{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models VII: 3+ level categorical predictors & One-way ANOVA\n",
    "\n",
    "In notebook `05_models` we saw how we can *encode* a *categorical predictor with 2-levels* (`Student = (Yes or No)`) in a GLM using **treatment (dummy) codes**. \n",
    "\n",
    "Let's extend this idea to explore how to model categorical variables with **more thant 2 levels**\n",
    "\n",
    "We're going to look at a new dataset that comes from the following paper:\n",
    "\n",
    "[Meyer, G., von Meduna, M., Brosowski, T., & Hayer, T. (2012). Is poker a game of skill or chance? A quasi-experimental study. Journal of Gambling Studies](https://link.springer.com/article/10.1007/s10899-012-9327-8)\n",
    "\n",
    "The experiments used a 2 (skill) x 3 (hand) x 2 (limit) design\n",
    "\n",
    "This is reflected in the provided tidy-dataset which has 4 columns:\n",
    "\n",
    "| Variable   | Description                     |\n",
    "|------------|---------------------------------|\n",
    "| skill     | a player's skill (expert/average)|\n",
    "| hand     | the quality of the hand experimenters manipulate (bad/neutral/good)|\n",
    "| limit     | the style of game (fixed/no-limit)|\n",
    "| balance     | a player's final balance in Euros|\n",
    "\n",
    "### Slides for reference\n",
    "\n",
    "[Modeling Data VI (slides)](https://stat-intuitions.com/lectures/wk7/2.html)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Let's load the data and verify the details above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>skill</th><th>hand</th><th>limit</th><th>balance</th></tr><tr><td>str</td><td>str</td><td>str</td><td>f64</td></tr></thead><tbody><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>4.0</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>5.55</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>9.45</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>7.19</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>5.71</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "┌────────┬──────┬───────┬─────────┐\n",
       "│ skill  ┆ hand ┆ limit ┆ balance │\n",
       "│ ---    ┆ ---  ┆ ---   ┆ ---     │\n",
       "│ str    ┆ str  ┆ str   ┆ f64     │\n",
       "╞════════╪══════╪═══════╪═════════╡\n",
       "│ expert ┆ bad  ┆ fixed ┆ 4.0     │\n",
       "│ expert ┆ bad  ┆ fixed ┆ 5.55    │\n",
       "│ expert ┆ bad  ┆ fixed ┆ 9.45    │\n",
       "│ expert ┆ bad  ┆ fixed ┆ 7.19    │\n",
       "│ expert ┆ bad  ┆ fixed ┆ 5.71    │\n",
       "└────────┴──────┴───────┴─────────┘"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import polars as pl\n",
    "from polars import col\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "df = pl.read_csv('./data/poker-tidy.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reflects the 300 participants in the experiment and 4 columns of our dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Challenge: Using polars, figure out how many observations there are per *cell* of the 2x3x2 design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>skill</th><th>hand</th><th>limit</th><th>len</th></tr><tr><td>str</td><td>str</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;expert&quot;</td><td>&quot;neutral&quot;</td><td>&quot;fixed&quot;</td><td>25</td></tr><tr><td>&quot;average&quot;</td><td>&quot;neutral&quot;</td><td>&quot;no-limit&quot;</td><td>25</td></tr><tr><td>&quot;average&quot;</td><td>&quot;good&quot;</td><td>&quot;fixed&quot;</td><td>25</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>25</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;good&quot;</td><td>&quot;no-limit&quot;</td><td>25</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;average&quot;</td><td>&quot;good&quot;</td><td>&quot;no-limit&quot;</td><td>25</td></tr><tr><td>&quot;average&quot;</td><td>&quot;bad&quot;</td><td>&quot;fixed&quot;</td><td>25</td></tr><tr><td>&quot;average&quot;</td><td>&quot;bad&quot;</td><td>&quot;no-limit&quot;</td><td>25</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;neutral&quot;</td><td>&quot;no-limit&quot;</td><td>25</td></tr><tr><td>&quot;expert&quot;</td><td>&quot;good&quot;</td><td>&quot;fixed&quot;</td><td>25</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 4)\n",
       "┌─────────┬─────────┬──────────┬─────┐\n",
       "│ skill   ┆ hand    ┆ limit    ┆ len │\n",
       "│ ---     ┆ ---     ┆ ---      ┆ --- │\n",
       "│ str     ┆ str     ┆ str      ┆ u32 │\n",
       "╞═════════╪═════════╪══════════╪═════╡\n",
       "│ expert  ┆ neutral ┆ fixed    ┆ 25  │\n",
       "│ average ┆ neutral ┆ no-limit ┆ 25  │\n",
       "│ average ┆ good    ┆ fixed    ┆ 25  │\n",
       "│ expert  ┆ bad     ┆ fixed    ┆ 25  │\n",
       "│ expert  ┆ good    ┆ no-limit ┆ 25  │\n",
       "│ …       ┆ …       ┆ …        ┆ …   │\n",
       "│ average ┆ good    ┆ no-limit ┆ 25  │\n",
       "│ average ┆ bad     ┆ fixed    ┆ 25  │\n",
       "│ average ┆ bad     ┆ no-limit ┆ 25  │\n",
       "│ expert  ┆ neutral ┆ no-limit ┆ 25  │\n",
       "│ expert  ┆ good    ┆ fixed    ┆ 25  │\n",
       "└─────────┴─────────┴──────────┴─────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "df.group_by(['skill','hand', 'limit']).len()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Predictor with 3 levels\n",
    "\n",
    "The first question we're interested in testing is: **does having a better `hand` earn more money, i.e a higher `balance`?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge: Visualization\n",
    "\n",
    "Create a figure that plots data from each level of `hand` on the x-axis and `balance` on the y-axis\n",
    "\n",
    "Add a bar or point that also reflect the *mean* balance at each level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10df0ddd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10df0ddd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 965
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "order = ['bad', 'neutral', 'good']\n",
    "with sns.plotting_context('talk'):\n",
    "    grid = sns.FacetGrid(data=df, height=5, aspect=2)\n",
    "    grid = grid.map(sns.stripplot, 'hand', 'balance', order=order, alpha=0.15, color='gray')\n",
    "    grid.map_dataframe(sns.pointplot, 'hand', 'balance', hue='hand', order=order, hue_order=order, palette='Set1')\n",
    "    grid.set_axis_labels('Poker Hand', 'Final Balance (in Euros)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge: Worth it?\n",
    "\n",
    "Like previous notebooks start by defining a *compact* model and an *augmented* model and then comparing them to see whether adding `hand` to a model reduces enough error to be worth it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>299.0</td>\n",
       "      <td>7579.984625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>297.0</td>\n",
       "      <td>5020.583223</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2559.401402</td>\n",
       "      <td>75.702581</td>\n",
       "      <td>2.699281e-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   df_resid          ssr  df_diff      ss_diff          F        Pr(>F)\n",
       "0     299.0  7579.984625      0.0          NaN        NaN           NaN\n",
       "1     297.0  5020.583223      2.0  2559.401402  75.702581  2.699281e-27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "# Compact\n",
    "model_c = ols('balance ~ 1', data=df.to_pandas())\n",
    "results_c = model_c.fit()\n",
    "\n",
    "# Augmented\n",
    "model_a = ols('balance ~ C(hand)', data=df.to_pandas())\n",
    "results_a = model_a.fit()\n",
    "\n",
    "# Worth it?\n",
    "anova_lm(results_c, results_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `anova_lm` with just your augmented model and the argument `type=3` to generate a One-way ANOVA table. How does this compare to model comparison you just did?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(hand)</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2559.401402</td>\n",
       "      <td>1279.700701</td>\n",
       "      <td>75.702581</td>\n",
       "      <td>2.699281e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>297.0</td>\n",
       "      <td>5020.583223</td>\n",
       "      <td>16.904321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             df       sum_sq      mean_sq          F        PR(>F)\n",
       "C(hand)     2.0  2559.401402  1279.700701  75.702581  2.699281e-27\n",
       "Residual  297.0  5020.583223    16.904321        NaN           NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solution\n",
    "anova_lm(results_a, type=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Parameters\n",
    "\n",
    "Let's try to understand how the GLM represented the levels of `hand`\n",
    "\n",
    "Remember that the default for `C()` is to represent our categorical variable using **treatment/dummy codes** - with the reference level being the *alphabetically* first level in the data.\n",
    "\n",
    "Using our helper plottign function we can visualize the **design matrix** for our model and see that it created **2 additional columns** to represent `hand`. In this case:\n",
    "- Intercept = mean of `hand = 'bad'`\n",
    "- $\\beta_1$ = difference between `hand = 'good'` and `hand = 'bad'`\n",
    "- $\\beta_2$ = difference between `hand = 'neutral'` and `hand = 'bad'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 629
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from helpers import plot_design_matrix\n",
    "\n",
    "# In case you named your model something different above we redefine it here\n",
    "model_a = ols('balance ~ C(hand)', data=df.to_pandas())\n",
    "\n",
    "plot_design_matrix(model_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "\n",
    "Use polars to calculate the mean for each level of `hand`. Then using the explanation of dummy-coding above, use the means to recreate what each parameter estimate represents.\n",
    "\n",
    "Use `print` in Python or a markdown cell to write out your explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept             5.9415\n",
       "C(hand)[T.good]       7.0849\n",
       "C(hand)[T.neutral]    4.4051\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here are the parameters\n",
    "# Can you get these values by using the means of each level of hand?\n",
    "results_a.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept = Bad hand: 5.941\n",
      "B1 = Good - Bad: 7.085\n",
      "B2 = Neutral - Bad: 4.405\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "hand_means = df.group_by('hand', maintain_order=True).agg(col('balance').mean())\n",
    "bad = hand_means[0,1]\n",
    "neutral = hand_means[1,1]\n",
    "good = hand_means[2,1]\n",
    "\n",
    "print(f'Intercept = Bad hand: {bad:.3f}')   \n",
    "print(f\"B1 = Good - Bad: {good - bad:.3f}\")\n",
    "print(f\"B2 = Neutral - Bad: {neutral - bad:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Coding Systems for Categorical Variables\n",
    "\n",
    "What if we want to represent the levels of our categorical variable in different way? We already saw how to change the **reference** level when using treatment/dummy codes - but we can also use an entirely different approach.\n",
    "\n",
    "In this section we'll cover the 2 additional common ways to *encode* categorical variables:\n",
    "1. Deviation (Sum) Coding\n",
    "2. Orthogonal Polynomial Coding\n",
    "\n",
    "The reason to understand these additional encoding schemes is that they provide **valid inferences** when we extend our model to include additional categorical or continuous predictors.\n",
    "\n",
    "Below we provide a brief overview of each coding scheme and how to use it but you should check-out the following resources as part of this lab as well:\n",
    "\n",
    "- [Coding Systems for Categorical Variables](https://stats.oarc.ucla.edu/spss/faq/coding-systems-for-categorical-variables-in-regression-analysis-2/#DEVIATION%20EFFECT%20CODING) *conceptual overview, but examples are in R*\n",
    "- [Contrasts in StatsModels](https://www.statsmodels.org/stable/contrasts.html) *Shorter Python version of the guide above*\n",
    "- [How to use different coding schemes in formulas](https://patsy.readthedocs.io/en/latest/API-reference.html#categorical-coding-ref)\n",
    "\n",
    "\n",
    "| Scheme Name      | Intercept Interpretation             | Other Parameters Interpretation                                | Formula Syntax        | Affected by Unbalanced Designs? | Typical Usage                                  | Additional Notes or Customizations                                   | Interpretation Warnings and Guidance                                      |\n",
    "|-----------------|------------------------------------|---------------------------------------------------------------|----------------------|--------------------------------|--------------------------------|------------------------------------------------------------------------|---------------------------------------------------------------------------|\n",
    "| Treatment (Dummy) | Baseline (reference) category    | Difference from the reference category                        | `C(var, Treatment)`  | Yes                            | Default; good for categorical predictors with a natural reference      | Reference level can be set via `C(var, Treatment(reference=...)`        | Be mindful of choosing a meaningful reference category                   |\n",
    "| Deviation (Sum)  | Grand mean                       | Difference from grand mean                                    | `C(var, Sum)`        | No                             | ANOVA-style contrasts; balanced designs                               | Ensures sum of contrasts = 0                                            | In unbalanced designs, parameter estimates may not match simple group means      |\n",
    "| Polynomial      | Linear trend                      | Higher-order polynomial trends                               | `C(var, Poly, k)`    | No                             | Modeling trends in ordered categories                                | Useful for variables with lots of levels                                 | `k` determines the highest-order polynomial (e.g. linear, quadratic, etc)                           | \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deviation (Sum/Contrast) Coding\n",
    "\n",
    "This form of coding uses the intercept to estimate the **grand-mean** (mean of means) of all levels and uses additional parameters to calculate the difference between each level and the grand-mean. This coding scheme is very useful when we have categorical variables with 3+ levels and **uneven group sizes** and want to perform valid F-tests (ANOVAs). \n",
    "\n",
    "*In the case of 2-levels $\\hat{\\beta_1}$ = 2x mean difference between levels*   \n",
    "\n",
    "We can use this coding scheme with `C(hand, Sum)`\n",
    "\n",
    "We will have 2 columns in our **design matrix** but now they encode `good` and `neutral` using 1s and 0s and represent `bad` using -1s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate model\n",
    "model_sum = ols(\"balance ~ C(hand, Sum(omit='bad'))\", data=df.to_pandas())\n",
    "results_sum = model_sum.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.],\n",
       "       [ 1., -1., -1.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print first 10 rows of the design matrix\n",
    "model_sum.exog[:10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 469,
       "width": 617
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the design matrix, adjust names, and add colorbar\n",
    "plot_design_matrix(model_sum,\n",
    "                    plot_names=['Intercept', 'C(hand, Sum)[S.good]', 'C(hand, Sum)[S.neutral]'], cbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Critically for valid ANOVA tests the sum over rows of this scheme adds up to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([300.,   0.,   0.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_sum.exog.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this to our treatment/dummy coding which did not!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([300., 100., 100.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_a.exog.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Parameters\n",
    "\n",
    "In this coding scheme parameters represent:\n",
    "\n",
    "- Intercept = the grand-mean of `balance` over all levels of `hand`\n",
    "- $\\beta_1$ = difference between the mean of `hand = 'good'` and the grand-mean\n",
    "- $\\beta_2$ = difference between the mean of `hand = 'neutral'` and the grand-mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                balance   R-squared:                       0.338\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "No. Observations:                 300   F-statistic:                     75.70\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           2.70e-27\n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "Intercept                               9.7715      0.237     41.165      0.000       9.304      10.239\n",
      "C(hand, Sum(omit='bad'))[S.good]        3.2549      0.336      9.696      0.000       2.594       3.916\n",
      "C(hand, Sum(omit='bad'))[S.neutral]     0.5751      0.336      1.713      0.088      -0.086       1.236\n",
      "=======================================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results_sum.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "\n",
    "Using the mean for each level of `hand` you calculated earlier and any other values you need to calculate, use the means to recreate what each parameter estimate represents\n",
    "\n",
    "Use `print` in Python or a markdown cell to write out your explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept = Grand-mean = 9.772\n",
      "B1 = Good - Grand-mean = 3.255\n",
      "B2 = Neutral - Grand-mean = 0.575\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "grand_mean = np.mean([bad, good, neutral])\n",
    "\n",
    "print(f\"Intercept = Grand-mean = {grand_mean:.3f}\")\n",
    "print(f\"B1 = Good - Grand-mean = {good - grand_mean:.3f}\")\n",
    "print(f\"B2 = Neutral - Grand-mean = {neutral - grand_mean:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Orthogonal Polynomial Coding\n",
    "\n",
    "This form of coding uses the intercept to estimate the **grand-mean** (mean of means) of all levels and uses additional parameters to calculate polynomial trends across levels of the categorical variable, i.e. *linear*, *quadratic*, *cubic*, etc.\n",
    "\n",
    "We can use this coding scheme with `C(Student, Poly)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate model\n",
    "model_poly = ols(\"balance ~ C(hand, Poly)\", data=df.to_pandas())\n",
    "results_poly = model_poly.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829],\n",
       "       [ 1.        , -0.70710678,  0.40824829]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print first 10 rows of the design matrix\n",
    "model_poly.exog[:10, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 617
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the design matrix\n",
    "plot_design_matrix(model_poly, cbar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like sum/deviation coding, polynomial contrasts are for valid ANOVA tests as the sum over rows of this scheme adds up to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.00000000e+02, -5.06586146e-15, -1.55653268e-13])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_poly.exog.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Parameters\n",
    "\n",
    "In this coding scheme parameters represent:\n",
    "\n",
    "- Intercept = the grand-mean of `balance` over all levels of `hand`\n",
    "- $\\beta_1$ = linear trend over levels of `hand`\n",
    "- $\\beta_2$ = quadratic trend over levels of `hand`\n",
    "\n",
    "To see this more clearly let's visualize how `Poly` is *encoding* levels of `hand`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Poly function which we don't normally use directly\n",
    "from patsy.contrasts import Poly\n",
    "\n",
    "# Generate a matrix using our predictor variables\n",
    "poly_codes = Poly().code_without_intercept(model_poly.exog_names).matrix\n",
    "\n",
    "poly_codes = pl.DataFrame(poly_codes, \n",
    "                          schema=['linear', 'quadratic']).with_columns(\n",
    "                              hand = np.array(['bad', 'good','neutral']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The matrix above is like a \"mini\" **design matrix**  mapping the levels of `hand` to the polynomial value\n",
    "\n",
    "Visually if we think about each level of `hand` on the x-axis below we can see how the polynomial value changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 559
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(poly_codes[:, 0], label='Linear')\n",
    "plt.plot(poly_codes[:, 1], label='Quadtratic')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                balance   R-squared:                       0.338\n",
      "Model:                            OLS   Adj. R-squared:                  0.333\n",
      "No. Observations:                 300   F-statistic:                     75.70\n",
      "Covariance Type:            nonrobust   Prob (F-statistic):           2.70e-27\n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "Intercept                   9.7715      0.237     41.165      0.000       9.304      10.239\n",
      "C(hand, Poly).Linear        3.1149      0.411      7.576      0.000       2.306       3.924\n",
      "C(hand, Poly).Quadratic    -3.9864      0.411     -9.696      0.000      -4.796      -3.177\n",
      "===========================================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(results_poly.summary(slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "\n",
    "Using the mean for each level of `hand` you calculated earlier and the `poly_codes` matrix below try to recreate the value for the linear and quadratic estimates above.\n",
    "\n",
    "*Hint: Think about using `np.dot`...*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>linear</th><th>quadratic</th><th>hand</th></tr><tr><td>f64</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>-0.707107</td><td>0.408248</td><td>&quot;bad&quot;</td></tr><tr><td>-1.6940e-17</td><td>-0.816497</td><td>&quot;good&quot;</td></tr><tr><td>0.707107</td><td>0.408248</td><td>&quot;neutral&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 3)\n",
       "┌─────────────┬───────────┬─────────┐\n",
       "│ linear      ┆ quadratic ┆ hand    │\n",
       "│ ---         ┆ ---       ┆ ---     │\n",
       "│ f64         ┆ f64       ┆ str     │\n",
       "╞═════════════╪═══════════╪═════════╡\n",
       "│ -0.707107   ┆ 0.408248  ┆ bad     │\n",
       "│ -1.6940e-17 ┆ -0.816497 ┆ good    │\n",
       "│ 0.707107    ┆ 0.408248  ┆ neutral │\n",
       "└─────────────┴───────────┴─────────┘"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the first 2 columns of this matrix\n",
    "poly_codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear contrast: 3.115\n",
      "Quadratic contrast: -3.986\n"
     ]
    }
   ],
   "source": [
    "# Solution\n",
    "linear_codes = poly_codes[:, 0].to_numpy()\n",
    "linear_contrast = np.dot([bad, good, neutral], linear_codes)\n",
    "\n",
    "quadratic_codes = poly_codes[:, 1].to_numpy()\n",
    "quadratic_contrast = np.dot([bad, good, neutral], quadratic_codes)\n",
    "\n",
    "print(f\"Linear contrast: {linear_contrast:.3f}\")\n",
    "print(f\"Quadratic contrast: {quadratic_contrast:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "201b",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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