vincentarelbundock/example_getting_started.ipynb

## example_getting_started.ipynb
{
 "metadata": {
  "name": "getting_started"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Getting started\n",
      "\n",
      "This very simple case-study is designed to get you up-and-running quickly with\n",
      "``statsmodels``. Starting from raw data, we will show the steps needed to\n",
      "estimate a statistical model and to draw a diagnostic plot. We will only use\n",
      "functions provided by ``statsmodels`` or its ``pandas`` and ``patsy``\n",
      "dependencies.  \n",
      "\n",
      "## Loading modules and functions\n",
      "\n",
      "After installing statsmodels and its dependencies we load a\n",
      "few modules and functions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "import pandas\n",
      "from patsy import dmatrices"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "[pandas](http://pandas.pydata.org/) builds on ``numpy`` arrays to provide\n",
      "rich data structures and data analysis tools. The ``pandas.DataFrame`` function\n",
      "provides labelled arrays of (potentially heterogenous) data, similar to the\n",
      "``R`` \"data.frame\". The ``pandas.read_csv`` function can be used to convert a\n",
      "comma-separated values file to a ``DataFrame`` object.\n",
      "\n",
      "[patsy](https://github.com/pydata/patsy) is a Python library for describing\n",
      "satistical models and building [Design Matrices(http://en.wikipedia.org/wiki/Design_matrix) using ``R``-like formulas. \n",
      "\n",
      "## Data\n",
      "\n",
      "We download the [Guerry dataset](http://vincentarelbundock.github.com/Rdatasets/doc/Guerry.html), a\n",
      "collection of historical data used in support of Andre-Michel Guerry's 1833\n",
      "*Essay on the Moral Statistics of France*. The data set is hosted online in\n",
      "comma-separated values format (CSV) by the [Rdatasets](http://vincentarelbundock.github.com/Rdatasets/) repository.\n",
      "We could download the file locally and then load it using ``read_csv``, but\n",
      "``pandas`` takes care of all of this automatically for us:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"http://vincentarelbundock.github.com/Rdatasets/csv/Guerry.csv\"\n",
      "df = pandas.read_csv(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The `Input/Output doc page` shows how to import from various\n",
      "other formats. \n",
      "\n",
      "We select the variables of interest and look at the bottom 5 rows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vars = ['Department', 'Lottery', 'Literacy', 'Wealth', 'Region']\n",
      "df = df[vars]\n",
      "df[-5:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Department</th>\n",
        "      <th>Lottery</th>\n",
        "      <th>Literacy</th>\n",
        "      <th>Wealth</th>\n",
        "      <th>Region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>81</strong></td>\n",
        "      <td>       Vienne</td>\n",
        "      <td> 40</td>\n",
        "      <td> 25</td>\n",
        "      <td> 68</td>\n",
        "      <td>   W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>82</strong></td>\n",
        "      <td> Haute-Vienne</td>\n",
        "      <td> 55</td>\n",
        "      <td> 13</td>\n",
        "      <td> 67</td>\n",
        "      <td>   C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>83</strong></td>\n",
        "      <td>       Vosges</td>\n",
        "      <td> 14</td>\n",
        "      <td> 62</td>\n",
        "      <td> 82</td>\n",
        "      <td>   E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>84</strong></td>\n",
        "      <td>        Yonne</td>\n",
        "      <td> 51</td>\n",
        "      <td> 47</td>\n",
        "      <td> 30</td>\n",
        "      <td>   C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>85</strong></td>\n",
        "      <td>        Corse</td>\n",
        "      <td> 83</td>\n",
        "      <td> 49</td>\n",
        "      <td> 37</td>\n",
        "      <td> NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "      Department  Lottery  Literacy  Wealth Region\n",
        "81        Vienne       40        25      68      W\n",
        "82  Haute-Vienne       55        13      67      C\n",
        "83        Vosges       14        62      82      E\n",
        "84         Yonne       51        47      30      C\n",
        "85         Corse       83        49      37    NaN"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that there is one missing observation in the *Region* column. We\n",
      "eliminate it using a ``DataFrame`` method provided by ``pandas``:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.dropna()\n",
      "df[-5:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Department</th>\n",
        "      <th>Lottery</th>\n",
        "      <th>Literacy</th>\n",
        "      <th>Wealth</th>\n",
        "      <th>Region</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>80</strong></td>\n",
        "      <td>       Vendee</td>\n",
        "      <td> 68</td>\n",
        "      <td> 28</td>\n",
        "      <td> 56</td>\n",
        "      <td> W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>81</strong></td>\n",
        "      <td>       Vienne</td>\n",
        "      <td> 40</td>\n",
        "      <td> 25</td>\n",
        "      <td> 68</td>\n",
        "      <td> W</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>82</strong></td>\n",
        "      <td> Haute-Vienne</td>\n",
        "      <td> 55</td>\n",
        "      <td> 13</td>\n",
        "      <td> 67</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>83</strong></td>\n",
        "      <td>       Vosges</td>\n",
        "      <td> 14</td>\n",
        "      <td> 62</td>\n",
        "      <td> 82</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>84</strong></td>\n",
        "      <td>        Yonne</td>\n",
        "      <td> 51</td>\n",
        "      <td> 47</td>\n",
        "      <td> 30</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "      Department  Lottery  Literacy  Wealth Region\n",
        "80        Vendee       68        28      56      W\n",
        "81        Vienne       40        25      68      W\n",
        "82  Haute-Vienne       55        13      67      C\n",
        "83        Vosges       14        62      82      E\n",
        "84         Yonne       51        47      30      C"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Substantive motivation and model\n",
      "\n",
      "We want to know whether literacy rates in the 86 French departments are\n",
      "associated with per capita wagers on the Royal Lottery in the 1820s. We need to\n",
      "control for the level of wealth in each department, and we also want to include\n",
      "a series of dummy variables on the right-hand side of our regression equation to\n",
      "control for unobserved heterogeneity due to regional effects. The model is\n",
      "estimated using ordinary least squares regression (OLS).  \n",
      "\n",
      "\n",
      "## Design matrices (*endog* & *exog*)\n",
      "\n",
      "To fit most of the models covered by ``statsmodels``, you will need to create\n",
      "two design matrices. The first is a matrix of endogenous variable(s) (i.e.\n",
      "dependent, response, regressand, etc.). The second is a matrix of exogenous\n",
      "variable(s) (i.e. independent, predictor, regressor, etc.). The OLS coefficient\n",
      "estimates are calculated as usual: \n",
      " \n",
      "$$\\hat{\\beta} = (X'X)^{-1} X'y$$\n",
      "\n",
      "where $y$ is an $N \\times 1$ column of data on lottery wagers per\n",
      "capita (*Lottery*). $X$ is $N \\times 7$ with an intercept, the\n",
      "*Literacy* and *Wealth* variables, and 4 region binary variables.\n",
      "\n",
      "The ``patsy`` module provides a convenient function to prepare design matrices\n",
      "using ``R``-like formulas. You can find more information here:\n",
      "http://patsy.readthedocs.org\n",
      "\n",
      "We use ``patsy``'s ``dmatrices`` function to create design matrices:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y, X = dmatrices('Lottery ~ Literacy + Wealth + Region', data=df, \n",
      "                 return_type='dataframe')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print y[:3]\n",
      "X[:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   Lottery\n",
        "0       41\n",
        "1       38\n",
        "2       66\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>Intercept</th>\n",
        "      <th>Region[T.E]</th>\n",
        "      <th>Region[T.N]</th>\n",
        "      <th>Region[T.S]</th>\n",
        "      <th>Region[T.W]</th>\n",
        "      <th>Literacy</th>\n",
        "      <th>Wealth</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <td><strong>0</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 37</td>\n",
        "      <td> 73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>1</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 51</td>\n",
        "      <td> 22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <td><strong>2</strong></td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 13</td>\n",
        "      <td> 61</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "   Intercept  Region[T.E]  Region[T.N]  Region[T.S]  Region[T.W]  Literacy  Wealth\n",
        "0          1            1            0            0            0        37      73\n",
        "1          1            0            1            0            0        51      22\n",
        "2          1            0            0            0            0        13      61"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that ``dmatrices`` has\n",
      "\n",
      "* split the categorical *Region* variable into a set of indicator variables. \n",
      "* added a constant to the exogenous regressors matrix. \n",
      "* returned ``pandas`` DataFrames instead of simple numpy arrays. This is useful because DataFrames allow ``statsmodels`` to carry-over meta-data (e.g. variable names) when reporting results.\n",
      "\n",
      "The above behavior can of course be altered. See the [patsy doc page](http://patsy.readthedocs.org/). \n",
      "\n",
      "## Model fit and summary\n",
      "\n",
      "Fitting a model in ``statsmodels`` typically involves 3 easy steps: \n",
      "\n",
      "1. Use the model class to describe the model\n",
      "2. Fit the model using a class method\n",
      "3. Inspect the results using a summary method\n",
      "\n",
      "For OLS, this is achieved by:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod = sm.OLS(y, X)    # Describe model\n",
      "res = mod.fit()       # Fit model\n",
      "print res.summary()   # Summarize model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:                Lottery   R-squared:                       0.338\n",
        "Model:                            OLS   Adj. R-squared:                  0.287\n",
        "Method:                 Least Squares   F-statistic:                     6.636\n",
        "Date:                Sun, 26 Aug 2012   Prob (F-statistic):           1.07e-05\n",
        "Time:                        21:55:45   Log-Likelihood:                -375.30\n",
        "No. Observations:                  85   AIC:                             764.6\n",
        "Df Residuals:                      78   BIC:                             781.7\n",
        "Df Model:                           6                                         \n",
        "===============================================================================\n",
        "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------\n",
        "Intercept      38.6517      9.456      4.087      0.000        19.826    57.478\n",
        "Region[T.E]   -15.4278      9.727     -1.586      0.117       -34.793     3.938\n",
        "Region[T.N]   -10.0170      9.260     -1.082      0.283       -28.453     8.419\n",
        "Region[T.S]    -4.5483      7.279     -0.625      0.534       -19.039     9.943\n",
        "Region[T.W]   -10.0913      7.196     -1.402      0.165       -24.418     4.235\n",
        "Literacy       -0.1858      0.210     -0.886      0.378        -0.603     0.232\n",
        "Wealth          0.4515      0.103      4.390      0.000         0.247     0.656\n",
        "==============================================================================\n",
        "Omnibus:                        3.049   Durbin-Watson:                   1.785\n",
        "Prob(Omnibus):                  0.218   Jarque-Bera (JB):                2.694\n",
        "Skew:                          -0.340   Prob(JB):                        0.260\n",
        "Kurtosis:                       2.454   Cond. No.                         371.\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ``res`` object has many useful attributes. For example, we can extract\n",
      "parameter estimates and r-squared by typing: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print res.params\n",
      "print 'R2: ', res.rsquared"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Intercept      38.651655\n",
        "Region[T.E]   -15.427785\n",
        "Region[T.N]   -10.016961\n",
        "Region[T.S]    -4.548257\n",
        "Region[T.W]   -10.091276\n",
        "Literacy       -0.185819\n",
        "Wealth          0.451475\n",
        "R2:  0.337950869193\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Type ``dir(res)`` for a full list of attributes.  \n",
      "\n",
      "For more information and examples, see the Regression doc page."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Diagnostics and specification tests\n",
      "\n",
      "``statsmodels`` allows you to conduct a range of useful `regression diagnostics and specification tests`.  For instance, apply the Rainbow test for linearity (the null hypothesis is that the\n",
      "relationship is properly modelled as linear): "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.stats.linear_rainbow(res)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "(0.84723399761569129, 0.69979655436216426)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Admittedly, the output produced above is not very verbose, but we know from\n",
      "reading the `docstring` that the first number is an F-statistic and that the second is the p-value. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sm.stats.linear_rainbow.__doc__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Rainbow test for linearity\n",
        "\n",
        "    The Null hypothesis is that the regression is correctly modelled as linear.\n",
        "    The alternative for which the power might be large are convex, check\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    res : Result instance\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    fstat : float\n",
        "        test statistic based of F test\n",
        "    pvalue : float\n",
        "        pvalue of the test\n",
        "\n",
        "    \n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "``statsmodels`` also provides graphics functions. For example, we can draw a\n",
      "plot of partial regression for a set of regressors by:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.graphics.regressionplots import plot_partregress \n",
      "plot_partregress(res)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEGCAYAAACJnEVTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
       "prompt_number": 30,
       "text": [
        "<matplotlib.figure.Figure at 0x4699690>"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEGCAYAAACJnEVTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n"
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## More\n",
      "\n",
      "Congratulations! You're ready to move on to other topics in the `Table of Contents`"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}