## Paired T-test with SciPy" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 2, 118 | "metadata": {}, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "Ttest_relResult(statistic=115.43840005074212, pvalue=2.349518677786564e-61)" 124 | ] 125 | }, 126 | "execution_count": 2, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | } 130 | ], 131 | "source": [ 132 | "from scipy.stats import ttest_rel\n", 133 | "\n", 134 | "b = df.query('test == \"Pre\"')['score']\n", 135 | "a = df.query('test == \"Post\"')['score']\n", 136 | "\n", 137 | "ttest_rel(a, b)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "## Dependent T-test with Pingouin" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 3, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "data": { 154 | "text/html": [ 155 | "
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Tdoftailp-valCI95%cohen-dBF10power
T-test115.438449two-sided2.349519e-61[5.86, 6.07]0.8813452.437e+571.0
\n", 197 | "
" 198 | ], 199 | "text/plain": [ 200 | " T dof tail p-val CI95% cohen-d \\\n", 201 | "T-test 115.4384 49 two-sided 2.349519e-61 [5.86, 6.07] 0.881345 \n", 202 | "\n", 203 | " BF10 power \n", 204 | "T-test 2.437e+57 1.0 " 205 | ] 206 | }, 207 | "execution_count": 3, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "import pingouin as pt\n", 214 | "\n", 215 | "pt.ttest(a, b, paired=True)" 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "metadata": {}, 221 | "source": [ 222 | "## Calculate Cohen's D" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": 4, 228 | "metadata": {}, 229 | "outputs": [ 230 | { 231 | "ename": "NameError", 232 | "evalue": "name 'xb' is not defined", 233 | "output_type": "error", 234 | "traceback": [ 235 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 236 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 237 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mm2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msd_pol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mxb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mm1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mm2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0msd_pol\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 238 | "\u001b[1;31mNameError\u001b[0m: name 'xb' is not defined" 239 | ] 240 | } 241 | ], 242 | "source": [ 243 | "import numpy as np\n", 244 | "\n", 245 | "m1 = a.mean()\n", 246 | "m2 = b.mean()\n", 247 | "\n", 248 | "sd_pol = np.sqrt(((a.std()**2 + xb.std()**2)/2))\n", 249 | "\n", 250 | "d = (m1 - m2)/sd_pol\n", 251 | "print(d)" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": null, 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "import seaborn as sns\n", 261 | "\n", 262 | "ax=sns.lineplot('test', 'score',\n", 263 | " ci=False, data=df)\n", 264 | "sns.despine()\n", 265 | "ax.set(xlabel=\"Test\", ylabel = \"Depression\")" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": null, 271 | "metadata": {}, 272 | "outputs": [], 273 | "source": [] 274 | } 275 | ], 276 | "metadata": { 277 | "kernelspec": { 278 | "display_name": "Python 3", 279 | "language": "python", 280 | "name": "python3" 281 | }, 282 | "language_info": { 283 | "codemirror_mode": { 284 | "name": "ipython", 285 | "version": 3 286 | }, 287 | "file_extension": ".py", 288 | "mimetype": "text/x-python", 289 | "name": "python", 290 | "nbconvert_exporter": "python", 291 | "pygments_lexer": "ipython3", 292 | "version": "3.8.5" 293 | } 294 | }, 295 | "nbformat": 4, 296 | "nbformat_minor": 4 297 | } 298 | -------------------------------------------------------------------------------- /convert_html_jupyter_notebook_tutorial.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## How to Convert HTML to .ipynb\n", 8 | "This is the code example used for the blog post [https://www.marsja.se/converting-html-to-a-jupyter-notebook/](https://www.marsja.se/converting-html-to-a-jupyter-notebook/) in which we learn how to convert code chunks from a webpage to a Jupyter notebook." 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "from bs4 import BeautifulSoup\n", 18 | "import json\n", 19 | "import urllib\n", 20 | "\n", 21 | "url = 'https://www.marsja.se/python-manova-made-easy-using-statsmodels/'\n", 22 | "\n", 23 | "headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11'\\\n", 24 | " '(KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',\n", 25 | " 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n", 26 | " 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',\n", 27 | " 'Accept-Encoding': 'none',\n", 28 | " 'Accept-Language': 'en-US,en;q=0.8',\n", 29 | " 'Connection': 'keep-alive'}" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 2, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "req = urllib.request.Request(url, headers=headers)\n", 39 | "page = urllib.request.urlopen(req)\n", 40 | "text = page.read()" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 3, 46 | "metadata": {}, 47 | "outputs": [], 48 | "source": [ 49 | "soup = BeautifulSoup(text, 'lxml')" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 10, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "create_nb = {'nbformat': 4, 'nbformat_minor': 2, \n", 59 | " 'cells': [], 'metadata': \n", 60 | " {\"kernelspec\": \n", 61 | " {\"display_name\": \"Python 3\", \n", 62 | " \"language\": \"python\", \"name\": \"python3\"\n", 63 | " }}}\n", 64 | "\n", 65 | "def get_data(soup, content_class):\n", 66 | " for div in soup.find_all('div', \n", 67 | " attrs={'class': content_class}):\n", 68 | " \n", 69 | " code_chunks = div.find_all('code')\n", 70 | " \n", 71 | " for chunk in code_chunks:\n", 72 | " cell_text = ' '\n", 73 | " cell = {}\n", 74 | " cell['metadata'] = {}\n", 75 | " cell['outputs'] = []\n", 76 | " cell['source'] = [chunk.get_text()]\n", 77 | " cell['execution_count'] = None\n", 78 | " cell['cell_type'] = 'code'\n", 79 | " create_nb['cells'].append(cell)\n", 80 | "\n", 81 | "get_data(soup, 'post-content')\n", 82 | "\n", 83 | "with open('Python_MANOVA.ipynb', 'w') as jynotebook:\n", 84 | " jynotebook.write(json.dumps(create_nb))" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 9, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "{'nbformat': 4,\n", 96 | " 'nbformat_minor': 2,\n", 97 | " 'cells': [{'metadata': {},\n", 98 | " 'outputs': [],\n", 99 | " 'source': ['import pandas as pd\\nfrom statsmodels.multivariate.manova import MANOVA'],\n", 100 | " 'execution_count': None,\n", 101 | " 'cell_type': 'code'},\n", 102 | " {'metadata': {},\n", 103 | " 'outputs': [],\n", 104 | " 'source': ['url = \\'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv\\'\\ndf = pd.read_csv(url, index_col=0)\\ndf.columns = df.columns.str.replace(\".\", \"_\")\\ndf.head()'],\n", 105 | " 'execution_count': None,\n", 106 | " 'cell_type': 'code'},\n", 107 | " {'metadata': {},\n", 108 | " 'outputs': [],\n", 109 | " 'source': [\"maov = MANOVA.from_formula('Sepal_Length + Sepal_Width + \\\\\\n Petal_Length + Petal_Width ~ Species', data=df)\"],\n", 110 | " 'execution_count': None,\n", 111 | " 'cell_type': 'code'},\n", 112 | " {'metadata': {},\n", 113 | " 'outputs': [],\n", 114 | " 'source': ['print(maov.mv_test())'],\n", 115 | " 'execution_count': None,\n", 116 | " 'cell_type': 'code'}],\n", 117 | " 'metadata': {'kernelspec': {'display_name': 'Python 3',\n", 118 | " 'language': 'python',\n", 119 | " 'name': 'python3'}}}" 120 | ] 121 | }, 122 | "execution_count": 9, 123 | "metadata": {}, 124 | "output_type": "execute_result" 125 | } 126 | ], 127 | "source": [ 128 | "create_nb" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [] 137 | } 138 | ], 139 | "metadata": { 140 | "kernelspec": { 141 | "display_name": "Python 3", 142 | "language": "python", 143 | "name": "python3" 144 | }, 145 | "language_info": { 146 | "codemirror_mode": { 147 | "name": "ipython", 148 | "version": 3 149 | }, 150 | "file_extension": ".py", 151 | "mimetype": "text/x-python", 152 | "name": "python", 153 | "nbconvert_exporter": "python", 154 | "pygments_lexer": "ipython3", 155 | "version": "3.7.3" 156 | } 157 | }, 158 | "nbformat": 4, 159 | "nbformat_minor": 2 160 | } 161 | -------------------------------------------------------------------------------- /convert_numpy_float_array_to_integer_array_Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "This is the code examples found in a blog post about converting float to integer array ([https://www.marsja.se/how-to-convert-a-float-array-to-an-integer-array-in-python-with-numpy/](https://www.marsja.se/how-to-convert-a-float-array-to-an-integer-array-in-python-with-numpy/). " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import numpy as np\n", 17 | "\n", 18 | "\n", 19 | "oned = np.array([0.1, 0.3, 0.4, \n", 20 | " 0.6, -1.1, 0.3])\n", 21 | "\n", 22 | "twod = np.array([[ 0.3, 1.2, 2.4, 3.1, 4.3],\n", 23 | " [ 5.9, 6.8, 7.6, 8.5, 9.2],\n", 24 | " [10.11, 11.1, 12.23, 13.2, 14.2],\n", 25 | " [15.2, 16.4, 17.1, 18.1, 19.1]])" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [ 33 | { 34 | "data": { 35 | "text/plain": [ 36 | "array([ 0.1, 0.3, 0.4, 0.6, -1.1, 0.3])" 37 | ] 38 | }, 39 | "metadata": {}, 40 | "output_type": "display_data" 41 | }, 42 | { 43 | "data": { 44 | "text/plain": [ 45 | "dtype('float64')" 46 | ] 47 | }, 48 | "metadata": {}, 49 | "output_type": "display_data" 50 | } 51 | ], 52 | "source": [ 53 | "display(oned)\n", 54 | "display(oned.dtype)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "## Converting a Float Array to Int\n", 62 | "Here's how to convert the float array to an integer array:" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 3, 68 | "metadata": {}, 69 | "outputs": [ 70 | { 71 | "data": { 72 | "text/plain": [ 73 | "array([ 0, 0, 0, 0, -1, 0])" 74 | ] 75 | }, 76 | "metadata": {}, 77 | "output_type": "display_data" 78 | }, 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "dtype('int32')" 83 | ] 84 | }, 85 | "metadata": {}, 86 | "output_type": "display_data" 87 | } 88 | ], 89 | "source": [ 90 | "# convert array to integer python\n", 91 | "oned_int = oned.astype(int)\n", 92 | "display(oned_int)\n", 93 | "display(oned_int.dtype)" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "## 2-d Array\n", 101 | "The same method, as above, can be applied to a n-way array. Here's how to do it on a 2-d array:" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 4, 107 | "metadata": {}, 108 | "outputs": [ 109 | { 110 | "data": { 111 | "text/plain": [ 112 | "array([[ 0, 1, 2, 3, 4],\n", 113 | " [ 5, 6, 7, 8, 9],\n", 114 | " [10, 11, 12, 13, 14],\n", 115 | " [15, 16, 17, 18, 19]])" 116 | ] 117 | }, 118 | "metadata": {}, 119 | "output_type": "display_data" 120 | }, 121 | { 122 | "data": { 123 | "text/plain": [ 124 | "dtype('int32')" 125 | ] 126 | }, 127 | "metadata": {}, 128 | "output_type": "display_data" 129 | } 130 | ], 131 | "source": [ 132 | "twod_int = twod.astype(int)\n", 133 | "display(twod_int)\n", 134 | "display(twod_int.dtype)" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": {}, 140 | "source": [ 141 | "## Rounding Before Converting\n", 142 | "Here's how to round the numbers BEFORE converting the float numbers to integer:" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 5, 148 | "metadata": {}, 149 | "outputs": [ 150 | { 151 | "data": { 152 | "text/plain": [ 153 | "array([ 0, 0, 0, 1, -1, 0])" 154 | ] 155 | }, 156 | "metadata": {}, 157 | "output_type": "display_data" 158 | }, 159 | { 160 | "data": { 161 | "text/plain": [ 162 | "dtype('int32')" 163 | ] 164 | }, 165 | "metadata": {}, 166 | "output_type": "display_data" 167 | } 168 | ], 169 | "source": [ 170 | "oned = np.array([0.1, 0.3, 0.4, \n", 171 | " 0.6, -1.1, 0.3])\n", 172 | "\n", 173 | "oned = np.around(oned)\n", 174 | "\n", 175 | "# numpy convert to int\n", 176 | "oned_int = oned.astype(int)\n", 177 | "display(oned_int)\n", 178 | "display(oned_int.dtype)" 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "metadata": {}, 184 | "source": [ 185 | "## Ceil:" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 6, 191 | "metadata": {}, 192 | "outputs": [ 193 | { 194 | "data": { 195 | "text/plain": [ 196 | "array([ 1, 1, 1, 1, -1, 1])" 197 | ] 198 | }, 199 | "metadata": {}, 200 | "output_type": "display_data" 201 | }, 202 | { 203 | "data": { 204 | "text/plain": [ 205 | "dtype('int32')" 206 | ] 207 | }, 208 | "metadata": {}, 209 | "output_type": "display_data" 210 | } 211 | ], 212 | "source": [ 213 | "oned = np.array([0.1, 0.3, 0.4, \n", 214 | " 0.6, -1.1, 0.3])\n", 215 | "\n", 216 | "oned = np.ceil(oned)\n", 217 | "\n", 218 | "# numpy float to int \n", 219 | "oned_int = oned.astype(int)\n", 220 | "display(oned_int)\n", 221 | "display(oned_int.dtype)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "markdown", 226 | "metadata": {}, 227 | "source": [ 228 | "## Floor:" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 7, 234 | "metadata": {}, 235 | "outputs": [ 236 | { 237 | "data": { 238 | "text/plain": [ 239 | "array([ 0, 0, 0, 0, -2, 0])" 240 | ] 241 | }, 242 | "metadata": {}, 243 | "output_type": "display_data" 244 | }, 245 | { 246 | "data": { 247 | "text/plain": [ 248 | "dtype('int32')" 249 | ] 250 | }, 251 | "metadata": {}, 252 | "output_type": "display_data" 253 | } 254 | ], 255 | "source": [ 256 | "oned = np.array([0.1, 0.3, 0.4, \n", 257 | " 0.6, -1.1, 0.3])\n", 258 | "\n", 259 | "oned = np.floor(oned)\n", 260 | "\n", 261 | "# numpy float to int \n", 262 | "oned_int = oned.astype(int)\n", 263 | "display(oned_int)\n", 264 | "display(oned_int.dtype)" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": null, 270 | "metadata": {}, 271 | "outputs": [], 272 | "source": [] 273 | } 274 | ], 275 | "metadata": { 276 | "kernelspec": { 277 | "display_name": "Python 3", 278 | "language": "python", 279 | "name": "python3" 280 | }, 281 | "language_info": { 282 | "codemirror_mode": { 283 | "name": "ipython", 284 | "version": 3 285 | }, 286 | "file_extension": ".py", 287 | "mimetype": "text/x-python", 288 | "name": "python", 289 | "nbconvert_exporter": "python", 290 | "pygments_lexer": "ipython3", 291 | "version": "3.8.3" 292 | } 293 | }, 294 | "nbformat": 4, 295 | "nbformat_minor": 4 296 | } 297 | -------------------------------------------------------------------------------- /descriptive_stats_using_numpy_python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "C:\\Users\\erima96\\.conda\\envs\\RPY3\\lib\\site-packages\\ipykernel_launcher.py:4: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.\n", 13 | " after removing the cwd from sys.path.\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "import numpy as np\n", 19 | "\n", 20 | "data_file = 'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/ToothGrowth.csv'\n", 21 | "data = np.genfromtxt(data_file, names=True,\n", 22 | " delimiter=\",\", dtype=None)" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": {}, 29 | "outputs": [], 30 | "source": [ 31 | "summary_stats = []\n", 32 | "for supp_lvl in np.unique(data['supp']):\n", 33 | " \n", 34 | " for dose_lvl in np.unique(data['dose']):\n", 35 | " \n", 36 | " # Subsetting\n", 37 | " data_to_sum = data[(data['supp'] == supp_lvl) & (data['dose'] == dose_lvl)]\n", 38 | " # Calculating the descriptives\n", 39 | " mean = data_to_sum['len'].mean()\n", 40 | " sd = data_to_sum['len'].std()\n", 41 | " max_supp = data_to_sum['len'].max()\n", 42 | " min_supp = data_to_sum['len'].min()\n", 43 | " ps = np.percentile(data_to_sum['len'], [25, 75] )\n", 44 | " summary_stats.append((mean, sd, max_supp, min_supp, ps[0], ps[1], supp_lvl, dose_lvl))" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 4, 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "name": "stdout", 54 | "output_type": "stream", 55 | "text": [ 56 | "[[b'13.229999999999999' b'4.230850978231212' b'21.5' b'8.2' b'9.7'\n", 57 | " b'16.175' b'\"OJ\"' b'0.5']\n", 58 | " [b'22.7' b'3.7102560558538276' b'27.3' b'14.5' b'20.3'\n", 59 | " b'25.650000000000002' b'\"OJ\"' b'1.0']\n", 60 | " [b'26.060000000000002' b'2.5188092424794695' b'30.9' b'22.4' b'24.575'\n", 61 | " b'27.075000000000003' b'\"OJ\"' b'2.0']\n", 62 | " [b'7.9799999999999995' b'2.605686090073015' b'11.5' b'4.2'\n", 63 | " b'5.949999999999999' b'10.899999999999999' b'\"VC\"' b'0.5']\n", 64 | " [b'16.770000000000003' b'2.386231338324095' b'22.5' b'13.6'\n", 65 | " b'15.274999999999999' b'17.3' b'\"VC\"' b'1.0']\n", 66 | " [b'26.139999999999997' b'4.551527216220946' b'33.9' b'18.5' b'23.375'\n", 67 | " b'28.8' b'\"VC\"' b'2.0']]\n" 68 | ] 69 | } 70 | ], 71 | "source": [ 72 | "results = np.array(summary_stats, dtype=None)\n", 73 | "np.set_printoptions(suppress=True)\n", 74 | "print(results)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": null, 80 | "metadata": {}, 81 | "outputs": [], 82 | "source": [] 83 | } 84 | ], 85 | "metadata": { 86 | "kernelspec": { 87 | "display_name": "Python 3", 88 | "language": "python", 89 | "name": "python3" 90 | }, 91 | "language_info": { 92 | "codemirror_mode": { 93 | "name": "ipython", 94 | "version": 3 95 | }, 96 | "file_extension": ".py", 97 | "mimetype": "text/x-python", 98 | "name": "python", 99 | "nbconvert_exporter": "python", 100 | "pygments_lexer": "ipython3", 101 | "version": "3.7.3" 102 | } 103 | }, 104 | "nbformat": 4, 105 | "nbformat_minor": 2 106 | } 107 | -------------------------------------------------------------------------------- /example_sheets2.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/example_sheets2.xlsx -------------------------------------------------------------------------------- /how_to_read_xlsx_files_in_Python_using_pylightxl.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pylightxl as xl\n", 10 | "# https://pylightxl.readthedocs.io/en/latest/quickstart.html#access-worksheet-and-cell-data" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 6, 16 | "metadata": {}, 17 | "outputs": [ 18 | { 19 | "data": { 20 | "text/plain": [ 21 | "pylightxl.Database.Worksheet" 22 | ] 23 | }, 24 | "execution_count": 6, 25 | "metadata": {}, 26 | "output_type": "execute_result" 27 | } 28 | ], 29 | "source": [ 30 | "db = xl.readxl('example_sheets2.xlsx')\n", 31 | "db.ws('Session1')" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [] 40 | } 41 | ], 42 | "metadata": { 43 | "kernelspec": { 44 | "display_name": "Python 3", 45 | "language": "python", 46 | "name": "python3" 47 | }, 48 | "language_info": { 49 | "codemirror_mode": { 50 | "name": "ipython", 51 | "version": 3 52 | }, 53 | "file_extension": ".py", 54 | "mimetype": "text/x-python", 55 | "name": "python", 56 | "nbconvert_exporter": "python", 57 | "pygments_lexer": "ipython3", 58 | "version": "3.7.3" 59 | } 60 | }, 61 | "nbformat": 4, 62 | "nbformat_minor": 2 63 | } 64 | -------------------------------------------------------------------------------- /mann_whitney_u_test_Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "forced-access", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "text/html": [ 12 | "
\n", 13 | "\n", 26 | "\n", 27 | " \n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | "
XY
00.7964690.831551
10.3861390.831828
20.3268510.934401
30.6513151.149432
40.8194691.024455
\n", 62 | "
" 63 | ], 64 | "text/plain": [ 65 | " X Y\n", 66 | "0 0.796469 0.831551\n", 67 | "1 0.386139 0.831828\n", 68 | "2 0.326851 0.934401\n", 69 | "3 0.651315 1.149432\n", 70 | "4 0.819469 1.024455" 71 | ] 72 | }, 73 | "execution_count": 1, 74 | "metadata": {}, 75 | "output_type": "execute_result" 76 | } 77 | ], 78 | "source": [ 79 | "import pandas as pd\n", 80 | "\n", 81 | "df = pd.read_csv('./SimData/mannwu.csv')\n", 82 | "\n", 83 | "df.head()" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 3, 89 | "id": "surprised-eclipse", 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "MannwhitneyuResult(statistic=81.0, pvalue=0.005434115153576264)" 96 | ] 97 | }, 98 | "execution_count": 3, 99 | "metadata": {}, 100 | "output_type": "execute_result" 101 | } 102 | ], 103 | "source": [ 104 | "from scipy.stats import mannwhitneyu\n", 105 | "\n", 106 | "mannwhitneyu(df['X'], df['Y'])" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 5, 112 | "id": "civil-price", 113 | "metadata": {}, 114 | "outputs": [ 115 | { 116 | "data": { 117 | "text/html": [ 118 | "
\n", 119 | "\n", 132 | "\n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | "
U-valtailp-valRBCCLES
MWU81.0less0.0054340.50.75
\n", 154 | "
" 155 | ], 156 | "text/plain": [ 157 | " U-val tail p-val RBC CLES\n", 158 | "MWU 81.0 less 0.005434 0.5 0.75" 159 | ] 160 | }, 161 | "execution_count": 5, 162 | "metadata": {}, 163 | "output_type": "execute_result" 164 | } 165 | ], 166 | "source": [ 167 | "from pingouin import mwu\n", 168 | "\n", 169 | "mwu(df['X'], df['Y'], tail='one-sided')" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": null, 175 | "id": "patient-conviction", 176 | "metadata": {}, 177 | "outputs": [], 178 | "source": [] 179 | } 180 | ], 181 | "metadata": { 182 | "kernelspec": { 183 | "display_name": "Python 3", 184 | "language": "python", 185 | "name": "python3" 186 | }, 187 | "language_info": { 188 | "codemirror_mode": { 189 | "name": "ipython", 190 | "version": 3 191 | }, 192 | "file_extension": ".py", 193 | "mimetype": "text/x-python", 194 | "name": "python", 195 | "nbconvert_exporter": "python", 196 | "pygments_lexer": "ipython3", 197 | "version": "3.9.1" 198 | } 199 | }, 200 | "nbformat": 4, 201 | "nbformat_minor": 5 202 | } 203 | -------------------------------------------------------------------------------- /multiple_Sheets.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/multiple_Sheets.xlsx -------------------------------------------------------------------------------- /names_ages.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/names_ages.xlsx -------------------------------------------------------------------------------- /newfilename.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/marsja/jupyter/76864b3554371de9eced965f46a06e43726d9863/newfilename.xlsx -------------------------------------------------------------------------------- /python_descriptive_statistics_sample.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "
\n", 12 | "\n", 25 | "\n", 26 | " \n", 27 | " \n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | "
IDNameDayAgeResponseGender
01JohnFifth230.4537330
12BillieFifth220.2573600
23RobertFifth200.4433930
34DonFifth270.4235920
45JosephFifth210.5713550
\n", 85 | "
" 86 | ], 87 | "text/plain": [ 88 | " ID Name Day Age Response Gender\n", 89 | "0 1 John Fifth 23 0.453733 0\n", 90 | "1 2 Billie Fifth 22 0.257360 0\n", 91 | "2 3 Robert Fifth 20 0.443393 0\n", 92 | "3 4 Don Fifth 27 0.423592 0\n", 93 | "4 5 Joseph Fifth 21 0.571355 0" 94 | ] 95 | }, 96 | "execution_count": 3, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | } 100 | ], 101 | "source": [ 102 | "import pandas as pd\n", 103 | "\n", 104 | "data = 'https://raw.githubusercontent.com/marsja/jupyter/master/SimData/FifthDayData.csv'\n", 105 | "\n", 106 | "df = pd.read_csv(data)\n", 107 | "\n", 108 | "df.head()" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 9, 114 | "metadata": {}, 115 | "outputs": [ 116 | { 117 | "data": { 118 | "text/plain": [ 119 | "Male 0\n", 120 | "1 0\n", 121 | "2 0\n", 122 | "3 0\n", 123 | "4 0\n", 124 | " ..\n", 125 | "195 1\n", 126 | "196 1\n", 127 | "197 1\n", 128 | "198 1\n", 129 | "199 1\n", 130 | "Name: Gender, Length: 200, dtype: int64" 131 | ] 132 | }, 133 | "execution_count": 9, 134 | "metadata": {}, 135 | "output_type": "execute_result" 136 | } 137 | ], 138 | "source": [ 139 | "df.Gender.rename({0:'Male'})" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": null, 145 | "metadata": {}, 146 | "outputs": [], 147 | "source": [] 148 | } 149 | ], 150 | "metadata": { 151 | "kernelspec": { 152 | "display_name": "Python 3", 153 | "language": "python", 154 | "name": "python3" 155 | }, 156 | "language_info": { 157 | "codemirror_mode": { 158 | "name": "ipython", 159 | "version": 3 160 | }, 161 | "file_extension": ".py", 162 | "mimetype": "text/x-python", 163 | "name": "python", 164 | "nbconvert_exporter": "python", 165 | "pygments_lexer": "ipython3", 166 | "version": "3.7.3" 167 | } 168 | }, 169 | "nbformat": 4, 170 | "nbformat_minor": 2 171 | } 172 | -------------------------------------------------------------------------------- /rpy2 tutorial example code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Rpy2 Tutorial: Code Example\n", 8 | "This code example is for the rpy2 tutorials on [my blog](https://www.marsja.se/r-from-python-rpy2-tutorial/) and my [YouTube Channel](https://youtu.be/GvmoOHkABNA).\n", 9 | "\n", 10 | "If you want another code example on how to call R from Python you can find it [here](https://youtu.be/RK-n78ZOXUg) in the YouTube tutorial." 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 2, 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import rpy2.robjects as robjects\n", 20 | "import rpy2.robjects.packages as rpackages\n", 21 | "from rpy2.robjects.vectors import StrVector\n", 22 | "\n", 23 | "packageNames = ('afex', 'emmeans')\n", 24 | "utils = rpackages.importr('utils')\n", 25 | "utils.chooseCRANmirror(ind=1)\n", 26 | "\n", 27 | "packnames_to_install = [x for x in packageNames if not rpackages.isinstalled(x)]\n", 28 | "\n", 29 | "if len(packnames_to_install) > 0:\n", 30 | " utils.install_packages(StrVector(packnames_to_install))" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 3, 36 | "metadata": {}, 37 | "outputs": [ 38 | { 39 | "data": { 40 | "text/html": [ 41 | "\n", 42 | " R/rpy2 DataFrame (6 x 4)\n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 64 | " \n", 65 | " \n", 68 | " \n", 69 | " \n", 72 | " \n", 73 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 84 | " \n", 85 | " \n", 88 | " \n", 89 | " \n", 92 | " \n", 93 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 104 | " \n", 105 | " \n", 108 | " \n", 109 | " \n", 112 | " \n", 113 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 124 | " \n", 125 | " \n", 128 | " \n", 129 | " \n", 132 | " \n", 133 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 144 | " \n", 145 | " \n", 148 | " \n", 149 | " \n", 152 | " \n", 153 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 164 | " \n", 165 | " \n", 168 | " \n", 169 | " \n", 172 | " \n", 173 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | "
ObservationSubjectValenceRecall
\n", 62 | " 1\n", 63 | " \n", 66 | " Jim\n", 67 | " \n", 70 | " Neg\n", 71 | " \n", 74 | " 32\n", 75 | "
\n", 82 | " 2\n", 83 | " \n", 86 | " Jim\n", 87 | " \n", 90 | " Neu\n", 91 | " \n", 94 | " 15\n", 95 | "
\n", 102 | " 3\n", 103 | " \n", 106 | " Jim\n", 107 | " \n", 110 | " Pos\n", 111 | " \n", 114 | " 45\n", 115 | "
\n", 122 | " 4\n", 123 | " \n", 126 | " Victor\n", 127 | " \n", 130 | " Neg\n", 131 | " \n", 134 | " 30\n", 135 | "
\n", 142 | " 5\n", 143 | " \n", 146 | " Victor\n", 147 | " \n", 150 | " Neu\n", 151 | " \n", 154 | " 13\n", 155 | "
\n", 162 | " 6\n", 163 | " \n", 166 | " Victor\n", 167 | " \n", 170 | " Pos\n", 171 | " \n", 174 | " 40\n", 175 | "
\n", 181 | " " 182 | ], 183 | "text/plain": [ 184 | "R object with classes: ('data.frame',) mapped to:\n", 185 | "\n", 186 | "[IntVector, FactorVector, FactorVector, IntVector]\n", 187 | " Observation: \n", 188 | " R object with classes: ('integer',) mapped to:\n", 189 | "\n", 190 | "[1, 2, 3, 4, 5, 6]\n", 191 | " Subject: \n", 192 | " R object with classes: ('factor',) mapped to:\n", 193 | "\n", 194 | "[Jim, Jim, Jim, Victor, Victor, Victor]\n", 195 | " Valence: \n", 196 | " R object with classes: ('factor',) mapped to:\n", 197 | "\n", 198 | "[Neg, Neu, Pos, Neg, Neu, Pos]\n", 199 | " Recall: \n", 200 | " R object with classes: ('integer',) mapped to:\n", 201 | "\n", 202 | "[32, 15, 45, 30, 13, 40]" 203 | ] 204 | }, 205 | "execution_count": 3, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | } 209 | ], 210 | "source": [ 211 | "data = robjects.r('read.table(file =' \\\n", 212 | " '\"http://personality-project.org/r/datasets/R.appendix3.data\", header = T)')\n", 213 | "\n", 214 | "data.head()" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "afex = rpackages.importr('afex') \n", 224 | "model = afex.aov_ez('Subject', 'Recall', data, within='Valence')\n", 225 | "print(model)" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 4, 231 | "metadata": {}, 232 | "outputs": [ 233 | { 234 | "name": "stdout", 235 | "output_type": "stream", 236 | "text": [ 237 | "$emmeans\n", 238 | "\r\n", 239 | " Valence emmean SE df lower.CL upper.CL\n", 240 | "\r\n", 241 | " Neg 27.8 1.570563 7.33 24.119388 31.48061\n", 242 | "\r\n", 243 | " Neu 11.6 1.570563 7.33 7.919388 15.28061\n", 244 | "\r\n", 245 | " Pos 40.0 1.570563 7.33 36.319388 43.68061\n", 246 | "\r\n", 247 | "\n", 248 | "\r\n", 249 | "Confidence level used: 0.95 \n", 250 | "\r\n", 251 | "\n", 252 | "\r\n", 253 | "$contrasts\n", 254 | "\r\n", 255 | " contrast estimate SE df t.ratio p.value\n", 256 | "\r\n", 257 | " Neg - Neu 16.2 1.465151 8 11.057 <.0001\n", 258 | "\r\n", 259 | " Neg - Pos -12.2 1.465151 8 -8.327 <.0001\n", 260 | "\r\n", 261 | " Neu - Pos -28.4 1.465151 8 -19.384 <.0001\n", 262 | "\r\n", 263 | "\n", 264 | "\r\n", 265 | "P value adjustment: holm method for 3 tests \n", 266 | "\r\n", 267 | "\n", 268 | "\n" 269 | ] 270 | } 271 | ], 272 | "source": [ 273 | "emmeans = rpackages.importr('emmeans', \n", 274 | " robject_translations = {\"recover.data.call\": \"recover_data_call1\"})\n", 275 | "pairwise = emmeans.emmeans(model, \"Valence\", contr=\"pairwise\", adjust=\"holm\")\n", 276 | "\n", 277 | "print(pairwise)" 278 | ] 279 | }, 280 | { 281 | "cell_type": "code", 282 | "execution_count": null, 283 | "metadata": {}, 284 | "outputs": [], 285 | "source": [] 286 | } 287 | ], 288 | "metadata": { 289 | "kernelspec": { 290 | "display_name": "Python 3", 291 | "language": "python", 292 | "name": "python3" 293 | }, 294 | "language_info": { 295 | "codemirror_mode": { 296 | "name": "ipython", 297 | "version": 3 298 | }, 299 | "file_extension": ".py", 300 | "mimetype": "text/x-python", 301 | "name": "python", 302 | "nbconvert_exporter": "python", 303 | "pygments_lexer": "ipython3", 304 | "version": "3.7.3" 305 | } 306 | }, 307 | "nbformat": 4, 308 | "nbformat_minor": 2 309 | } 310 | --------------------------------------------------------------------------------