{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bda38497",
   "metadata": {},
   "source": [
    "## Hurdle and truncated count models\n",
    "\n",
    "Author: Josef Perktold\n",
    "\n",
    "Statsmodels has now hurdle and truncated count models, added in version 0.14.\n",
    "\n",
    "A hurdle model is composed of a model for zeros and a model for the distribution for counts larger than zero. The zero model is a binary model for a count of zero versus larger than zero. The count model for nonzero counts is a zero truncated count model.\n",
    "\n",
    "Statsmodels currently supports hurdle models with Poisson and Negative Binomial distributions as zero model and as count model. Binary models like Logit, Probit or GLM-Binomial are not yet supported as zero model.\n",
    "The advantage of Poisson-Poisson hurdle is that the standard Poisson model is a special case with equal parameters in both models. This provides a simple Wald test for the hurdle model against the Poisson model.\n",
    "\n",
    "The implemented binary model is a censored model where observations are right censored at one. That means that only 0 or 1 counts are observed.\n",
    "\n",
    "The hurdle model can be estimated by separately estimating the zero model and the count model for the zero truncated data assuming that observations are independently distributed (no correlation across observations). The resulting covariance matrix of the parameter estimates is block diagonal with diagonal blocks given by the submodels.\n",
    "Joint estimation is not yet implemented.\n",
    "\n",
    "The censored and truncated count models were developed mainly to support the hurdle model. However, the left truncated count models have other applications than supporting the hurdle models. The right censored models are not of separate interest because they only support binary observations that can be modeled by GLM-Binomial, Logit or Probit.\n",
    "\n",
    "For the hurdle model there is a single class `HurdleCountModel`, that includes the distributions of the submodels as option. \n",
    "Classes for truncated models are currently `TruncatedLFPoisson` and `TruncatedLFNegativeBinomialP`, where \"LF\" stands for left truncation at a fixed, observation independent truncation point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e6105a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eed890e6",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import statsmodels.discrete.truncated_model as smtc\n",
    "\n",
    "from statsmodels.discrete.discrete_model import (\n",
    "    Poisson, NegativeBinomial, NegativeBinomialP, GeneralizedPoisson)\n",
    "from statsmodels.discrete.count_model import (\n",
    "    ZeroInflatedPoisson,\n",
    "    ZeroInflatedGeneralizedPoisson,\n",
    "    ZeroInflatedNegativeBinomialP\n",
    "    )\n",
    "\n",
    "from statsmodels.discrete.truncated_model import (\n",
    "    TruncatedLFPoisson,\n",
    "    TruncatedLFNegativeBinomialP,\n",
    "    _RCensoredPoisson,\n",
    "    HurdleCountModel,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85da298e",
   "metadata": {},
   "source": [
    "## Simulating a hurdle model\n",
    "\n",
    "We are simulating a Poisson-Poisson hurdle model explicitly because there are not yet any distribution helper functions for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99c1d162",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[102 335 590 770 816 739 573 402 265 176 116  59  35   7  11   4]\n",
      "4898\n",
      "602\n",
      "93\n",
      "11\n",
      "2\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 102, 1448, 1502, 1049,  542,  234,   81,   31,    6,    5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(987456348)\n",
    "# large sample to get strong results\n",
    "nobs = 5000\n",
    "x = np.column_stack((np.ones(nobs), np.linspace(0, 1, nobs)))\n",
    "\n",
    "mu0 = np.exp(0.5 *2 * x.sum(1))\n",
    "y = np.random.poisson(mu0, size=nobs)\n",
    "print(np.bincount(y))\n",
    "y_ = y\n",
    "indices = np.arange(len(y))\n",
    "mask = mask0 = y > 0 \n",
    "for _ in range(10):\n",
    "    \n",
    "    print( mask.sum())\n",
    "    indices = mask #indices[mask]\n",
    "    if not np.any(mask):\n",
    "        break\n",
    "    mu_ = np.exp(0.5 * x[indices].sum(1))\n",
    "    y[indices] = y_ = np.random.poisson(mu_, size=len(mu_))\n",
    "    np.place(y, mask, y_)\n",
    "    mask = np.logical_and(mask0, y == 0)\n",
    "    \n",
    "np.bincount(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6435d35",
   "metadata": {},
   "source": [
    "## Estimating misspecified Poisson Model\n",
    "\n",
    "The data that we generated has zero deflation, this is, we observe fewer zeros than what we would expect in a Poisson model.\n",
    "\n",
    "After fitting the model, we can use the plot function in the poisson diagnostic class to compare the expected predictive distribution and the realized frequencies. The shows that the Poisson model overestimates the number of zeros and underestimates counts of one and two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fdd59b7",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 1.668079\n",
      "         Iterations 4\n",
      "                          Poisson Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 5000\n",
      "Model:                        Poisson   Df Residuals:                     4998\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Fri, 20 Mar 2026   Pseudo R-squ.:                0.008678\n",
      "Time:                        11:18:46   Log-Likelihood:                -8340.4\n",
      "converged:                       True   LL-Null:                       -8413.4\n",
      "Covariance Type:            nonrobust   LLR p-value:                 1.279e-33\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.6532      0.019     33.642      0.000       0.615       0.691\n",
      "x1             0.3871      0.032     12.062      0.000       0.324       0.450\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "mod_p = Poisson(y, x)\n",
    "res_p = mod_p.fit()\n",
    "print(res_p.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91a5c9cb",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dia_p = res_p.get_diagnostic()\n",
    "dia_p.plot_probs();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a237e21f",
   "metadata": {},
   "source": [
    "## Estimating the Hurdle Model\n",
    "\n",
    "Next, we estimate the correctly specified Poisson-Poisson hurdle model.\n",
    "\n",
    "Signature and options for the HurdleCountModel shows that poisson-poisson is the default, so we do not need to specify any options when creating this model.\n",
    "\n",
    "`HurdleCountModel(endog, exog, offset=None, dist='poisson', zerodist='poisson', \n",
    "                  p=2, pzero=2, exposure=None, missing='none', **kwargs)`\n",
    "                  \n",
    "The results class of the HurdleCountModel has a `get_diagnostic` method. However, only part of the diagnostic methods are currently available. The plot of the predictive distribution shows very high agreement with the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "70065ce8",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     HurdleCountModel Regression Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 5000\n",
      "Model:               HurdleCountModel   Df Residuals:                     4996\n",
      "Method:                           MLE   Df Model:                            2\n",
      "Date:                Fri, 20 Mar 2026   Pseudo R-squ.:                 0.01503\n",
      "Time:                        11:18:46   Log-Likelihood:                -8004.9\n",
      "converged:               [True, True]   LL-Null:                       -8127.1\n",
      "Covariance Type:            nonrobust   LLR p-value:                 8.901e-54\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "zm_const       0.9577      0.048     20.063      0.000       0.864       1.051\n",
      "zm_x1          1.0576      0.121      8.737      0.000       0.820       1.295\n",
      "const          0.5009      0.024     20.875      0.000       0.454       0.548\n",
      "x1             0.4577      0.039     11.882      0.000       0.382       0.533\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "mod_h = HurdleCountModel(y, x)\n",
    "res_h = mod_h.fit(disp=False)\n",
    "print(res_h.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c3aadbf",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dia_h = res_h.get_diagnostic()\n",
    "dia_h.plot_probs();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0827fde3",
   "metadata": {},
   "source": [
    "We can use the Wald test to test whether the parameters of the zero model are the same as the parameters of the zero-truncated count model. The p-value is very small and correctly rejects that the model is just Poisson. We are using a large sample size, so the power of the test will be large in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c000b4cb",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'statsmodels.stats.contrast.ContrastResults'>\n",
       "<Wald test (chi2): statistic=470.67320754391915, p-value=6.231772522807044e-103, df_denom=2>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.wald_test(\"zm_const = const, zm_x1 = x1\", scalar=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100ba8a8",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "The hurdle model can be used for prediction for statistics of the overall model and of the two submodels. The statistics that should be predicted is specified using the `which` keyword.\n",
    "\n",
    "The following is taken from the docstring for predict and lists available the options.\n",
    "\n",
    "        which : str (optional)\n",
    "            Statitistic to predict. Default is 'mean'.\n",
    "\n",
    "            - 'mean' : the conditional expectation of endog E(y | x)\n",
    "            - 'mean-main' : mean parameter of truncated count model.\n",
    "              Note, this is not the mean of the truncated distribution.\n",
    "            - 'linear' : the linear predictor of the truncated count model.\n",
    "            - 'var' : returns the estimated variance of endog implied by the\n",
    "              model.\n",
    "            - 'prob-main' : probability of selecting the main model which is\n",
    "              the probability of observing a nonzero count P(y > 0 | x).\n",
    "            - 'prob-zero' : probability of observing a zero count. P(y=0 | x).\n",
    "              This is equal to is ``1 - prob-main``\n",
    "            - 'prob-trunc' : probability of truncation of the truncated count\n",
    "              model. This is the probability of observing a zero count implied\n",
    "              by the truncation model.\n",
    "            - 'mean-nonzero' : expected value conditional on having observation\n",
    "              larger than zero, E(y | X, y>0)\n",
    "            - 'prob' : probabilities of each count from 0 to max(endog), or\n",
    "              for y_values if those are provided. This is a multivariate\n",
    "              return (2-dim when predicting for several observations).\n",
    "              \n",
    "These options are available in the `predict` and the `get_prediction` methods of the results class.\n",
    "\n",
    "For the following example, we create a set of explanatory variables that are taken from the original data at equal spaced intervals. Then we can predict the available statistics conditional on these explanatory variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9757da68",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.        ],\n",
       "       [1.        , 0.20004001],\n",
       "       [1.        , 0.40008002],\n",
       "       [1.        , 0.60012002],\n",
       "       [1.        , 0.80016003]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "which_options = [\"mean\", \"mean-main\", \"linear\", \"mean-nonzero\", \"prob-zero\", \"prob-main\", \"prob-trunc\", \"var\", \"prob\"]\n",
    "ex = x[slice(None, None, nobs // 5), :]\n",
    "ex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3614ee25",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "     [1.89150663 2.07648059 2.25555158 2.43319456 2.61673457]\n",
      "mean-main\n",
      "     [1.65015181 1.8083782  1.98177629 2.17180081 2.38004602]\n",
      "linear\n",
      "     [0.50086729 0.59243042 0.68399356 0.77555669 0.86711982]\n",
      "mean-nonzero\n",
      "     [2.04231955 2.16292424 2.29857565 2.45116551 2.62277411]\n",
      "prob-zero\n",
      "     [0.07384394 0.0399661  0.01871771 0.00733159 0.00230273]\n",
      "prob-main\n",
      "     [0.92615606 0.9600339  0.98128229 0.99266841 0.99769727]\n",
      "prob-trunc\n",
      "     [0.19202076 0.16391977 0.1378242  0.11397219 0.09254632]\n",
      "var\n",
      "     [1.43498239 1.51977118 1.63803729 1.7971727  1.99738345]\n",
      "prob\n",
      "     [[7.38439416e-02 3.63208532e-01 2.99674608e-01 1.64836199e-01\n",
      "  6.80011882e-02 2.24424568e-02 6.17224344e-03 1.45501981e-03\n",
      "  3.00125448e-04 5.50280612e-05]\n",
      " [3.99660987e-02 3.40376213e-01 3.07764462e-01 1.85518182e-01\n",
      "  8.38717591e-02 3.03343722e-02 9.14266959e-03 2.36191491e-03\n",
      "  5.33904431e-04 1.07277904e-04]\n",
      " [1.87177088e-02 3.10869602e-01 3.08037002e-01 2.03486809e-01\n",
      "  1.00816333e-01 3.99590837e-02 1.31983274e-02 3.73659033e-03\n",
      "  9.25635762e-04 2.03822556e-04]\n",
      " [7.33159258e-03 2.77316512e-01 3.01138113e-01 2.18003999e-01\n",
      "  1.18365316e-01 5.14131777e-02 1.86098635e-02 5.77384524e-03\n",
      "  1.56745522e-03 3.78244503e-04]\n",
      " [2.30272798e-03 2.42169151e-01 2.88186862e-01 2.28632665e-01\n",
      "  1.36039066e-01 6.47558475e-02 2.56869828e-02 8.73374304e-03\n",
      "  2.59833880e-03 6.87129546e-04]]\n"
     ]
    }
   ],
   "source": [
    "for w in which_options:\n",
    "    print(w)\n",
    "    pred = res_h.predict(ex, which=w)\n",
    "    print(\"    \", pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bbc9ebed",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean\n",
      "     [1.89150663 2.07648059 2.25555158 2.43319456 2.61673457]\n",
      "  se [0.07877461 0.05693768 0.05866892 0.09551274 0.15359057]\n",
      "mean-main\n",
      "     [1.65015181 1.8083782  1.98177629 2.17180081 2.38004602]\n",
      "  se [0.03959242 0.03164634 0.02471869 0.02415162 0.03453261]\n",
      "linear\n",
      "     [0.50086729 0.59243042 0.68399356 0.77555669 0.86711982]\n",
      "  se [0.04773779 0.03148549 0.02960421 0.04397859 0.06453261]\n",
      "mean-nonzero\n",
      "     [2.04231955 2.16292424 2.29857565 2.45116551 2.62277411]\n",
      "  se [0.02978486 0.02443098 0.01958745 0.0196433  0.02881753]\n",
      "prob-zero\n",
      "     [0.07384394 0.0399661  0.01871771 0.00733159 0.00230273]\n",
      "  se [0.00918583 0.00405155 0.00220446 0.00158494 0.00090255]\n",
      "prob-main\n",
      "     [0.92615606 0.9600339  0.98128229 0.99266841 0.99769727]\n",
      "  se [0.00918583 0.00405155 0.00220446 0.00158494 0.00090255]\n",
      "prob-trunc\n",
      "     [0.19202076 0.16391977 0.1378242  0.11397219 0.09254632]\n",
      "  se [0.00760257 0.00518746 0.00340683 0.00275261 0.00319587]\n",
      "var\n",
      "     [1.43498239 1.51977118 1.63803729 1.7971727  1.99738345]\n",
      "  se [0.04853902 0.03615054 0.02747485 0.02655145 0.03733328]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/base/_prediction_inference.py:782: UserWarning: using default log-link in get_prediction\n",
      "  warnings.warn(\"using default log-link in get_prediction\")\n"
     ]
    }
   ],
   "source": [
    "for w in which_options[:-1]:\n",
    "    print(w)\n",
    "    pred = res_h.get_prediction(ex, which=w)\n",
    "    print(\"    \", pred.predicted)\n",
    "    print(\"  se\", pred.se)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e356eb1b",
   "metadata": {},
   "source": [
    "The option `which=\"prob\"` returns an array of predicted probabilities for each row of the predict `exog`.\n",
    "We are often interested in the mean probabilities averaged over all exog. The prediction methods have an option `average=True` to compute the average of the predicted values across observations and the corresponding standard errors and confidence intervals for those averaged predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d90afc2",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     [2.84324139e-02 3.06788002e-01 3.00960210e-01 2.00095571e-01\n",
      " 1.01418732e-01 4.17809876e-02 1.45620174e-02 4.41222267e-03\n",
      " 1.18509193e-03 2.86300514e-04]\n",
      "  se [2.81472152e-03 5.00830805e-03 1.37524763e-03 1.87343644e-03\n",
      " 1.99068649e-03 1.23878525e-03 5.78099173e-04 2.21180110e-04\n",
      " 7.25021189e-05 2.08872558e-05]\n"
     ]
    }
   ],
   "source": [
    "pred = res_h.get_prediction(ex, which=\"prob\", average=True)\n",
    "print(\"    \", pred.predicted)\n",
    "print(\"  se\", pred.se)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09602e97",
   "metadata": {},
   "source": [
    "We use the panda DataFrame to get a display that is easier to read. The \"predicted\" column shows the probability mass function for the predicted distribution of response values averaged of our 5 grid points of exog. The probabilities do not add up to one because counts larger than those observed have positive probability and are missing in the table, although in this example that probability is small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "77c7eff9",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>se</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.028432</td>\n",
       "      <td>0.002815</td>\n",
       "      <td>0.022916</td>\n",
       "      <td>0.033949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.306788</td>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.296972</td>\n",
       "      <td>0.316604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300960</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.298265</td>\n",
       "      <td>0.303656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200096</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.196424</td>\n",
       "      <td>0.203767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.101419</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.097517</td>\n",
       "      <td>0.105320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041781</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.039353</td>\n",
       "      <td>0.044209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.013429</td>\n",
       "      <td>0.015695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.004846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted        se  ci_lower  ci_upper\n",
       "0   0.028432  0.002815  0.022916  0.033949\n",
       "1   0.306788  0.005008  0.296972  0.316604\n",
       "2   0.300960  0.001375  0.298265  0.303656\n",
       "3   0.200096  0.001873  0.196424  0.203767\n",
       "4   0.101419  0.001991  0.097517  0.105320\n",
       "5   0.041781  0.001239  0.039353  0.044209\n",
       "6   0.014562  0.000578  0.013429  0.015695\n",
       "7   0.004412  0.000221  0.003979  0.004846\n",
       "8   0.001185  0.000073  0.001043  0.001327\n",
       "9   0.000286  0.000021  0.000245  0.000327"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfp_h = pred.summary_frame()\n",
    "dfp_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "185a5042",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.9999215487936677), np.float64(7.84512063323195e-05))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_larger9 = pred.predicted.sum()\n",
    "prob_larger9, 1 - prob_larger9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75795cd",
   "metadata": {},
   "source": [
    "`get_prediction` returns in this case an instance of the base `PredictionResultsDelta` class.\n",
    "\n",
    "Inferential statistics like standard errors, p-values and confidence interval for nonlinear functions that depend on several distribution parameters are computed using the delta method. Inference for predictions is based on the normal distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f8124e02",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.base._prediction_inference.PredictionResultsDelta at 0xadde5de1e8ed>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c1d314d",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<scipy.stats._continuous_distns.norm_gen at 0xadde5de1e8ed>, ())"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.dist, pred.dist_args"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b032002",
   "metadata": {},
   "source": [
    "We can compare the distribution predicted by the hurdle model with the one predicted by the Poisson model that we estimated earlier. The last column, \"diff\", shows that Poisson model overestimates the number of zeros by around 8% of observations and underestimates the counts of 1 and 2 by 7%, resp. 3.7% at the average over the `exog` grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd01ba63",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>se</th>\n",
       "      <th>ci_lower</th>\n",
       "      <th>ci_upper</th>\n",
       "      <th>poisson</th>\n",
       "      <th>diff</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.022916</td>\n",
       "      <td>0.033949</td>\n",
       "      <td>0.107848</td>\n",
       "      <td>0.079416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.306788</td>\n",
       "      <td>0.005008</td>\n",
       "      <td>0.296972</td>\n",
       "      <td>0.316604</td>\n",
       "      <td>0.237020</td>\n",
       "      <td>-0.069768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.300960</td>\n",
       "      <td>0.001375</td>\n",
       "      <td>0.298265</td>\n",
       "      <td>0.303656</td>\n",
       "      <td>0.263523</td>\n",
       "      <td>-0.037437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.200096</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.196424</td>\n",
       "      <td>0.203767</td>\n",
       "      <td>0.197657</td>\n",
       "      <td>-0.002439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.101419</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>0.097517</td>\n",
       "      <td>0.105320</td>\n",
       "      <td>0.112511</td>\n",
       "      <td>0.011093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041781</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>0.039353</td>\n",
       "      <td>0.044209</td>\n",
       "      <td>0.051833</td>\n",
       "      <td>0.010052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.014562</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.013429</td>\n",
       "      <td>0.015695</td>\n",
       "      <td>0.020124</td>\n",
       "      <td>0.005561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.004412</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.003979</td>\n",
       "      <td>0.004846</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.002356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.001185</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>0.002012</td>\n",
       "      <td>0.000827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted        se  ci_lower  ci_upper   poisson      diff\n",
       "0   0.028432  0.002815  0.022916  0.033949  0.107848  0.079416\n",
       "1   0.306788  0.005008  0.296972  0.316604  0.237020 -0.069768\n",
       "2   0.300960  0.001375  0.298265  0.303656  0.263523 -0.037437\n",
       "3   0.200096  0.001873  0.196424  0.203767  0.197657 -0.002439\n",
       "4   0.101419  0.001991  0.097517  0.105320  0.112511  0.011093\n",
       "5   0.041781  0.001239  0.039353  0.044209  0.051833  0.010052\n",
       "6   0.014562  0.000578  0.013429  0.015695  0.020124  0.005561\n",
       "7   0.004412  0.000221  0.003979  0.004846  0.006769  0.002356\n",
       "8   0.001185  0.000073  0.001043  0.001327  0.002012  0.000827\n",
       "9   0.000286  0.000021  0.000245  0.000327  0.000537  0.000250"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_p = res_p.get_prediction(ex, which=\"prob\", average=True)\n",
    "dfp_p = pred_p.summary_frame()\n",
    "dfp_h[\"poisson\"] = dfp_p[\"predicted\"]\n",
    "dfp_h[\"diff\"] = dfp_h[\"poisson\"] - dfp_h[\"predicted\"]\n",
    "dfp_h"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0da7fb38",
   "metadata": {},
   "source": [
    "## Other post-estimation\n",
    "\n",
    "The estimated hurdle model can be use for wald test of parameters and for prediction. Other maximum likelihood statistics such as loglikelihood value and information criteria are also available. \n",
    "\n",
    "However, some post-estimation methods that require helper functions that are not needed for estimation, parameter inference and prediction are not yet available. The main methods that are not supported yet are `score_test`, `get_distribution`, and `get_influence`. Diagnostic measures in `get_diagnostics` are only available for statistics that are based on prediction.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e821f6e8",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(-8004.904002793644),\n",
       " 4996,\n",
       " np.float64(16017.808005587289),\n",
       " np.float64(16043.876778352953))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.llf, res_h.df_resid, res_h.aic, res_h.bic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d2ae62",
   "metadata": {},
   "source": [
    "Is there excess dispersion? We can use the pearson residuals to compute a pearson chi2 statistics which should be close to 1 if the model is correctly specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f1a5124",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9989670114949286)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(res_h.resid_pearson**2).sum() / res_h.df_resid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff9df60",
   "metadata": {},
   "source": [
    "The diagnostic class also has the predictive distribution which is used in the diagnostic plots. No other statistics or tests are currently availalbe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50ba5545",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02044612, 0.29147174, 0.29856288, 0.20740118, 0.10990976,\n",
       "       0.04737579, 0.0172898 , 0.00548983, 0.00154646, 0.00039214])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia_h.probs_predicted.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c913a1bb",
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.10849337,  1.10830496, -0.89188344, -0.89207183,  1.10773978,\n",
       "       -0.8924486 , -0.89263697,  0.10717466,  0.1069863 ,  0.10679794])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_h.resid[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f4f45d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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