{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robust Linear Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "np.random.seed(1234) # for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation\n",
    "\n",
    "Load data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "data = sm.datasets.stackloss.load()\n",
    "data.exog = sm.add_constant(data.exog)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huber's T norm with the (default) median absolute deviation scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const       -41.026498\n",
      "AIRFLOW       0.829384\n",
      "WATERTEMP     0.926066\n",
      "ACIDCONC     -0.127847\n",
      "dtype: float64\n",
      "const        9.791899\n",
      "AIRFLOW      0.111005\n",
      "WATERTEMP    0.302930\n",
      "ACIDCONC     0.128650\n",
      "dtype: float64\n",
      "                    Robust linear Model Regression Results                    \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                   21\n",
      "Model:                            RLM   Df Residuals:                       17\n",
      "Method:                          IRLS   Df Model:                            3\n",
      "Norm:                          HuberT                                         \n",
      "Scale Est.:                       mad                                         \n",
      "Cov Type:                          H1                                         \n",
      "Date:                Fri, 20 Mar 2026                                         \n",
      "Time:                        11:18:46                                         \n",
      "No. Iterations:                    19                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "var_0        -41.0265      9.792     -4.190      0.000     -60.218     -21.835\n",
      "var_1          0.8294      0.111      7.472      0.000       0.612       1.047\n",
      "var_2          0.9261      0.303      3.057      0.002       0.332       1.520\n",
      "var_3         -0.1278      0.129     -0.994      0.320      -0.380       0.124\n",
      "==============================================================================\n",
      "\n",
      "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n"
     ]
    }
   ],
   "source": [
    "huber_t = sm.RLM(data.endog, data.exog, M=sm.robust.norms.HuberT())\n",
    "hub_results = huber_t.fit()\n",
    "print(hub_results.params)\n",
    "print(hub_results.bse)\n",
    "print(\n",
    "    hub_results.summary(\n",
    "        yname=\"y\", xname=[\"var_%d\" % i for i in range(len(hub_results.params))]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huber's T norm with 'H2' covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const       -41.026498\n",
      "AIRFLOW       0.829384\n",
      "WATERTEMP     0.926066\n",
      "ACIDCONC     -0.127847\n",
      "dtype: float64\n",
      "const        9.089504\n",
      "AIRFLOW      0.119460\n",
      "WATERTEMP    0.322355\n",
      "ACIDCONC     0.117963\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "hub_results2 = huber_t.fit(cov=\"H2\")\n",
    "print(hub_results2.params)\n",
    "print(hub_results2.bse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Andrew's Wave norm with Huber's Proposal 2 scaling and 'H3' covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  const       -40.881796\n",
      "AIRFLOW       0.792761\n",
      "WATERTEMP     1.048576\n",
      "ACIDCONC     -0.133609\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "andrew_mod = sm.RLM(data.endog, data.exog, M=sm.robust.norms.AndrewWave())\n",
    "andrew_results = andrew_mod.fit(scale_est=sm.robust.scale.HuberScale(), cov=\"H3\")\n",
    "print(\"Parameters: \", andrew_results.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See ``help(sm.RLM.fit)`` for more options and ``module sm.robust.scale`` for scale options\n",
    "\n",
    "## Comparing OLS and RLM\n",
    "\n",
    "Artificial data with outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "nsample = 50\n",
    "x1 = np.linspace(0, 20, nsample)\n",
    "X = np.column_stack((x1, (x1 - 5) ** 2))\n",
    "X = sm.add_constant(X)\n",
    "sig = 0.3  # smaller error variance makes OLS<->RLM contrast bigger\n",
    "beta = [5, 0.5, -0.0]\n",
    "y_true2 = np.dot(X, beta)\n",
    "y2 = y_true2 + sig * 1.0 * np.random.normal(size=nsample)\n",
    "y2[[39, 41, 43, 45, 48]] -= 5  # add some outliers (10% of nsample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1: quadratic function with linear truth\n",
    "\n",
    "Note that the quadratic term in OLS regression will capture outlier effects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.05665597  0.5275415  -0.0135642 ]\n",
      "[0.46154403 0.07125617 0.00630507]\n",
      "[ 4.717551    4.98597838  5.24988624  5.50927459  5.76414342  6.01449273\n",
      "  6.26032253  6.50163281  6.73842358  6.97069482  7.19844655  7.42167877\n",
      "  7.64039147  7.85458465  8.06425831  8.26941246  8.4700471   8.66616221\n",
      "  8.85775781  9.04483389  9.22739046  9.40542751  9.57894504  9.74794306\n",
      "  9.91242156 10.07238055 10.22782001 10.37873997 10.5251404  10.66702132\n",
      " 10.80438272 10.93722461 11.06554698 11.18934983 11.30863316 11.42339698\n",
      " 11.53364129 11.63936607 11.74057135 11.8372571  11.92942334 12.01707006\n",
      " 12.10019726 12.17880495 12.25289312 12.32246178 12.38751092 12.44804054\n",
      " 12.50405064 12.55554123]\n"
     ]
    }
   ],
   "source": [
    "res = sm.OLS(y2, X).fit()\n",
    "print(res.params)\n",
    "print(res.bse)\n",
    "print(res.predict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate RLM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.01422175e+00  5.08736800e-01 -2.21950568e-03]\n",
      "[0.12351938 0.01906972 0.00168738]\n"
     ]
    }
   ],
   "source": [
    "resrlm = sm.RLM(y2, X).fit()\n",
    "print(resrlm.params)\n",
    "print(resrlm.bse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a plot to compare OLS estimates to the robust estimates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xadde5de1e8ed>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x1, y2, \"o\", label=\"data\")\n",
    "ax.plot(x1, y_true2, \"b-\", label=\"True\")\n",
    "pred_ols = res.get_prediction()\n",
    "iv_l = pred_ols.summary_frame()[\"obs_ci_lower\"]\n",
    "iv_u = pred_ols.summary_frame()[\"obs_ci_upper\"]\n",
    "\n",
    "ax.plot(x1, res.fittedvalues, \"r-\", label=\"OLS\")\n",
    "ax.plot(x1, iv_u, \"r--\")\n",
    "ax.plot(x1, iv_l, \"r--\")\n",
    "ax.plot(x1, resrlm.fittedvalues, \"g.-\", label=\"RLM\")\n",
    "ax.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2: linear function with linear truth\n",
    "\n",
    "Fit a new OLS model using only the linear term and the constant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.60337623 0.39189951]\n",
      "[0.39957397 0.03442891]\n"
     ]
    }
   ],
   "source": [
    "X2 = X[:, [0, 1]]\n",
    "res2 = sm.OLS(y2, X2).fit()\n",
    "print(res2.params)\n",
    "print(res2.bse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate RLM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.09270067 0.49009652]\n",
      "[0.09491513 0.00817827]\n"
     ]
    }
   ],
   "source": [
    "resrlm2 = sm.RLM(y2, X2).fit()\n",
    "print(resrlm2.params)\n",
    "print(resrlm2.bse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a plot to compare OLS estimates to the robust estimates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_ols = res2.get_prediction()\n",
    "iv_l = pred_ols.summary_frame()[\"obs_ci_lower\"]\n",
    "iv_u = pred_ols.summary_frame()[\"obs_ci_upper\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(x1, y2, \"o\", label=\"data\")\n",
    "ax.plot(x1, y_true2, \"b-\", label=\"True\")\n",
    "ax.plot(x1, res2.fittedvalues, \"r-\", label=\"OLS\")\n",
    "ax.plot(x1, iv_u, \"r--\")\n",
    "ax.plot(x1, iv_l, \"r--\")\n",
    "ax.plot(x1, resrlm2.fittedvalues, \"g.-\", label=\"RLM\")\n",
    "legend = ax.legend(loc=\"best\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
