{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "intro-md",
   "metadata": {},
   "source": [
    "# Case Study: Teacher Effectiveness Ratings (CLMM)\n",
    "\n",
    "This notebook demonstrates **cumulative link mixed models (CLMM)** using `interlace.clmm()` — the Python equivalent of R's `ordinal::clmm()`. CLMMs are the right tool when your outcome is an **ordered categorical variable** (Likert scale, pain ratings, quality grades) and observations are clustered.\n",
    "\n",
    "## Scenario\n",
    "\n",
    "We analyse synthetic **teacher effectiveness ratings** collected from students. The design:\n",
    "\n",
    "| Feature | Value |\n",
    "|---|---|\n",
    "| Teachers | 20 |\n",
    "| Students per teacher | 10 |\n",
    "| Total observations | 200 |\n",
    "| Outcome | 5-point rating (1 = poor … 5 = excellent) |\n",
    "| Fixed effects | `method` (traditional / active), `size` (large / small) |\n",
    "| Random effect | Teacher intercept |\n",
    "| Design | Balanced 2×2, 5 teachers per cell |\n",
    "\n",
    "Both predictors vary **at the teacher level** (not within teacher), so the teacher random effect absorbs unmeasured instructor-level heterogeneity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "data-gen",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:41.314010Z",
     "iopub.status.busy": "2026-05-05T08:15:41.313880Z",
     "iopub.status.idle": "2026-05-05T08:15:42.413485Z",
     "shell.execute_reply": "2026-05-05T08:15:42.413030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (200, 4)\n",
      "\n",
      "Rating distribution:\n",
      "rating\n",
      "1    23\n",
      "2    52\n",
      "3    43\n",
      "4    42\n",
      "5    40\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import interlace\n",
    "from scipy.special import expit\n",
    "from plotnine import (\n",
    "    ggplot, aes, geom_bar, geom_point, geom_errorbar, geom_line,\n",
    "    facet_grid, facet_wrap, coord_flip, labs, theme_bw, theme,\n",
    "    scale_fill_manual, scale_color_manual, scale_linetype_manual,\n",
    "    element_text, element_blank, geom_hline, scale_y_continuous,\n",
    ")\n",
    "\n",
    "# True parameters\n",
    "TRUE_THRESHOLDS = np.array([-2.0, -0.5, 0.8, 2.1])\n",
    "TRUE_BETA_METHOD = 1.2   # active → higher ratings\n",
    "TRUE_BETA_SIZE = -0.3    # small → slightly higher ratings\n",
    "TRUE_SD_TEACHER = 0.8\n",
    "\n",
    "rng = np.random.default_rng(42)\n",
    "\n",
    "conditions = (\n",
    "    [('traditional', 'large')] * 5 + [('traditional', 'small')] * 5 +\n",
    "    [('active', 'large')] * 5 + [('active', 'small')] * 5\n",
    ")\n",
    "u_teacher = rng.normal(0, TRUE_SD_TEACHER, 20)\n",
    "\n",
    "rows = []\n",
    "for j in range(20):\n",
    "    method, size = conditions[j]\n",
    "    eta = (TRUE_BETA_METHOD * (method == 'active')\n",
    "           + TRUE_BETA_SIZE * (size == 'small')\n",
    "           + u_teacher[j])\n",
    "    cum_probs = expit(TRUE_THRESHOLDS - eta)\n",
    "    cat_probs = np.diff(np.concatenate([[0], cum_probs, [1]]))\n",
    "    cat_probs = np.clip(cat_probs, 1e-10, None)\n",
    "    cat_probs /= cat_probs.sum()\n",
    "    ratings = rng.choice(np.arange(1, 6), size=10, p=cat_probs)\n",
    "    for r in ratings:\n",
    "        rows.append({'teacher': f'T{j+1:02d}', 'method': method,\n",
    "                     'size': size, 'rating': int(r)})\n",
    "\n",
    "df = pd.DataFrame(rows)\n",
    "print(f\"Shape: {df.shape}\")\n",
    "print(\"\\nRating distribution:\")\n",
    "print(df['rating'].value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "data-head",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:42.414794Z",
     "iopub.status.busy": "2026-05-05T08:15:42.414701Z",
     "iopub.status.idle": "2026-05-05T08:15:42.420415Z",
     "shell.execute_reply": "2026-05-05T08:15:42.420119Z"
    }
   },
   "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>teacher</th>\n",
       "      <th>method</th>\n",
       "      <th>size</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>T01</td>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  teacher       method   size  rating\n",
       "0     T01  traditional  large       4\n",
       "1     T01  traditional  large       3\n",
       "2     T01  traditional  large       5\n",
       "3     T01  traditional  large       5\n",
       "4     T01  traditional  large       4\n",
       "5     T01  traditional  large       2\n",
       "6     T01  traditional  large       3\n",
       "7     T01  traditional  large       1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda-md",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "We expect active-method teachers to receive higher ratings, and small-class teachers to receive slightly higher ratings. The stacked proportional bar chart shows the rating distribution for each condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eda-plot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:42.421642Z",
     "iopub.status.busy": "2026-05-05T08:15:42.421576Z",
     "iopub.status.idle": "2026-05-05T08:15:42.866293Z",
     "shell.execute_reply": "2026-05-05T08:15:42.865796Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<plotnine.ggplot.ggplot object at 0x12124a030>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ORDINAL_PALETTE = ['#d73027', '#fc8d59', '#fee090', '#91bfdb', '#4575b4']\n",
    "\n",
    "(\n",
    "    ggplot(df, aes(x='method', fill='factor(rating)'))\n",
    "    + geom_bar(position='fill')\n",
    "    + facet_wrap('size', labeller='label_both')\n",
    "    + scale_fill_manual(values=ORDINAL_PALETTE, name='Rating')\n",
    "    + scale_y_continuous(labels=lambda l: [f\"{v:.0%}\" for v in l])\n",
    "    + labs(\n",
    "        title='Rating distribution by teaching method and class size',\n",
    "        x='Method', y='Proportion',\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + theme(figure_size=(8, 4))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "model-spec-md",
   "metadata": {},
   "source": [
    "## Model Specification\n",
    "\n",
    "The cumulative link mixed model (proportional-odds formulation) is:\n",
    "\n",
    "$$\n",
    "\\operatorname{logit}[P(Y_{ij} \\leq k)] = \\alpha_k - (\\beta_1 \\cdot \\text{method}_j + \\beta_2 \\cdot \\text{size}_j + u_j)\n",
    "$$\n",
    "\n",
    "where $\\alpha_1 < \\alpha_2 < \\alpha_3 < \\alpha_4$ are the **threshold** parameters and $u_j \\sim \\mathcal{N}(0, \\sigma^2)$ is the teacher random intercept.\n",
    "\n",
    "The `interlace.clmm()` call mirrors `statsmodels.MixedLM.from_formula()`: a Wilkinson–Rogers formula for fixed effects, plus `groups=` for the clustering variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fit",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:42.867702Z",
     "iopub.status.busy": "2026-05-05T08:15:42.867593Z",
     "iopub.status.idle": "2026-05-05T08:15:43.793352Z",
     "shell.execute_reply": "2026-05-05T08:15:43.792982Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converged : True\n",
      "N obs     : 200\n",
      "AIC       : 595.22\n",
      "BIC       : 618.30\n"
     ]
    }
   ],
   "source": [
    "result = interlace.clmm(\n",
    "    formula=\"rating ~ method + size\",\n",
    "    data=df,\n",
    "    groups=\"teacher\",\n",
    "    link=\"logit\",\n",
    ")\n",
    "\n",
    "print(f\"Converged : {result.converged}\")\n",
    "print(f\"N obs     : {result.nobs}\")\n",
    "print(f\"AIC       : {result.aic:.2f}\")\n",
    "print(f\"BIC       : {result.bic:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "thresholds",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:43.794653Z",
     "iopub.status.busy": "2026-05-05T08:15:43.794576Z",
     "iopub.status.idle": "2026-05-05T08:15:43.799358Z",
     "shell.execute_reply": "2026-05-05T08:15:43.799004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thresholds (logit scale — more negative = lower cumulative probability):\n"
     ]
    },
    {
     "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>threshold</th>\n",
       "      <th>estimate</th>\n",
       "      <th>true</th>\n",
       "      <th>abs_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1|2</td>\n",
       "      <td>-3.730</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2|3</td>\n",
       "      <td>-1.927</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3|4</td>\n",
       "      <td>-0.829</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4|5</td>\n",
       "      <td>0.409</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  threshold  estimate  true  abs_error\n",
       "0       1|2    -3.730  -2.0      1.730\n",
       "1       2|3    -1.927  -0.5      1.427\n",
       "2       3|4    -0.829   0.8      1.629\n",
       "3       4|5     0.409   2.1      1.691"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thr = result.thresholds\n",
    "thr_keys = list(thr.keys())\n",
    "thr_values = np.array(list(thr.values()))\n",
    "thr_df = pd.DataFrame({\n",
    "    'threshold': thr_keys,\n",
    "    'estimate': thr_values,\n",
    "    'true': TRUE_THRESHOLDS,\n",
    "    'abs_error': np.abs(thr_values - TRUE_THRESHOLDS),\n",
    "})\n",
    "print(\"Thresholds (logit scale — more negative = lower cumulative probability):\")\n",
    "thr_df.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe-table",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:43.800707Z",
     "iopub.status.busy": "2026-05-05T08:15:43.800630Z",
     "iopub.status.idle": "2026-05-05T08:15:43.806829Z",
     "shell.execute_reply": "2026-05-05T08:15:43.806504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed effects (OR = odds ratio for moving to a higher rating category):\n"
     ]
    },
    {
     "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>estimate</th>\n",
       "      <th>SE</th>\n",
       "      <th>OR</th>\n",
       "      <th>CI_lower</th>\n",
       "      <th>CI_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>method[T.traditional]</th>\n",
       "      <td>-1.735</td>\n",
       "      <td>0.351</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size[T.small]</th>\n",
       "      <td>-0.830</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       estimate     SE     OR  CI_lower  CI_upper\n",
       "method[T.traditional]    -1.735  0.351  0.176     0.089     0.351\n",
       "size[T.small]            -0.830  0.338  0.436     0.225     0.846"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ci = result.confint()          # DataFrame indexed by param name\n",
    "# Keep only fixed-effect rows (exclude threshold rows)\n",
    "fe_names = list(result.fe_params.index)\n",
    "fe_ci = ci.loc[fe_names]\n",
    "\n",
    "fe_table = pd.DataFrame({\n",
    "    'estimate': result.fe_params,\n",
    "    'SE':       result.fe_bse[fe_names],\n",
    "    'OR':       np.exp(result.fe_params),\n",
    "    'CI_lower': np.exp(fe_ci.iloc[:, 0]),\n",
    "    'CI_upper': np.exp(fe_ci.iloc[:, 1]),\n",
    "}, index=fe_names)\n",
    "\n",
    "print(\"Fixed effects (OR = odds ratio for moving to a higher rating category):\")\n",
    "fe_table.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "predictions",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:43.808102Z",
     "iopub.status.busy": "2026-05-05T08:15:43.808019Z",
     "iopub.status.idle": "2026-05-05T08:15:43.815568Z",
     "shell.execute_reply": "2026-05-05T08:15:43.815207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted category probabilities for an average new teacher (u=0):\n"
     ]
    },
    {
     "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>method</th>\n",
       "      <th>size</th>\n",
       "      <th>P(1)</th>\n",
       "      <th>P(2)</th>\n",
       "      <th>P(3)</th>\n",
       "      <th>P(4)</th>\n",
       "      <th>P(5)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>traditional</td>\n",
       "      <td>large</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>traditional</td>\n",
       "      <td>small</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.196</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>active</td>\n",
       "      <td>large</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.297</td>\n",
       "      <td>0.399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>active</td>\n",
       "      <td>small</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        method   size   P(1)   P(2)   P(3)   P(4)   P(5)\n",
       "0  traditional  large  0.120  0.332  0.260  0.183  0.105\n",
       "1  traditional  small  0.238  0.417  0.196  0.101  0.049\n",
       "2       active  large  0.023  0.104  0.177  0.297  0.399\n",
       "3       active  small  0.052  0.198  0.250  0.275  0.225"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_grid = pd.DataFrame({\n",
    "    'method':  ['traditional', 'traditional', 'active', 'active'],\n",
    "    'size':    ['large',       'small',       'large',  'small'],\n",
    "    'teacher': ['T_new'] * 4,\n",
    "})\n",
    "\n",
    "probs = result.predict(newdata=pred_grid, type=\"prob\")\n",
    "\n",
    "prob_df = pred_grid[['method', 'size']].copy()\n",
    "for k in range(1, 6):\n",
    "    prob_df[f'P({k})'] = probs[:, k - 1].round(3)\n",
    "\n",
    "print(\"Predicted category probabilities for an average new teacher (u=0):\")\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cum-prob-plot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:43.816645Z",
     "iopub.status.busy": "2026-05-05T08:15:43.816579Z",
     "iopub.status.idle": "2026-05-05T08:15:43.919727Z",
     "shell.execute_reply": "2026-05-05T08:15:43.919346Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<plotnine.ggplot.ggplot object at 0x123633e70>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 700
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build cumulative probability curves P(Y <= k) for k=1..4\n",
    "cum_rows = []\n",
    "for _, row in prob_df.iterrows():\n",
    "    cond = f\"{row['method']}/{row['size']}\"\n",
    "    cump = 0.0\n",
    "    for k in range(1, 5):\n",
    "        cump += row[f'P({k})']\n",
    "        cum_rows.append({'condition': cond, 'k': k, 'cum_prob': cump,\n",
    "                         'method': row['method'], 'size': row['size']})\n",
    "\n",
    "cum_df = pd.DataFrame(cum_rows)\n",
    "\n",
    "(\n",
    "    ggplot(cum_df, aes(x='k', y='cum_prob',\n",
    "                       color='method', linetype='size', group='condition'))\n",
    "    + geom_line(size=1.1)\n",
    "    + geom_point(size=3)\n",
    "    + scale_color_manual(values=['#d73027', '#4575b4'], name='Method')\n",
    "    + scale_linetype_manual(values=['solid', 'dashed'], name='Size')\n",
    "    + scale_y_continuous(limits=[0, 1],\n",
    "                         labels=lambda l: [f\"{v:.0%}\" for v in l])\n",
    "    + labs(\n",
    "        title='Cumulative rating probabilities for a new teacher',\n",
    "        x='Rating threshold k', y='P(Y \\u2264 k)',\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + theme(figure_size=(7, 4))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "blup-plot",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:43.920906Z",
     "iopub.status.busy": "2026-05-05T08:15:43.920831Z",
     "iopub.status.idle": "2026-05-05T08:15:44.116455Z",
     "shell.execute_reply": "2026-05-05T08:15:44.116040Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unknown name 'grey60' for a color.",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/mizani/_colors/named_colors.py:20\u001b[39m, in \u001b[36m_color_lookup.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m     19\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m20\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     21\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[31mKeyError\u001b[39m: 'grey60'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/IPython/core/formatters.py:1036\u001b[39m, in \u001b[36mMimeBundleFormatter.__call__\u001b[39m\u001b[34m(self, obj, include, exclude)\u001b[39m\n\u001b[32m   1033\u001b[39m     method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n\u001b[32m   1035\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1036\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43minclude\u001b[49m\u001b[43m=\u001b[49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[43m=\u001b[49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1037\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1038\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/ggplot.py:172\u001b[39m, in \u001b[36mggplot._repr_mimebundle_\u001b[39m\u001b[34m(self, include, exclude)\u001b[39m\n\u001b[32m    169\u001b[39m     \u001b[38;5;28mself\u001b[39m.theme = \u001b[38;5;28mself\u001b[39m.theme.to_retina()\n\u001b[32m    171\u001b[39m buf = BytesIO()\n\u001b[32m--> \u001b[39m\u001b[32m172\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mpng\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mretina\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m    173\u001b[39m figure_size_px = \u001b[38;5;28mself\u001b[39m.theme._figure_size_px\n\u001b[32m    174\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m get_mimebundle(buf.getvalue(), \u001b[38;5;28mformat\u001b[39m, figure_size_px)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/ggplot.py:681\u001b[39m, in \u001b[36mggplot.save\u001b[39m\u001b[34m(self, filename, format, path, width, height, units, dpi, limitsize, verbose, **kwargs)\u001b[39m\n\u001b[32m    632\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msave\u001b[39m(\n\u001b[32m    633\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m    634\u001b[39m     filename: Optional[\u001b[38;5;28mstr\u001b[39m | Path | BytesIO] = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   (...)\u001b[39m\u001b[32m    643\u001b[39m     **kwargs: Any,\n\u001b[32m    644\u001b[39m ):\n\u001b[32m    645\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    646\u001b[39m \u001b[33;03m    Save a ggplot object as an image file\u001b[39;00m\n\u001b[32m    647\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    679\u001b[39m \u001b[33;03m        Additional arguments to pass to matplotlib `savefig()`.\u001b[39;00m\n\u001b[32m    680\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m681\u001b[39m     sv = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msave_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    682\u001b[39m \u001b[43m        \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    683\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    684\u001b[39m \u001b[43m        \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m=\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    685\u001b[39m \u001b[43m        \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    686\u001b[39m \u001b[43m        \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m=\u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    687\u001b[39m \u001b[43m        \u001b[49m\u001b[43munits\u001b[49m\u001b[43m=\u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    688\u001b[39m \u001b[43m        \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m=\u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    689\u001b[39m \u001b[43m        \u001b[49m\u001b[43mlimitsize\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlimitsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    690\u001b[39m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    691\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    692\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    694\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m plot_context(\u001b[38;5;28mself\u001b[39m).rc_context:\n\u001b[32m    695\u001b[39m         sv.figure.savefig(**sv.kwargs)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/ggplot.py:629\u001b[39m, in \u001b[36mggplot.save_helper\u001b[39m\u001b[34m(self, filename, format, path, width, height, units, dpi, limitsize, verbose, **kwargs)\u001b[39m\n\u001b[32m    626\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m dpi \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    627\u001b[39m     \u001b[38;5;28mself\u001b[39m.theme = \u001b[38;5;28mself\u001b[39m.theme + theme(dpi=dpi)\n\u001b[32m--> \u001b[39m\u001b[32m629\u001b[39m figure = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshow\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m    630\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m mpl_save_view(figure, fig_kwargs)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/ggplot.py:314\u001b[39m, in \u001b[36mggplot.draw\u001b[39m\u001b[34m(self, show)\u001b[39m\n\u001b[32m    311\u001b[39m \u001b[38;5;28mself\u001b[39m.theme.setup(\u001b[38;5;28mself\u001b[39m)\n\u001b[32m    313\u001b[39m \u001b[38;5;66;03m# Drawing\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_draw_layers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    315\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_panel_borders()\n\u001b[32m    316\u001b[39m \u001b[38;5;28mself\u001b[39m._draw_breaks_and_labels()\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/ggplot.py:461\u001b[39m, in \u001b[36mggplot._draw_layers\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    457\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    458\u001b[39m \u001b[33;03mDraw the main plot(s) onto the axes.\u001b[39;00m\n\u001b[32m    459\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    460\u001b[39m \u001b[38;5;66;03m# Draw the geoms\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m461\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlayers\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcoordinates\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/layer.py:481\u001b[39m, in \u001b[36mLayers.draw\u001b[39m\u001b[34m(self, layout, coord)\u001b[39m\n\u001b[32m    479\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdraw\u001b[39m(\u001b[38;5;28mself\u001b[39m, layout: Layout, coord: coord):\n\u001b[32m    480\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m481\u001b[39m         \u001b[43ml\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoord\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/layer.py:377\u001b[39m, in \u001b[36mlayer.draw\u001b[39m\u001b[34m(self, layout, coord)\u001b[39m\n\u001b[32m    374\u001b[39m \u001b[38;5;28mself\u001b[39m.data = \u001b[38;5;28mself\u001b[39m.geom.handle_na(\u001b[38;5;28mself\u001b[39m.data)\n\u001b[32m    375\u001b[39m \u001b[38;5;66;03m# At this point each layer must have the data\u001b[39;00m\n\u001b[32m    376\u001b[39m \u001b[38;5;66;03m# that is created by the plot build process\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m377\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgeom\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoord\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/geoms/geom.py:326\u001b[39m, in \u001b[36mgeom.draw_layer\u001b[39m\u001b[34m(self, data, layout, coord)\u001b[39m\n\u001b[32m    324\u001b[39m panel_params = layout.panel_params[ploc]\n\u001b[32m    325\u001b[39m ax = layout.axs[ploc]\n\u001b[32m--> \u001b[39m\u001b[32m326\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdraw_panel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpanel_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/geoms/geom_hline.py:96\u001b[39m, in \u001b[36mgeom_hline.draw_panel\u001b[39m\u001b[34m(self, data, panel_params, coord, ax)\u001b[39m\n\u001b[32m     94\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m _, gdata \u001b[38;5;129;01min\u001b[39;00m data.groupby(\u001b[33m\"\u001b[39m\u001b[33mgroup\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m     95\u001b[39m     gdata.reset_index(inplace=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m96\u001b[39m     \u001b[43mgeom_segment\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdraw_group\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     97\u001b[39m \u001b[43m        \u001b[49m\u001b[43mgdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpanel_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparams\u001b[49m\n\u001b[32m     98\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/plotnine/geoms/geom_segment.py:73\u001b[39m, in \u001b[36mgeom_segment.draw_group\u001b[39m\u001b[34m(data, panel_params, coord, ax, params)\u001b[39m\n\u001b[32m     71\u001b[39m data = coord.transform(data, panel_params)\n\u001b[32m     72\u001b[39m linewidth = data[\u001b[33m\"\u001b[39m\u001b[33msize\u001b[39m\u001b[33m\"\u001b[39m] * SIZE_FACTOR\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m color = \u001b[43mto_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcolor\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43malpha\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     75\u001b[39m \u001b[38;5;66;03m# start point -> end point, sequence of xy points\u001b[39;00m\n\u001b[32m     76\u001b[39m \u001b[38;5;66;03m# from which line segments are created\u001b[39;00m\n\u001b[32m     77\u001b[39m x = interleave(data[\u001b[33m\"\u001b[39m\u001b[33mx\u001b[39m\u001b[33m\"\u001b[39m], data[\u001b[33m\"\u001b[39m\u001b[33mxend\u001b[39m\u001b[33m\"\u001b[39m])\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/mizani/_colors/utils.py:130\u001b[39m, in \u001b[36mto_rgba\u001b[39m\u001b[34m(colors, alpha)\u001b[39m\n\u001b[32m    128\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m [to_rgba(c, alpha) \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m colors]  \u001b[38;5;66;03m# pyright: ignore[reportCallIssue,reportArgumentType]\u001b[39;00m\n\u001b[32m    129\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m130\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mto_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m c, a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(colors, alpha)]\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/mizani/_colors/utils.py:110\u001b[39m, in \u001b[36mto_rgba\u001b[39m\u001b[34m(colors, alpha)\u001b[39m\n\u001b[32m    107\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mnone\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    109\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(alpha, (\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mint\u001b[39m, np.floating, np.integer)):\n\u001b[32m--> \u001b[39m\u001b[32m110\u001b[39m     c = \u001b[43mget_named_color\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    111\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(c) > \u001b[32m7\u001b[39m:\n\u001b[32m    112\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m c\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/mizani/_colors/named_colors.py:72\u001b[39m, in \u001b[36mget_named_color\u001b[39m\u001b[34m(name)\u001b[39m\n\u001b[32m     70\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m name\n\u001b[32m     71\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m72\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNAMED_COLORS\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/repos/gpg/interlace/.venv/lib/python3.14/site-packages/mizani/_colors/named_colors.py:22\u001b[39m, in \u001b[36m_color_lookup.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m     20\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getitem__\u001b[39m(key)\n\u001b[32m     21\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnknown name \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m for a color.\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01merr\u001b[39;00m\n",
      "\u001b[31mValueError\u001b[39m: Unknown name 'grey60' for a color."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<plotnine.ggplot.ggplot object at 0x1237bcbb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teacher BLUPs with ±1.96 * teacher SD uncertainty bands\n",
    "blups_series = result.random_effects['teacher']\n",
    "teacher_sd = float(result.variance_components['teacher']) ** 0.5\n",
    "half_width = 1.96 * teacher_sd\n",
    "\n",
    "blup_df = pd.DataFrame({\n",
    "    'teacher': blups_series.index,\n",
    "    'blup': blups_series.values,\n",
    "}).sort_values('blup').reset_index(drop=True)\n",
    "\n",
    "# Attach method info for colouring\n",
    "teacher_meta = df.drop_duplicates('teacher')[['teacher', 'method']]\n",
    "blup_df = blup_df.merge(teacher_meta, on='teacher')\n",
    "blup_df['teacher'] = pd.Categorical(blup_df['teacher'],\n",
    "                                     categories=blup_df['teacher'].tolist(),\n",
    "                                     ordered=True)\n",
    "\n",
    "(\n",
    "    ggplot(blup_df, aes(x='teacher', y='blup', color='method'))\n",
    "    + geom_hline(yintercept=0, linetype='dashed', color='grey60')\n",
    "    + geom_errorbar(\n",
    "        aes(ymin='blup - ' + str(half_width),\n",
    "            ymax='blup + ' + str(half_width)),\n",
    "        width=0.4,\n",
    "    )\n",
    "    + geom_point(size=3)\n",
    "    + coord_flip()\n",
    "    + scale_color_manual(values=['#d73027', '#4575b4'], name='Method')\n",
    "    + labs(\n",
    "        title='Teacher BLUPs (sorted) with \\u00b11.96\\u00d7SD bands',\n",
    "        x='Teacher', y='Random intercept (logit scale)',\n",
    "    )\n",
    "    + theme_bw()\n",
    "    + theme(figure_size=(6, 7),\n",
    "            axis_text_y=element_text(size=8))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "link-compare",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-05T08:15:44.117711Z",
     "iopub.status.busy": "2026-05-05T08:15:44.117616Z",
     "iopub.status.idle": "2026-05-05T08:15:44.917009Z",
     "shell.execute_reply": "2026-05-05T08:15:44.916501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit vs probit link — fixed effects:\n",
      "                       logit_est  logit_SE  probit_est  probit_SE\n",
      "method[T.traditional]     -1.735     0.351      -1.033      0.198\n",
      "size[T.small]             -0.830     0.338      -0.480      0.193\n",
      "\n",
      "Logit  AIC: 595.22  |  Probit AIC: 594.35\n",
      "(Estimates on probit scale are ~0.59× logit — the π/√3 rescaling)\n"
     ]
    }
   ],
   "source": [
    "result_probit = interlace.clmm(\n",
    "    formula=\"rating ~ method + size\",\n",
    "    data=df,\n",
    "    groups=\"teacher\",\n",
    "    link=\"probit\",\n",
    ")\n",
    "\n",
    "compare = pd.DataFrame({\n",
    "    'logit_est':  result.fe_params.round(3).values,\n",
    "    'logit_SE':   result.fe_bse[fe_names].round(3).values,\n",
    "    'probit_est': result_probit.fe_params.round(3).values,\n",
    "    'probit_SE':  result_probit.fe_bse[fe_names].round(3).values,\n",
    "}, index=fe_names)\n",
    "\n",
    "print(\"Logit vs probit link — fixed effects:\")\n",
    "print(compare.to_string())\n",
    "print(f\"\\nLogit  AIC: {result.aic:.2f}  |  Probit AIC: {result_probit.aic:.2f}\")\n",
    "print(\"(Estimates on probit scale are ~0.59× logit — the π/√3 rescaling)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "summary-md",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "**Workflow checklist for CLMMs with `interlace`:**\n",
    "\n",
    "- [ ] Confirm the outcome is genuinely ordered (not nominal).\n",
    "- [ ] Identify the clustering variable and check group sizes.\n",
    "- [ ] Call `interlace.clmm(formula, data, groups, link=\"logit\")` and verify `result.converged`.\n",
    "- [ ] Inspect thresholds for ordering ($\\alpha_1 < \\alpha_2 < \\ldots$).\n",
    "- [ ] Interpret fixed-effect odds ratios (logit link) or cumulative probability shifts.\n",
    "- [ ] Use `result.predict(newdata, type=\"prob\")` for marginal predictions at new group levels.\n",
    "- [ ] Check the BLUP caterpillar plot — wide spread indicates substantial teacher heterogeneity.\n",
    "- [ ] Compare logit vs probit AIC to confirm link choice.\n",
    "\n",
    "**Where to go next:**\n",
    "\n",
    "- {doc}`clmm-quickstart` — minimal working example and API reference\n",
    "- {doc}`api/clmm` — full parameter and return-value documentation\n",
    "- {doc}`case-study-glmm` — count outcomes (Poisson/NB) with crossed random effects"
   ]
  }
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