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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Homework for GLM lecture I\n",
      "\n",
      "[Tom Wallis](http://www.tomwallis.info) and [Philipp Berens](http://philippberens.wordpress.com/)\n",
      "\n",
      "The University of T\u00fcbingen\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import patsy\n",
      "import seaborn as sns\n",
      "import statsmodels.formula.api as smf\n",
      "import statsmodels.stats as sm_stats\n",
      "import itertools\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "%matplotlib inline\n",
      "\n",
      "sns.set_style(\"white\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import the dataset csv file into a Pandas dataframe object:\n",
      "sleep = pd.read_csv('../Datasets/sleep.txt', sep='\\t') \n",
      "sleep = sleep.dropna(axis=0)  # drop on axis 0 (i.e. rows)\n",
      "\n",
      "# variable creation:\n",
      "sleep['log_BodyWt'] = np.log(sleep['BodyWt'])\n",
      "\n",
      "sleep['danger_cat'] = 'low'  # set as a string, \"low\" for all.\n",
      "sleep.loc[sleep['Danger'] > 3, 'danger_cat'] = 'high' # set values of 4 and 5 to \"high\"\n",
      "\n",
      "sleep['life_cat'] = pd.qcut(sleep['LifeSpan'], [0, .33, .66, 1])\n",
      "sleep['life_cat'] = sleep['life_cat'].cat.rename_categories(['short', 'med', 'long']) # rename categories\n",
      "sleep['life_cat'] = sleep['life_cat'].astype('object')  # make it an object, rather than a category\n",
      "\n",
      "sleep.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 42 entries, 1 to 60\n",
        "Data columns (total 14 columns):\n",
        "Species        42 non-null object\n",
        "BodyWt         42 non-null float64\n",
        "BrainWt        42 non-null float64\n",
        "NonDreaming    42 non-null float64\n",
        "Dreaming       42 non-null float64\n",
        "TotalSleep     42 non-null float64\n",
        "LifeSpan       42 non-null float64\n",
        "Gestation      42 non-null float64\n",
        "Predation      42 non-null int64\n",
        "Exposure       42 non-null int64\n",
        "Danger         42 non-null int64\n",
        "log_BodyWt     42 non-null float64\n",
        "danger_cat     42 non-null object\n",
        "life_cat       42 non-null object\n",
        "dtypes: float64(8), int64(3), object(3)\n",
        "memory usage: 4.9+ KB\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Simple model\n",
      "\n",
      "We can build the simple model `TotalSleep ~ log_BodyWt + danger_cat` first, to see how categorical and continuous variables work together in the design matrix."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = patsy.dmatrix('log_BodyWt + danger_cat', sleep)\n",
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "DesignMatrix with shape (42, 3)\n",
        "  Intercept  danger_cat[T.low]  log_BodyWt\n",
        "          1                  1     0.00000\n",
        "          1                  0     7.84267\n",
        "          1                  0     2.35613\n",
        "          1                  1    -3.77226\n",
        "          1                  0     5.07517\n",
        "          1                  1     1.19392\n",
        "          1                  1     3.95432\n",
        "          1                  0    -0.85567\n",
        "          1                  0     6.14204\n",
        "          1                  1    -2.59027\n",
        "          1                  1     1.09861\n",
        "          1                  1    -0.24207\n",
        "          1                  1    -1.60944\n",
        "          1                  0     3.31999\n",
        "          1                  1    -2.12026\n",
        "          1                  1     4.44265\n",
        "          1                  1    -2.29263\n",
        "          1                  0     0.03922\n",
        "          1                  0     6.25575\n",
        "          1                  0    -5.29832\n",
        "          1                  1    -4.60517\n",
        "          1                  1     4.12713\n",
        "          1                  1    -3.77226\n",
        "          1                  1    -3.03655\n",
        "          1                  1     0.53063\n",
        "          1                  1     1.25276\n",
        "          1                  1    -0.73397\n",
        "          1                  0     2.30259\n",
        "          1                  1     0.48243\n",
        "          1                  0     5.25750\n",
        "  [12 rows omitted]\n",
        "  Terms:\n",
        "    'Intercept' (column 0)\n",
        "    'danger_cat' (column 1)\n",
        "    'log_BodyWt' (column 2)\n",
        "  (to view full data, use np.asarray(this_obj))"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# fit the model:\n",
      "fit = smf.glm('TotalSleep ~ log_BodyWt + danger_cat', sleep).fit()\n",
      "print(fit.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 Generalized Linear Model Regression Results                  \n",
        "==============================================================================\n",
        "Dep. Variable:             TotalSleep   No. Observations:                   42\n",
        "Model:                            GLM   Df Residuals:                       39\n",
        "Model Family:                Gaussian   Df Model:                            2\n",
        "Link Function:               identity   Scale:                   12.9203335198\n",
        "Method:                          IRLS   Log-Likelihood:                -111.77\n",
        "Date:                Tue, 24 Feb 2015   Deviance:                       503.89\n",
        "Time:                        17:08:10   Pearson chi2:                     504.\n",
        "No. Iterations:                     4                                         \n",
        "=====================================================================================\n",
        "                        coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------------\n",
        "Intercept             9.3607      1.117      8.378      0.000         7.171    11.551\n",
        "danger_cat[T.low]     2.8230      1.324      2.132      0.033         0.228     5.418\n",
        "log_BodyWt           -0.7461      0.206     -3.625      0.000        -1.150    -0.343\n",
        "=====================================================================================\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## What do the coefficients mean?\n",
      "\n",
      "We have three coefficients: the `Intercept` ($\\beta_1$), `danger_cat[T.low]` ($\\beta_2$) and `log_BodyWt` ($\\beta_3$). What do they mean?\n",
      "\n",
      "If `danger_cat` is `high` (i.e. the species is in the \"high\" danger group), then the second beta weight is multiplied by zero. So for the `high` group, we just have a line relating log bodyweight and total sleep, whose intercept is 9.36 (i.e. when log bodyweight is zero). \n",
      "\n",
      "When `danger_cat` is `low`, the second beta weight of 2.82 is added to the prediction. This has the effect of shifting the whole line relating bodyweight to sleep *upwards* --- but does not change its slope.\n",
      "\n",
      "This model is the hypothesis that the only effect of low danger is to raise the average amount the animal sleeps, keeping the relationship between bodyweight and sleep the same.\n",
      "\n",
      "We visualise these predictions after we fit the more complex model, below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# More complex model (including `life_cat`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = patsy.dmatrix('log_BodyWt + danger_cat + life_cat', sleep)\n",
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "DesignMatrix with shape (42, 5)\n",
        "  Columns:\n",
        "    ['Intercept',\n",
        "     'danger_cat[T.low]',\n",
        "     'life_cat[T.med]',\n",
        "     'life_cat[T.short]',\n",
        "     'log_BodyWt']\n",
        "  Terms:\n",
        "    'Intercept' (column 0)\n",
        "    'danger_cat' (column 1)\n",
        "    'life_cat' (columns 2:4)\n",
        "    'log_BodyWt' (column 4)\n",
        "  (to view full data, use np.asarray(this_obj))"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[0:10, :]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "array([[ 1.        ,  1.        ,  0.        ,  1.        ,  0.        ],\n",
        "       [ 1.        ,  0.        ,  0.        ,  0.        ,  7.84267147],\n",
        "       [ 1.        ,  0.        ,  0.        ,  0.        ,  2.35612586],\n",
        "       [ 1.        ,  1.        ,  1.        ,  0.        , -3.77226106],\n",
        "       [ 1.        ,  0.        ,  0.        ,  0.        ,  5.07517382],\n",
        "       [ 1.        ,  1.        ,  0.        ,  0.        ,  1.19392247],\n",
        "       [ 1.        ,  1.        ,  0.        ,  0.        ,  3.95431592],\n",
        "       [ 1.        ,  0.        ,  1.        ,  0.        , -0.85566611],\n",
        "       [ 1.        ,  0.        ,  0.        ,  0.        ,  6.14203741],\n",
        "       [ 1.        ,  1.        ,  0.        ,  1.        , -2.59026717]])"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fit_2 = smf.glm('TotalSleep ~ log_BodyWt + danger_cat + life_cat', sleep).fit()\n",
      "print(fit_2.summary())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 Generalized Linear Model Regression Results                  \n",
        "==============================================================================\n",
        "Dep. Variable:             TotalSleep   No. Observations:                   42\n",
        "Model:                            GLM   Df Residuals:                       37\n",
        "Model Family:                Gaussian   Df Model:                            4\n",
        "Link Function:               identity   Scale:                   13.4347736658\n",
        "Method:                          IRLS   Log-Likelihood:                -111.49\n",
        "Date:                Tue, 24 Feb 2015   Deviance:                       497.09\n",
        "Time:                        17:08:10   Pearson chi2:                     497.\n",
        "No. Iterations:                     4                                         \n",
        "=====================================================================================\n",
        "                        coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "-------------------------------------------------------------------------------------\n",
        "Intercept            10.0980      1.601      6.306      0.000         6.960    13.236\n",
        "danger_cat[T.low]     2.6883      1.396      1.926      0.054        -0.047     5.424\n",
        "life_cat[T.med]      -1.1116      1.573     -0.707      0.480        -4.195     1.972\n",
        "life_cat[T.short]    -0.6439      1.759     -0.366      0.714        -4.091     2.803\n",
        "log_BodyWt           -0.8236      0.253     -3.254      0.001        -1.320    -0.327\n",
        "=====================================================================================\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## What do the coefficients mean?\n",
      "\n",
      "### Intercept\n",
      "\n",
      "What is the situation when all the non-intercept columns of the design matrix equal zero? Answer: when `danger_cat` is `high`, `life_cat` is `long` and `log_BodyWt` is $0$.\n",
      "\n",
      "So the intercept is the predicted total amount of sleep, in hours, of species when they are in high danger, live for a relatively long time, and weigh 1 kg (`exp(0) = 1`). \n",
      "\n",
      "Note that no such species necessarily has to exist in the dataset (it doesn't):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sleep['Species'][(sleep['life_cat']=='long') & \n",
      "                 (sleep['danger_cat']=='high') & \n",
      "                 (sleep['log_BodyWt']==0.)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "Series([], name: Species, dtype: object)"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### danger_cat[T.low]\n",
      "\n",
      "When the amount of danger is low, this corresponds to the predicted difference in total sleep from the intercept term, all other things being equal. So if we had a species that had low danger, lived for a long time and weighed 1 kg, it would be expected to sleep for $10 + 2.69 = 12.7$ hours.\n",
      "\n",
      "### life_cat[T.med]\n",
      "\n",
      "All other things being equal to the intercept case, this is the expected difference in total sleep for when lifespan is \"medium\" instead of \"long\".\n",
      "\n",
      "### life_cat[T.short]\n",
      "\n",
      "All other things being equal to the intercept case, this is the expected difference in total sleep for when lifespan is \"low\" instead of \"long\".\n",
      "\n",
      "### log_BodyWt\n",
      "\n",
      "The expected total sleep duration drops by 0.82 hours for every log unit increase in bodyweight."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Bonus question\n",
      "\n",
      "Work out the predicted total sleep for a species that is\n",
      "\n",
      "- low danger\n",
      "- medium lifespan\n",
      "- weighs 13 kg.\n",
      "\n",
      "We can do this manually, by accessing the fit parameters and multiplying them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# intercept * low danger * medium lifespan * log bodyweight:\n",
      "\n",
      "fit_2.params[0] + \\\n",
      "(fit_2.params[1] * 1) + \\\n",
      "(fit_2.params[2] * 1) + \\\n",
      "(fit_2.params[3] * 0) + \\\n",
      "(fit_2.params[4] * np.log(13))  # remember we need the log of 13 kg\n",
      "\n",
      "# the \"\\\" character lets you continue a statement over multiple lines"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "9.5621480810353781"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So for an animal at low danger, with a medium lifespan and weighing 13 kg, we predict they sleep about 9.6 hours.\n",
      "\n",
      "The manual multiplication above is awkward and prone to errors in larger design matrices. We can instead use the `predict` method, and pass a new design matrix of our own construction:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_dat = pd.DataFrame({'log_BodyWt': np.log(13),\n",
      "                        'danger_cat': 'low',\n",
      "                        'life_cat': 'med'\n",
      "                        }, index=[0])\n",
      "new_dat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>danger_cat</th>\n",
        "      <th>life_cat</th>\n",
        "      <th>log_BodyWt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> low</td>\n",
        "      <td> med</td>\n",
        "      <td> 2.564949</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "  danger_cat life_cat  log_BodyWt\n",
        "0        low      med    2.564949"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fit_2.predict(new_dat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "array([ 9.56214808])"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The prediction ($\\mathbf X \\beta$) is calculated internally. Note that for this to work, the column names and the values they take on must exactly match the original data --- otherwise the predict method doesn't know what to do."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# One way to plot the predictions\n",
      "\n",
      "Unfortunately `statsmodels` does not yet have a neat way to visualise predictions from models with continuous and categorical predictors. \n",
      "\n",
      "To make some nice visualisations we can use Seaborn. Recall that most of Seaborn's cool functionality expects data as a data frame. What happens if we just add predictions to our existing `sleep` dataframe?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Simple model (`TotalSleep ~ log_BodyWt + danger_cat`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add the predictions to the dataframe:\n",
      "sleep['yhat'] = fit.predict()  \n",
      "\n",
      "# set up some axes using Seaborn's facetgrid:\n",
      "g = sns.FacetGrid(sleep, hue='danger_cat', size=5)\n",
      "# use the `map` method to add stuff to the facetgrid axes:\n",
      "g.map(plt.plot, \"log_BodyWt\", \"yhat\")\n",
      "g.map(plt.scatter, \"log_BodyWt\", \"TotalSleep\")\n",
      "sns.despine(offset=10)\n",
      "g.set_titles\n",
      "g.add_legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 364,
         "width": 417
        }
       },
       "output_type": "display_data",
       "png": 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LMS4IITxG6vkhqWA+/Ggx10x6juXp2PnLSx8D4OqTd2Xtr6xWwM5WXqbbpObM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\n/O7MQ/nS0SPp26fnatdfeXMpP/nDo9T+/F5mPfsm0DWfp1QOrhrXRq4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v37ZkZPW4XG/Q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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d70d668>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### More complex model (`TotalSleep ~ log_BodyWt + danger_cat + life_cat`)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add the predictions to the dataframe:\n",
      "sleep['yhat_2'] = fit_2.predict()  \n",
      "\n",
      "# set up some axes using Seaborn's facetgrid:\n",
      "g = sns.FacetGrid(sleep, hue='danger_cat', col=\"life_cat\", size=3)\n",
      "# use the `map` method to add stuff to the facetgrid axes:\n",
      "g.map(plt.plot, \"log_BodyWt\", \"yhat_2\")\n",
      "g.map(plt.scatter, \"log_BodyWt\", \"TotalSleep\")\n",
      "sns.despine(offset=5)\n",
      "g.set_titles\n",
      "g.add_legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 216,
         "width": 703
        }
       },
       "output_type": "display_data",
       "png": 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QyoE/A4cCvwIuztF5VcR+sNJqVFYM5/Nvv+D0Ry9hzvw5S8TMXzifk0b9BoDr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       "text": [
        "<matplotlib.figure.Figure at 0x10ceaceb8>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we can see this plotting start to break down. Why are the model prediction lines all different lengths? Why is there no \"high\" danger line in the \"short\" lifespan group?\n",
      "\n",
      "The reason is that the predictions only extend as far as our data does. There's no blue line in the last facet because there's only one \"high\" danger animal in the \"short\" lifespan category -- and hence no second x value to draw a line to."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# A better way to plot the predictions\n",
      "\n",
      "To make the model predictions consistent, we can create a new dataframe that contains all  combinations of the variables we need. To do this we can use a function, given below, called `expand_grid`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# define expand grid function:\n",
      "def expand_grid(data_dict):\n",
      "    \"\"\" A port of R's expand.grid function for use with Pandas dataframes.\n",
      "    \n",
      "    from http://pandas.pydata.org/pandas-docs/stable/cookbook.html?highlight=expand%20grid\n",
      "    \n",
      "    \"\"\"\n",
      "    rows = itertools.product(*data_dict.values())\n",
      "    return pd.DataFrame.from_records(rows, columns=data_dict.keys())\n",
      "\n",
      "\n",
      "# build a new matrix with expand grid:\n",
      "\n",
      "predict_frame = expand_grid(\n",
      "                {'log_BodyWt': np.linspace(-6, 9, 2),\n",
      "                 'life_cat': ['short', 'med', 'long'],\n",
      "                 'danger_cat': ['low', 'high']})\n",
      "predict_frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>log_BodyWt</th>\n",
        "      <th>life_cat</th>\n",
        "      <th>danger_cat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>-6</td>\n",
        "      <td> short</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>-6</td>\n",
        "      <td> short</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>-6</td>\n",
        "      <td>   med</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>-6</td>\n",
        "      <td>   med</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>-6</td>\n",
        "      <td>  long</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>-6</td>\n",
        "      <td>  long</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 9</td>\n",
        "      <td> short</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 9</td>\n",
        "      <td> short</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 9</td>\n",
        "      <td>   med</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 9</td>\n",
        "      <td>   med</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 9</td>\n",
        "      <td>  long</td>\n",
        "      <td>  low</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 9</td>\n",
        "      <td>  long</td>\n",
        "      <td> high</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "    log_BodyWt life_cat danger_cat\n",
        "0           -6    short        low\n",
        "1           -6    short       high\n",
        "2           -6      med        low\n",
        "3           -6      med       high\n",
        "4           -6     long        low\n",
        "5           -6     long       high\n",
        "6            9    short        low\n",
        "7            9    short       high\n",
        "8            9      med        low\n",
        "9            9      med       high\n",
        "10           9     long        low\n",
        "11           9     long       high"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So in `predict_frame`, we have a number of rows containing all combinations of the values we need. Note here we have only two values for the x-axis (log_BodyWt) that we want to plot. This is sufficient here because our model is linear -- you can draw a line between two points. If we had a nonlinear response (e.g., a logistic link function) we would need to make a bigger vector (using `np.linspace` with more than 2 points) to show the curve of our predictions appropriately.\n",
      "\n",
      "Now add our model predictions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict_frame['yhat'] = fit.predict(predict_frame)\n",
      "predict_frame['yhat_2'] = fit_2.predict(predict_frame)\n",
      "predict_frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>log_BodyWt</th>\n",
        "      <th>life_cat</th>\n",
        "      <th>danger_cat</th>\n",
        "      <th>yhat</th>\n",
        "      <th>yhat_2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>-6</td>\n",
        "      <td> short</td>\n",
        "      <td>  low</td>\n",
        "      <td> 16.660219</td>\n",
        "      <td> 17.084153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>-6</td>\n",
        "      <td> short</td>\n",
        "      <td> high</td>\n",
        "      <td> 13.837226</td>\n",
        "      <td> 14.395840</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>-6</td>\n",
        "      <td>   med</td>\n",
        "      <td>  low</td>\n",
        "      <td> 16.660219</td>\n",
        "      <td> 16.616439</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>-6</td>\n",
        "      <td>   med</td>\n",
        "      <td> high</td>\n",
        "      <td> 13.837226</td>\n",
        "      <td> 13.928126</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>-6</td>\n",
        "      <td>  long</td>\n",
        "      <td>  low</td>\n",
        "      <td> 16.660219</td>\n",
        "      <td> 17.728076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>-6</td>\n",
        "      <td>  long</td>\n",
        "      <td> high</td>\n",
        "      <td> 13.837226</td>\n",
        "      <td> 15.039763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 9</td>\n",
        "      <td> short</td>\n",
        "      <td>  low</td>\n",
        "      <td>  5.468932</td>\n",
        "      <td>  4.729805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 9</td>\n",
        "      <td> short</td>\n",
        "      <td> high</td>\n",
        "      <td>  2.645938</td>\n",
        "      <td>  2.041492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 9</td>\n",
        "      <td>   med</td>\n",
        "      <td>  low</td>\n",
        "      <td>  5.468932</td>\n",
        "      <td>  4.262091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 9</td>\n",
        "      <td>   med</td>\n",
        "      <td> high</td>\n",
        "      <td>  2.645938</td>\n",
        "      <td>  1.573778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 9</td>\n",
        "      <td>  long</td>\n",
        "      <td>  low</td>\n",
        "      <td>  5.468932</td>\n",
        "      <td>  5.373728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 9</td>\n",
        "      <td>  long</td>\n",
        "      <td> high</td>\n",
        "      <td>  2.645938</td>\n",
        "      <td>  2.685415</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "    log_BodyWt life_cat danger_cat       yhat     yhat_2\n",
        "0           -6    short        low  16.660219  17.084153\n",
        "1           -6    short       high  13.837226  14.395840\n",
        "2           -6      med        low  16.660219  16.616439\n",
        "3           -6      med       high  13.837226  13.928126\n",
        "4           -6     long        low  16.660219  17.728076\n",
        "5           -6     long       high  13.837226  15.039763\n",
        "6            9    short        low   5.468932   4.729805\n",
        "7            9    short       high   2.645938   2.041492\n",
        "8            9      med        low   5.468932   4.262091\n",
        "9            9      med       high   2.645938   1.573778\n",
        "10           9     long        low   5.468932   5.373728\n",
        "11           9     long       high   2.645938   2.685415"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since Seaborn plotting expects one, and only one, data frame, in this case we can just append the predictions dataframe onto the sleep dataframe (i.e. we tack it on the end, after the last row in `sleep`). Any columns not present in `predict_frame` are filled with `NaNs`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# munge data togther.\n",
      "merged_df = sleep.append(predict_frame)  # use append method to tack predict_frame onto the end.\n",
      "merged_df.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>BodyWt</th>\n",
        "      <th>BrainWt</th>\n",
        "      <th>Danger</th>\n",
        "      <th>Dreaming</th>\n",
        "      <th>Exposure</th>\n",
        "      <th>Gestation</th>\n",
        "      <th>LifeSpan</th>\n",
        "      <th>NonDreaming</th>\n",
        "      <th>Predation</th>\n",
        "      <th>Species</th>\n",
        "      <th>TotalSleep</th>\n",
        "      <th>danger_cat</th>\n",
        "      <th>life_cat</th>\n",
        "      <th>log_BodyWt</th>\n",
        "      <th>yhat</th>\n",
        "      <th>yhat_2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> high</td>\n",
        "      <td> short</td>\n",
        "      <td> 9</td>\n",
        "      <td> 2.645938</td>\n",
        "      <td> 2.041492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  low</td>\n",
        "      <td>   med</td>\n",
        "      <td> 9</td>\n",
        "      <td> 5.468932</td>\n",
        "      <td> 4.262091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> high</td>\n",
        "      <td>   med</td>\n",
        "      <td> 9</td>\n",
        "      <td> 2.645938</td>\n",
        "      <td> 1.573778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  low</td>\n",
        "      <td>  long</td>\n",
        "      <td> 9</td>\n",
        "      <td> 5.468932</td>\n",
        "      <td> 5.373728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> high</td>\n",
        "      <td>  long</td>\n",
        "      <td> 9</td>\n",
        "      <td> 2.645938</td>\n",
        "      <td> 2.685415</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "    BodyWt  BrainWt  Danger  Dreaming  Exposure  Gestation  LifeSpan  \\\n",
        "7      NaN      NaN     NaN       NaN       NaN        NaN       NaN   \n",
        "8      NaN      NaN     NaN       NaN       NaN        NaN       NaN   \n",
        "9      NaN      NaN     NaN       NaN       NaN        NaN       NaN   \n",
        "10     NaN      NaN     NaN       NaN       NaN        NaN       NaN   \n",
        "11     NaN      NaN     NaN       NaN       NaN        NaN       NaN   \n",
        "\n",
        "    NonDreaming  Predation Species  TotalSleep danger_cat life_cat  \\\n",
        "7           NaN        NaN     NaN         NaN       high    short   \n",
        "8           NaN        NaN     NaN         NaN        low      med   \n",
        "9           NaN        NaN     NaN         NaN       high      med   \n",
        "10          NaN        NaN     NaN         NaN        low     long   \n",
        "11          NaN        NaN     NaN         NaN       high     long   \n",
        "\n",
        "    log_BodyWt      yhat    yhat_2  \n",
        "7            9  2.645938  2.041492  \n",
        "8            9  5.468932  4.262091  \n",
        "9            9  2.645938  1.573778  \n",
        "10           9  5.468932  5.373728  \n",
        "11           9  2.645938  2.685415  "
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now let's do the plots again, using merged frame:\n",
      "g = sns.FacetGrid(merged_df, hue='danger_cat', size=5)\n",
      "# use the `map` method to add stuff to the facetgrid axes:\n",
      "g.map(plt.plot, \"log_BodyWt\", \"yhat\")\n",
      "g.map(plt.scatter, \"log_BodyWt\", \"TotalSleep\")\n",
      "sns.despine(offset=10)\n",
      "g.set_xlabels('Bodyweight [log kg]')\n",
      "g.set_ylabels('Sleep [hours]')\n",
      "g.add_legend(title='Danger');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 364,
         "width": 416
        }
       },
       "output_type": "display_data",
       "png": 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670OuvvlZAKY+8ipTH3mV2t1G8N09R/ZqXyq8fn37sdeGO3L7S9Py1p33YOY+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\nIiJSVf4HK+IWh1md7ZIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cfdd908>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = sns.FacetGrid(merged_df, hue='danger_cat', col='life_cat', size=3)\n",
      "# use the `map` method to add stuff to the facetgrid axes:\n",
      "g.map(plt.plot, \"log_BodyWt\", \"yhat_2\")\n",
      "g.map(plt.scatter, \"log_BodyWt\", \"TotalSleep\")\n",
      "sns.despine(offset=10)\n",
      "g.fig.subplots_adjust(wspace=0.3)\n",
      "g.set_xlabels('Bodyweight [log kg]')\n",
      "g.set_ylabels('Sleep [hours]')\n",
      "g.add_legend(title='Danger');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {
        "png": {
         "height": 221,
         "width": 707
        }
       },
       "output_type": "display_data",
       "png": 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K7yQuMR3atefQjcrp3K7jAus6t+vIoRuVe4FVUt7l5NJkCGE/4GzgJ6BlavFh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d6ba898>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So now we have some nice plots, showing how the categorical variables (danger and lifespan) change the model predictions. Note how the slope of the relationship between bodyweight and sleep is the same in all panels -- that's by design in the model. Adding in the categorical variables only changes the intercepts, not the slope. Changing the slopes requires having an *interaction* term... which we will talk about next week."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "\n",
      "\n",
      "def css_styling():\n",
      "    styles = open(\"../custom_style.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-weight: bold;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-style: oblique;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n",
        "    }\n",
        "    @font-face {\n",
        "        font-family: \"Computer Modern\";\n",
        "        font-weight: bold;\n",
        "        font-style: oblique;\n",
        "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
        "    }\n",
        "    div.cell{\n",
        "        width:850px;\n",
        "        font-size: 11pt;\n",
        "        margin-left:16% !important;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    h1 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h2 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h3 {\n",
        "        font-family: Minion Pro, serif;\n",
        "    }\n",
        "    h4{\n",
        "        margin-top:12px;\n",
        "        margin-bottom: 3px;\n",
        "       }\n",
        "    div.text_cell_render{\n",
        "        font-family: Optima, sans-serif;\n",
        "        line-height: 145%;\n",
        "        font-size: 12pt;\n",
        "        width:850px;\n",
        "        margin-left:auto;\n",
        "        margin-right:auto;\n",
        "    }\n",
        "    .CodeMirror{\n",
        "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
        "    }\n",
        "    .prompt{\n",
        "        display: None;\n",
        "    }\n",
        "    .text_cell_render h5 {\n",
        "        font-weight: 300;\n",
        "        font-size: 12pt;\n",
        "        color: #4057A1;\n",
        "        font-style: italic;\n",
        "        margin-bottom: .5em;\n",
        "        margin-top: 0.5em;\n",
        "        display: block;\n",
        "    }\n",
        "\n",
        "    .warning{\n",
        "        color: rgb( 240, 20, 20 )\n",
        "        }\n",
        "</style>\n",
        "<script>\n",
        "    MathJax.Hub.Config({\n",
        "                        TeX: {\n",
        "                           extensions: [\"AMSmath.js\"]\n",
        "                           },\n",
        "                tex2jax: {\n",
        "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
        "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
        "                }\n",
        "        });\n",
        "</script>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x105e0ccc0>"
       ]
      }
     ],
     "prompt_number": 1
    }
   ],
   "metadata": {}
  }
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}