week2/model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Setup\" data-toc-modified-id=\"Setup-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Setup</a></div><div class=\"lev1 toc-item\"><a href=\"#Last-time\" data-toc-modified-id=\"Last-time-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Last time</a></div><div class=\"lev1 toc-item\"><a href=\"#Evaluation\" data-toc-modified-id=\"Evaluation-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Evaluation</a></div><div class=\"lev2 toc-item\"><a href=\"#Plotting-predicted-vs.-actual-values\" data-toc-modified-id=\"Plotting-predicted-vs.-actual-values-31\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Plotting predicted vs. actual values</a></div><div class=\"lev2 toc-item\"><a href=\"#RMSE\" data-toc-modified-id=\"RMSE-32\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>RMSE</a></div><div class=\"lev2 toc-item\"><a href=\"#$R^2$\" data-toc-modified-id=\"$R^2$-33\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span><span class=\"MathJax_Preview\" style=\"color: inherit;\"></span><span class=\"MathJax\" id=\"MathJax-Element-27-Frame\" tabindex=\"0\" data-mathml=\"&lt;math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;&gt;&lt;msup&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msup&gt;&lt;/math&gt;\" role=\"presentation\" style=\"position: relative;\"><nobr aria-hidden=\"true\"><span class=\"math\" id=\"MathJax-Span-131\" role=\"math\" style=\"width: 1.29em; display: inline-block;\"><span style=\"display: inline-block; position: relative; width: 1.063em; height: 0px; font-size: 120%;\"><span style=\"position: absolute; clip: rect(1.252em, 1001.063em, 2.313em, -999.998em); top: -2.195em; left: 0.002em;\"><span class=\"mrow\" id=\"MathJax-Span-132\"><span class=\"msubsup\" id=\"MathJax-Span-133\"><span style=\"display: inline-block; position: relative; width: 1.025em; height: 0px;\"><span style=\"position: absolute; clip: rect(3.259em, 1000.608em, 4.131em, -999.998em); top: -4.013em; left: 0.002em;\"><span class=\"mi\" id=\"MathJax-Span-134\" style=\"font-family: STIXMathJax_Main-italic;\">R</span><span style=\"display: inline-block; width: 0px; height: 4.017em;\"></span></span><span style=\"position: absolute; top: -4.392em; left: 0.608em;\"><span class=\"mn\" id=\"MathJax-Span-135\" style=\"font-size: 70.7%; font-family: STIXMathJax_Main;\">2</span><span style=\"display: inline-block; width: 0px; height: 4.017em;\"></span></span></span></span></span><span style=\"display: inline-block; width: 0px; height: 2.199em;\"></span></span></span><span style=\"display: inline-block; overflow: hidden; vertical-align: -0.043em; border-left-width: 0px; border-left-style: solid; width: 0px; height: 1.093em;\"></span></span></nobr><span class=\"MJX_Assistive_MathML\" role=\"presentation\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"><msup><mi>R</mi><mn>2</mn></msup></math></span></span><script type=\"math/tex\" id=\"MathJax-Element-27\">R^2</script></a></div><div class=\"lev1 toc-item\"><a href=\"#Overfitting\" data-toc-modified-id=\"Overfitting-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Overfitting</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup\n",
    "\n",
    "First we'll load some packages, including the recent `modelr` for easy modeling, setting options to warn us whenever observations with missing values are ignored by our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(scales)\n",
    "\n",
    "library(modelr)\n",
    "options(na.action = na.warn)\n",
    "\n",
    "library(broom)\n",
    "\n",
    "theme_set(theme_bw())\n",
    "options(repr.plot.width=4, repr.plot.height=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll read in a file from last lecture with information on 225,000 anonymous Nielsen panelists, containing their age, gender, and the number of pages (~distinct urls) they typically access on their web browser each day, filtering out users with zero pageviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  age = col_integer(),\n",
      "  gender = col_character(),\n",
      "  daily.views = col_double()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "users <- read_tsv(gzfile('users.tsv.gz'))\n",
    "\n",
    "model_data <- filter(users, daily.views > 0, age >= 18 & age <= 65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Last time\n",
    "\n",
    "Now we'll fit the quadratic model from last time and remind ourselves of the design matrix and results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>(Intercept)</th><th scope=col>genderMale</th><th scope=col>age</th><th scope=col>I(age^2)</th><th scope=col>genderMale:age</th><th scope=col>genderMale:I(age^2)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1   </td><td>0   </td><td>55  </td><td>3025</td><td> 0  </td><td>   0</td></tr>\n",
       "\t<tr><td>1   </td><td>1   </td><td>19  </td><td> 361</td><td>19  </td><td> 361</td></tr>\n",
       "\t<tr><td>1   </td><td>0   </td><td>33  </td><td>1089</td><td> 0  </td><td>   0</td></tr>\n",
       "\t<tr><td>1   </td><td>1   </td><td>38  </td><td>1444</td><td>38  </td><td>1444</td></tr>\n",
       "\t<tr><td>1   </td><td>0   </td><td>65  </td><td>4225</td><td> 0  </td><td>   0</td></tr>\n",
       "\t<tr><td>1   </td><td>0   </td><td>20  </td><td> 400</td><td> 0  </td><td>   0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " (Intercept) & genderMale & age & I(age\\textasciicircum{}2) & genderMale:age & genderMale:I(age\\textasciicircum{}2)\\\\\n",
       "\\hline\n",
       "\t 1    & 0    & 55   & 3025 &  0   &    0\\\\\n",
       "\t 1    & 1    & 19   &  361 & 19   &  361\\\\\n",
       "\t 1    & 0    & 33   & 1089 &  0   &    0\\\\\n",
       "\t 1    & 1    & 38   & 1444 & 38   & 1444\\\\\n",
       "\t 1    & 0    & 65   & 4225 &  0   &    0\\\\\n",
       "\t 1    & 0    & 20   &  400 &  0   &    0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| (Intercept) | genderMale | age | I(age^2) | genderMale:age | genderMale:I(age^2) |\n",
       "|---|---|---|---|---|---|\n",
       "| 1    | 0    | 55   | 3025 |  0   |    0 |\n",
       "| 1    | 1    | 19   |  361 | 19   |  361 |\n",
       "| 1    | 0    | 33   | 1089 |  0   |    0 |\n",
       "| 1    | 1    | 38   | 1444 | 38   | 1444 |\n",
       "| 1    | 0    | 65   | 4225 |  0   |    0 |\n",
       "| 1    | 0    | 20   |  400 |  0   |    0 |\n",
       "\n"
      ],
      "text/plain": [
       "  (Intercept) genderMale age I(age^2) genderMale:age genderMale:I(age^2)\n",
       "1 1           0          55  3025      0                0               \n",
       "2 1           1          19   361     19              361               \n",
       "3 1           0          33  1089      0                0               \n",
       "4 1           1          38  1444     38             1444               \n",
       "5 1           0          65  4225      0                0               \n",
       "6 1           0          20   400      0                0               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "form <- as.formula(log10(daily.views) ~ gender * (age + I(age^2)))\n",
    "M <- model.matrix(form, model_data)\n",
    "head(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)        </td><td> 1.3111470352      </td><td>2.022344e-02       </td><td> 64.83304          </td><td> 0.000000e+00      </td></tr>\n",
       "\t<tr><td>genderMale         </td><td> 0.5702781647      </td><td>2.995316e-02       </td><td> 19.03900          </td><td> 9.614887e-81      </td></tr>\n",
       "\t<tr><td>age                </td><td> 0.0253548721      </td><td>1.049913e-03       </td><td> 24.14950          </td><td>1.174116e-128      </td></tr>\n",
       "\t<tr><td>I(age^2)           </td><td>-0.0003252173      </td><td>1.274476e-05       </td><td>-25.51772          </td><td>2.169909e-143      </td></tr>\n",
       "\t<tr><td>genderMale:age     </td><td>-0.0386189907      </td><td>1.585310e-03       </td><td>-24.36052          </td><td>7.076032e-131      </td></tr>\n",
       "\t<tr><td>genderMale:I(age^2)</td><td> 0.0004569954      </td><td>1.942110e-05       </td><td> 23.53086          </td><td>2.932671e-122      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " term & estimate & std.error & statistic & p.value\\\\\n",
       "\\hline\n",
       "\t (Intercept)         &  1.3111470352       & 2.022344e-02        &  64.83304           &  0.000000e+00      \\\\\n",
       "\t genderMale          &  0.5702781647       & 2.995316e-02        &  19.03900           &  9.614887e-81      \\\\\n",
       "\t age                 &  0.0253548721       & 1.049913e-03        &  24.14950           & 1.174116e-128      \\\\\n",
       "\t I(age\\textasciicircum{}2)            & -0.0003252173                         & 1.274476e-05                          & -25.51772                             & 2.169909e-143                        \\\\\n",
       "\t genderMale:age      & -0.0386189907       & 1.585310e-03        & -24.36052           & 7.076032e-131      \\\\\n",
       "\t genderMale:I(age\\textasciicircum{}2) &  0.0004569954                         & 1.942110e-05                          &  23.53086                             & 2.932671e-122                        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| term | estimate | std.error | statistic | p.value |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept)         |  1.3111470352       | 2.022344e-02        |  64.83304           |  0.000000e+00       |\n",
       "| genderMale          |  0.5702781647       | 2.995316e-02        |  19.03900           |  9.614887e-81       |\n",
       "| age                 |  0.0253548721       | 1.049913e-03        |  24.14950           | 1.174116e-128       |\n",
       "| I(age^2)            | -0.0003252173       | 1.274476e-05        | -25.51772           | 2.169909e-143       |\n",
       "| genderMale:age      | -0.0386189907       | 1.585310e-03        | -24.36052           | 7.076032e-131       |\n",
       "| genderMale:I(age^2) |  0.0004569954       | 1.942110e-05        |  23.53086           | 2.932671e-122       |\n",
       "\n"
      ],
      "text/plain": [
       "  term                estimate      std.error    statistic p.value      \n",
       "1 (Intercept)          1.3111470352 2.022344e-02  64.83304  0.000000e+00\n",
       "2 genderMale           0.5702781647 2.995316e-02  19.03900  9.614887e-81\n",
       "3 age                  0.0253548721 1.049913e-03  24.14950 1.174116e-128\n",
       "4 I(age^2)            -0.0003252173 1.274476e-05 -25.51772 2.169909e-143\n",
       "5 genderMale:age      -0.0386189907 1.585310e-03 -24.36052 7.076032e-131\n",
       "6 genderMale:I(age^2)  0.0004569954 1.942110e-05  23.53086 2.932671e-122"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.01927013</td><td>0.01924476</td><td>0.6689456 </td><td>759.6056  </td><td>0         </td><td>6         </td><td>-196563.1 </td><td>393140.2  </td><td>393211.4  </td><td>86497.68  </td><td>193296    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       "\\hline\n",
       "\t 0.01927013 & 0.01924476 & 0.6689456  & 759.6056   & 0          & 6          & -196563.1  & 393140.2   & 393211.4   & 86497.68   & 193296    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.01927013 | 0.01924476 | 0.6689456  | 759.6056   | 0          | 6          | -196563.1  | 393140.2   | 393211.4   | 86497.68   | 193296     |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared  adj.r.squared sigma     statistic p.value df logLik    AIC     \n",
       "1 0.01927013 0.01924476    0.6689456 759.6056  0       6  -196563.1 393140.2\n",
       "  BIC      deviance df.residual\n",
       "1 393211.4 86497.68 193296     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- lm(form, model_data)\n",
    "tidy(model)\n",
    "glance(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NIcHc9oVoPKhM4hD263saja/kbWrKHgd99SaOp30URJ9MTWFJ7mQ6Di0XNfsjUo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HMsWxxx4W4YWOB0/Tw4ek6eqSGtGUlxvZ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\ndrfvaXl6vug9Ns0kz+Zvx7/mcleBbLJIoXNMJZdlrCfJM1+ujHs1tHAdiLj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seuh82bY/1mWM6tg16+XtLh39sblSt8H9sDobpeZo7jon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\nBBBAAAEEEEAAgfAE/gOB+G4/17DKHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data <- model_data %>%\n",
    "  group_by(age, gender) %>%\n",
    "  summarize(count = n(),\n",
    "            geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%\n",
    "  add_predictions(model) %>%\n",
    "  mutate(pred = 10^pred)\n",
    "\n",
    "options(repr.plot.width=6, repr.plot.height=3)\n",
    "ggplot(plot_data, aes(x = age, y = pred, color = gender)) +\n",
    "  geom_line(aes(y = pred)) +\n",
    "  geom_point(aes(y = geom_mean_daily_views, size = count)) +\n",
    "  scale_size_area(guide = F)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation\n",
    "\n",
    "We decided that this looked reasonable last lecture in that the model was capturing the trends in the empirical averages by age and gender.\n",
    "\n",
    "Let's first look at some more detailed plots to get a sense of how well the predicted and actual values match\n",
    "\n",
    "## Plotting predicted vs. actual values\n",
    "\n",
    "It's always a good idea to plot predicted vs. actual values along with a diagonal line to indicate how a perfect model would fit.\n",
    "\n",
    "Often it's useful to bin points to show, for instance, the average actual outcome for all observations that were predicted to be in a small range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MpWrVple+e33HKLDZcqzvXQGLC7kvoU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\n6H55AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=4, repr.plot.height=3)\n",
    "\n",
    "ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views)) +\n",
    "  geom_point() +\n",
    "  geom_abline(linetype = \"dashed\") +\n",
    "  xlab('Predicted') +\n",
    "  ylab('Actual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use other aesthetics to add more information on these plots.\n",
    "This can help identify where any systematic error might be creeping in.\n",
    "\n",
    "For instance, we can color the points by gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uWoUaOsSCZYjjRnzhyldPE2OXXqVPUmj7kgESFQUAnYF8wn59Yt3v6OYUx4LpUy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\nw03sG2+8oZxLwfkUdBo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vP0xAJWwGOa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E3hlakfaLCSrJfrwr0ebA68WHQHS/rPF5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\n6vTm8XM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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views, color = gender)) +\n",
    "  geom_point() +\n",
    "  geom_abline(linetype = \"dashed\") +\n",
    "  xlab('Predicted') +\n",
    "  ylab('Actual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can try to add age as well, although it's difficult to read the continuous color scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7inTC+WKtURjjn+nj2MihbIqVmMjxkBmyBw584dafiXPXt2+t///meTNrkR+yKQ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40atdfahRNcshA1HF+uElh+pf//qX3MR/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\nTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(plot_data, aes(x = pred, y = geom_mean_daily_views, color = age)) +\n",
    "  geom_point() +\n",
    "  geom_abline(linetype = \"dashed\") +\n",
    "  xlab('Predicted') +\n",
    "  ylab('Actual') +\n",
    "  facet_wrap(~ gender, scale = \"free\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can do the same without binning.\n",
    "This shows all individual predictions.\n",
    "\n",
    "Trying to show 225,000 points is never a good idea, but in this case it demostrates how much variation there is at the individual level.\n",
    "\n",
    "So even though we've done a reasonable job modeling the averages, there's still lots of unexplained variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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enQ9sILL+ztN+yWHmJ1IgmX6IKMdEhu7dkruXIgERprQDf6khcPbGoqAWsXfLG/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VG0F7ia0qMFuZKYq91UloKND7rAzf8R3f8Wz2QsujH/3olew+q0w8tyYG9atTs1Vb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9IFEgkb6CfpAdeSgBULW0ucVo0SUoAsZv1W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\ngIcAk6cTgUQgEUgEEoFJIvC/O0zaFKO0MMoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_actual <- model_data %>%\n",
    "  add_predictions(model) %>%\n",
    "  mutate(actual = log10(daily.views))\n",
    "\n",
    "ggplot(pred_actual, aes(x = 10^pred, y = 10^actual)) +\n",
    "  geom_point(alpha = 0.1) +\n",
    "  geom_abline(linetype = \"dashed\") +\n",
    "  scale_x_log10(label = comma, breaks = seq(0,100,by=10)) +\n",
    "  scale_y_log10(label = comma) +\n",
    "  xlab('Predicted') +\n",
    "  ylab('Actual')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSE\n",
    "\n",
    "We can quantify the model fit in a number of ways.\n",
    "\n",
    "One natural choice is to use the squared loss that was used in fitting the model, but to take a square root to adjust units to match those of the outcome (e.g., pageviews in this case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>rmse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6689352</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " rmse\\\\\n",
       "\\hline\n",
       "\t 0.6689352\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| rmse |\n",
       "|---|\n",
       "| 0.6689352 |\n",
       "\n"
      ],
      "text/plain": [
       "  rmse     \n",
       "1 0.6689352"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_actual %>%\n",
    "  summarize(rmse = sqrt(mean((pred - actual)^2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is easy to compute, but a bit hard to interpret.\n",
    "Especially because it represents an average error in log-pageview space.\n",
    "\n",
    "We can always convert back to pageviews and measure the RMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>rmse</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>165.2601</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " rmse\\\\\n",
       "\\hline\n",
       "\t 165.2601\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| rmse |\n",
       "|---|\n",
       "| 165.2601 |\n",
       "\n"
      ],
      "text/plain": [
       "  rmse    \n",
       "1 165.2601"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_actual %>%\n",
    "  summarize(rmse = sqrt(mean((10^pred - 10^actual)^2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have a better sense of scale here, we can tell that an average error of 165 pageviews is a relatively poor fit!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $R^2$\n",
    "\n",
    "When we don't have a sense of scale, though, we can use the coefficient of determination, or $R^2$, to get a measure of performance.\n",
    "$R^2$ measures the fraction of variance in outcomes explained by the model.\n",
    "\n",
    "One way to compute this is to look at the Pearson correlation coefficient between predicted and actual values, and square it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>rmse</th><th scope=col>cor</th><th scope=col>cor_sq</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.6689352 </td><td>0.1388169 </td><td>0.01927013</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " rmse & cor & cor\\_sq\\\\\n",
       "\\hline\n",
       "\t 0.6689352  & 0.1388169  & 0.01927013\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| rmse | cor | cor_sq |\n",
       "|---|---|---|\n",
       "| 0.6689352  | 0.1388169  | 0.01927013 |\n",
       "\n"
      ],
      "text/plain": [
       "  rmse      cor       cor_sq    \n",
       "1 0.6689352 0.1388169 0.01927013"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_actual %>%\n",
    "  summarize(rmse = sqrt(mean((pred - actual)^2)),\n",
    "            cor = cor(pred, actual),\n",
    "            cor_sq = cor^2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are functions in `modelr` for doing all of these more easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.668935201902014"
      ],
      "text/latex": [
       "0.668935201902014"
      ],
      "text/markdown": [
       "0.668935201902014"
      ],
      "text/plain": [
       "[1] 0.6689352"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.0192701316722141"
      ],
      "text/latex": [
       "0.0192701316722141"
      ],
      "text/markdown": [
       "0.0192701316722141"
      ],
      "text/plain": [
       "[1] 0.01927013"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rmse(model, model_data)\n",
    "rsquare(model, model_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This matches the reported $R^2$ in `summary(model)$r.squared` or `glance(model)`, and means that we're explaining just 2% of the variance in outcomes, verifying what we saw in the noisy scatter plot above.\n",
    "\n",
    "This is equivalent as comparing the mean squared error (MSE) of the model to the MSE of a simple baseline where we always predict the average outcome.\n",
    "We can compute the performance of this baseline explicitly and compare things to verify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>mse</th><th scope=col>mse_baseline</th><th scope=col>mse_vs_baseline</th><th scope=col>cor</th><th scope=col>cor_sq</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.4474743 </td><td>0.4562666 </td><td>0.01927013</td><td>0.1388169 </td><td>0.01927013</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " mse & mse\\_baseline & mse\\_vs\\_baseline & cor & cor\\_sq\\\\\n",
       "\\hline\n",
       "\t 0.4474743  & 0.4562666  & 0.01927013 & 0.1388169  & 0.01927013\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| mse | mse_baseline | mse_vs_baseline | cor | cor_sq |\n",
       "|---|---|---|---|---|\n",
       "| 0.4474743  | 0.4562666  | 0.01927013 | 0.1388169  | 0.01927013 |\n",
       "\n"
      ],
      "text/plain": [
       "  mse       mse_baseline mse_vs_baseline cor       cor_sq    \n",
       "1 0.4474743 0.4562666    0.01927013      0.1388169 0.01927013"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_actual %>%\n",
    "  summarize(mse = mean((pred - actual)^2),\n",
    "            mse_baseline = mean((mean(actual) - actual)^2),\n",
    "            mse_vs_baseline = (mse_baseline - mse) / mse_baseline,\n",
    "            cor = cor(pred, actual),\n",
    "            cor_sq = cor^2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overfitting\n",
    "\n",
    "Finally, we can make the point that a bigger model is not always a better one, for instance by fitting a ridiculously complex 30-degree polynomial to the browsing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>(Intercept)</th><th scope=col>genderMale</th><th scope=col>poly(age, K, raw = T)1</th><th scope=col>poly(age, K, raw = T)2</th><th scope=col>poly(age, K, raw = T)3</th><th scope=col>poly(age, K, raw = T)4</th><th scope=col>poly(age, K, raw = T)5</th><th scope=col>poly(age, K, raw = T)6</th><th scope=col>poly(age, K, raw = T)7</th><th scope=col>poly(age, K, raw = T)8</th><th scope=col>⋯</th><th scope=col>genderMale:poly(age, K, raw = T)21</th><th scope=col>genderMale:poly(age, K, raw = T)22</th><th scope=col>genderMale:poly(age, K, raw = T)23</th><th scope=col>genderMale:poly(age, K, raw = T)24</th><th scope=col>genderMale:poly(age, K, raw = T)25</th><th scope=col>genderMale:poly(age, K, raw = T)26</th><th scope=col>genderMale:poly(age, K, raw = T)27</th><th scope=col>genderMale:poly(age, K, raw = T)28</th><th scope=col>genderMale:poly(age, K, raw = T)29</th><th scope=col>genderMale:poly(age, K, raw = T)30</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1           </td><td>0           </td><td>55          </td><td>3025        </td><td>166375      </td><td> 9150625    </td><td> 503284375  </td><td>27680640625 </td><td>1.522435e+12</td><td>8.373394e+13</td><td>⋯           </td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>1           </td><td>1           </td><td>19          </td><td> 361        </td><td>  6859      </td><td>  130321    </td><td>   2476099  </td><td>   47045881 </td><td>8.938717e+08</td><td>1.698356e+10</td><td>⋯           </td><td>7.142095e+26</td><td>1.356998e+28</td><td>2.578296e+29</td><td>4.898763e+30</td><td>9.307650e+31</td><td>1.768453e+33</td><td>3.360061e+34</td><td>6.384117e+35</td><td>1.212982e+37</td><td>2.304666e+38</td></tr>\n",
       "\t<tr><td>1           </td><td>0           </td><td>33          </td><td>1089        </td><td> 35937      </td><td> 1185921    </td><td>  39135393  </td><td> 1291467969 </td><td>4.261844e+10</td><td>1.406409e+12</td><td>⋯           </td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>1           </td><td>1           </td><td>38          </td><td>1444        </td><td> 54872      </td><td> 2085136    </td><td>  79235168  </td><td> 3010936384 </td><td>1.144156e+11</td><td>4.347792e+12</td><td>⋯           </td><td>1.497806e+33</td><td>5.691662e+34</td><td>2.162832e+36</td><td>8.218760e+37</td><td>3.123129e+39</td><td>1.186789e+41</td><td>4.509798e+42</td><td>1.713723e+44</td><td>6.512149e+45</td><td>2.474616e+47</td></tr>\n",
       "\t<tr><td>1           </td><td>0           </td><td>65          </td><td>4225        </td><td>274625      </td><td>17850625    </td><td>1160290625  </td><td>75418890625 </td><td>4.902228e+12</td><td>3.186448e+14</td><td>⋯           </td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td></tr>\n",
       "\t<tr><td>1           </td><td>0           </td><td>20          </td><td> 400        </td><td>  8000      </td><td>  160000    </td><td>   3200000  </td><td>   64000000 </td><td>1.280000e+09</td><td>2.560000e+10</td><td>⋯           </td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td><td>0.000000e+00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " (Intercept) & genderMale & poly(age, K, raw = T)1 & poly(age, K, raw = T)2 & poly(age, K, raw = T)3 & poly(age, K, raw = T)4 & poly(age, K, raw = T)5 & poly(age, K, raw = T)6 & poly(age, K, raw = T)7 & poly(age, K, raw = T)8 & ⋯ & genderMale:poly(age, K, raw = T)21 & genderMale:poly(age, K, raw = T)22 & genderMale:poly(age, K, raw = T)23 & genderMale:poly(age, K, raw = T)24 & genderMale:poly(age, K, raw = T)25 & genderMale:poly(age, K, raw = T)26 & genderMale:poly(age, K, raw = T)27 & genderMale:poly(age, K, raw = T)28 & genderMale:poly(age, K, raw = T)29 & genderMale:poly(age, K, raw = T)30\\\\\n",
       "\\hline\n",
       "\t 1            & 0            & 55           & 3025         & 166375       &  9150625     &  503284375   & 27680640625  & 1.522435e+12 & 8.373394e+13 & ⋯            & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00\\\\\n",
       "\t 1            & 1            & 19           &  361         &   6859       &   130321     &    2476099   &    47045881  & 8.938717e+08 & 1.698356e+10 & ⋯            & 7.142095e+26 & 1.356998e+28 & 2.578296e+29 & 4.898763e+30 & 9.307650e+31 & 1.768453e+33 & 3.360061e+34 & 6.384117e+35 & 1.212982e+37 & 2.304666e+38\\\\\n",
       "\t 1            & 0            & 33           & 1089         &  35937       &  1185921     &   39135393   &  1291467969  & 4.261844e+10 & 1.406409e+12 & ⋯            & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00\\\\\n",
       "\t 1            & 1            & 38           & 1444         &  54872       &  2085136     &   79235168   &  3010936384  & 1.144156e+11 & 4.347792e+12 & ⋯            & 1.497806e+33 & 5.691662e+34 & 2.162832e+36 & 8.218760e+37 & 3.123129e+39 & 1.186789e+41 & 4.509798e+42 & 1.713723e+44 & 6.512149e+45 & 2.474616e+47\\\\\n",
       "\t 1            & 0            & 65           & 4225         & 274625       & 17850625     & 1160290625   & 75418890625  & 4.902228e+12 & 3.186448e+14 & ⋯            & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00\\\\\n",
       "\t 1            & 0            & 20           &  400         &   8000       &   160000     &    3200000   &    64000000  & 1.280000e+09 & 2.560000e+10 & ⋯            & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00 & 0.000000e+00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| (Intercept) | genderMale | poly(age, K, raw = T)1 | poly(age, K, raw = T)2 | poly(age, K, raw = T)3 | poly(age, K, raw = T)4 | poly(age, K, raw = T)5 | poly(age, K, raw = T)6 | poly(age, K, raw = T)7 | poly(age, K, raw = T)8 | ⋯ | genderMale:poly(age, K, raw = T)21 | genderMale:poly(age, K, raw = T)22 | genderMale:poly(age, K, raw = T)23 | genderMale:poly(age, K, raw = T)24 | genderMale:poly(age, K, raw = T)25 | genderMale:poly(age, K, raw = T)26 | genderMale:poly(age, K, raw = T)27 | genderMale:poly(age, K, raw = T)28 | genderMale:poly(age, K, raw = T)29 | genderMale:poly(age, K, raw = T)30 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1            | 0            | 55           | 3025         | 166375       |  9150625     |  503284375   | 27680640625  | 1.522435e+12 | 8.373394e+13 | ⋯            | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |\n",
       "| 1            | 1            | 19           |  361         |   6859       |   130321     |    2476099   |    47045881  | 8.938717e+08 | 1.698356e+10 | ⋯            | 7.142095e+26 | 1.356998e+28 | 2.578296e+29 | 4.898763e+30 | 9.307650e+31 | 1.768453e+33 | 3.360061e+34 | 6.384117e+35 | 1.212982e+37 | 2.304666e+38 |\n",
       "| 1            | 0            | 33           | 1089         |  35937       |  1185921     |   39135393   |  1291467969  | 4.261844e+10 | 1.406409e+12 | ⋯            | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |\n",
       "| 1            | 1            | 38           | 1444         |  54872       |  2085136     |   79235168   |  3010936384  | 1.144156e+11 | 4.347792e+12 | ⋯            | 1.497806e+33 | 5.691662e+34 | 2.162832e+36 | 8.218760e+37 | 3.123129e+39 | 1.186789e+41 | 4.509798e+42 | 1.713723e+44 | 6.512149e+45 | 2.474616e+47 |\n",
       "| 1            | 0            | 65           | 4225         | 274625       | 17850625     | 1160290625   | 75418890625  | 4.902228e+12 | 3.186448e+14 | ⋯            | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |\n",
       "| 1            | 0            | 20           |  400         |   8000       |   160000     |    3200000   |    64000000  | 1.280000e+09 | 2.560000e+10 | ⋯            | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 |\n",
       "\n"
      ],
      "text/plain": [
       "  (Intercept) genderMale poly(age, K, raw = T)1 poly(age, K, raw = T)2\n",
       "1 1           0          55                     3025                  \n",
       "2 1           1          19                      361                  \n",
       "3 1           0          33                     1089                  \n",
       "4 1           1          38                     1444                  \n",
       "5 1           0          65                     4225                  \n",
       "6 1           0          20                      400                  \n",
       "  poly(age, K, raw = T)3 poly(age, K, raw = T)4 poly(age, K, raw = T)5\n",
       "1 166375                  9150625                503284375            \n",
       "2   6859                   130321                  2476099            \n",
       "3  35937                  1185921                 39135393            \n",
       "4  54872                  2085136                 79235168            \n",
       "5 274625                 17850625               1160290625            \n",
       "6   8000                   160000                  3200000            \n",
       "  poly(age, K, raw = T)6 poly(age, K, raw = T)7 poly(age, K, raw = T)8 ⋯\n",
       "1 27680640625            1.522435e+12           8.373394e+13           ⋯\n",
       "2    47045881            8.938717e+08           1.698356e+10           ⋯\n",
       "3  1291467969            4.261844e+10           1.406409e+12           ⋯\n",
       "4  3010936384            1.144156e+11           4.347792e+12           ⋯\n",
       "5 75418890625            4.902228e+12           3.186448e+14           ⋯\n",
       "6    64000000            1.280000e+09           2.560000e+10           ⋯\n",
       "  genderMale:poly(age, K, raw = T)21 genderMale:poly(age, K, raw = T)22\n",
       "1 0.000000e+00                       0.000000e+00                      \n",
       "2 7.142095e+26                       1.356998e+28                      \n",
       "3 0.000000e+00                       0.000000e+00                      \n",
       "4 1.497806e+33                       5.691662e+34                      \n",
       "5 0.000000e+00                       0.000000e+00                      \n",
       "6 0.000000e+00                       0.000000e+00                      \n",
       "  genderMale:poly(age, K, raw = T)23 genderMale:poly(age, K, raw = T)24\n",
       "1 0.000000e+00                       0.000000e+00                      \n",
       "2 2.578296e+29                       4.898763e+30                      \n",
       "3 0.000000e+00                       0.000000e+00                      \n",
       "4 2.162832e+36                       8.218760e+37                      \n",
       "5 0.000000e+00                       0.000000e+00                      \n",
       "6 0.000000e+00                       0.000000e+00                      \n",
       "  genderMale:poly(age, K, raw = T)25 genderMale:poly(age, K, raw = T)26\n",
       "1 0.000000e+00                       0.000000e+00                      \n",
       "2 9.307650e+31                       1.768453e+33                      \n",
       "3 0.000000e+00                       0.000000e+00                      \n",
       "4 3.123129e+39                       1.186789e+41                      \n",
       "5 0.000000e+00                       0.000000e+00                      \n",
       "6 0.000000e+00                       0.000000e+00                      \n",
       "  genderMale:poly(age, K, raw = T)27 genderMale:poly(age, K, raw = T)28\n",
       "1 0.000000e+00                       0.000000e+00                      \n",
       "2 3.360061e+34                       6.384117e+35                      \n",
       "3 0.000000e+00                       0.000000e+00                      \n",
       "4 4.509798e+42                       1.713723e+44                      \n",
       "5 0.000000e+00                       0.000000e+00                      \n",
       "6 0.000000e+00                       0.000000e+00                      \n",
       "  genderMale:poly(age, K, raw = T)29 genderMale:poly(age, K, raw = T)30\n",
       "1 0.000000e+00                       0.000000e+00                      \n",
       "2 1.212982e+37                       2.304666e+38                      \n",
       "3 0.000000e+00                       0.000000e+00                      \n",
       "4 6.512149e+45                       2.474616e+47                      \n",
       "5 0.000000e+00                       0.000000e+00                      \n",
       "6 0.000000e+00                       0.000000e+00                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "K <- 30\n",
    "form <- as.formula(log10(daily.views) ~ gender * poly(age, K, raw=T))\n",
    "M <- model.matrix(form, model_data)\n",
    "head(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>r.squared</th><th scope=col>adj.r.squared</th><th scope=col>sigma</th><th scope=col>statistic</th><th scope=col>p.value</th><th scope=col>df</th><th scope=col>logLik</th><th scope=col>AIC</th><th scope=col>BIC</th><th scope=col>deviance</th><th scope=col>df.residual</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.02063472</td><td>0.02043709</td><td>0.6685388 </td><td>104.4085  </td><td>0         </td><td>40        </td><td>-196428.5 </td><td>392939    </td><td>393356.1  </td><td>86377.32  </td><td>193262    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllll}\n",
       " r.squared & adj.r.squared & sigma & statistic & p.value & df & logLik & AIC & BIC & deviance & df.residual\\\\\n",
       "\\hline\n",
       "\t 0.02063472 & 0.02043709 & 0.6685388  & 104.4085   & 0          & 40         & -196428.5  & 392939     & 393356.1   & 86377.32   & 193262    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.02063472 | 0.02043709 | 0.6685388  | 104.4085   | 0          | 40         | -196428.5  | 392939     | 393356.1   | 86377.32   | 193262     |\n",
       "\n"
      ],
      "text/plain": [
       "  r.squared  adj.r.squared sigma     statistic p.value df logLik    AIC   \n",
       "1 0.02063472 0.02043709    0.6685388 104.4085  0       40 -196428.5 392939\n",
       "  BIC      deviance df.residual\n",
       "1 393356.1 86377.32 193262     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>term</th><th scope=col>estimate</th><th scope=col>std.error</th><th scope=col>statistic</th><th scope=col>p.value</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>(Intercept)                       </td><td>-1.303723e+05                     </td><td>5.801182e+05                      </td><td>-0.2247340                        </td><td>0.8221865                         </td></tr>\n",
       "\t<tr><td>genderMale                        </td><td>-1.678235e+05                     </td><td>2.093948e+05                      </td><td>-0.8014689                        </td><td>0.4228612                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)1            </td><td> 5.969850e+04                     </td><td>2.748227e+05                      </td><td> 0.2172255                        </td><td>0.8280329                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)2            </td><td>-1.268910e+04                     </td><td>5.980100e+04                      </td><td>-0.2121888                        </td><td>0.8319600                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)3            </td><td> 1.653247e+03                     </td><td>7.903331e+03                      </td><td> 0.2091836                        </td><td>0.8343051                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)4            </td><td>-1.465994e+02                     </td><td>7.059029e+02                      </td><td>-0.2076764                        </td><td>0.8354819                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)5            </td><td> 9.268892e+00                     </td><td>4.476673e+01                      </td><td> 0.2070487                        </td><td>0.8359721                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)6            </td><td>-4.242073e-01                     </td><td>2.053246e+00                      </td><td>-0.2066032                        </td><td>0.8363200                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)7            </td><td> 1.388301e-02                     </td><td>6.753549e-02                      </td><td> 0.2055661                        </td><td>0.8371301                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)8            </td><td>-3.075867e-04                     </td><td>1.514638e-03                      </td><td>-0.2030760                        </td><td>0.8390759                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)9            </td><td> 3.831871e-06                     </td><td>1.933803e-05                      </td><td> 0.1981520                        </td><td>0.8429263                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)11           </td><td>-8.257038e-10                     </td><td>4.691756e-09                      </td><td>-0.1759903                        </td><td>0.8603017                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)12           </td><td> 1.047858e-11                     </td><td>6.751863e-11                      </td><td> 0.1551954                        </td><td>0.8766675                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)14           </td><td>-5.664393e-16                     </td><td>7.196874e-15                      </td><td>-0.0787063                        </td><td>0.9372663                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)17           </td><td>-1.171520e-20                     </td><td>6.436421e-20                      </td><td>-0.1820141                        </td><td>0.8555718                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)18           </td><td> 5.372602e-22                     </td><td>1.973349e-21                      </td><td> 0.2722580                        </td><td>0.7854239                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)19           </td><td>-8.045331e-24                     </td><td>2.490759e-23                      </td><td>-0.3230073                        </td><td>0.7466901                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)21           </td><td> 1.313111e-27                     </td><td>3.930058e-27                      </td><td> 0.3341200                        </td><td>0.7382894                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)22           </td><td>-1.217826e-29                     </td><td>3.797813e-29                      </td><td>-0.3206650                        </td><td>0.7484646                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)25           </td><td> 9.998679e-36                     </td><td>3.778098e-35                      </td><td> 0.2646485                        </td><td>0.7912805                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)26           </td><td>-8.864173e-38                     </td><td>3.621607e-37                      </td><td>-0.2447580                        </td><td>0.8066440                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)28           </td><td> 2.094231e-42                     </td><td>1.021774e-41                      </td><td> 0.2049603                        </td><td>0.8376034                         </td></tr>\n",
       "\t<tr><td>poly(age, K, raw = T)30           </td><td>-3.203491e-47                     </td><td>1.934642e-46                      </td><td>-0.1655857                        </td><td>0.8684832                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)1 </td><td> 7.457309e+04                     </td><td>9.074985e+04                      </td><td> 0.8217434                        </td><td>0.4112240                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)2 </td><td>-1.508358e+04                     </td><td>1.794170e+04                      </td><td>-0.8406994                        </td><td>0.4005174                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)3 </td><td> 1.836561e+03                     </td><td>2.139989e+03                      </td><td> 0.8582104                        </td><td>0.3907774                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)4 </td><td>-1.498901e+02                     </td><td>1.714620e+02                      </td><td>-0.8741885                        </td><td>0.3820167                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)5 </td><td> 8.629959e+00                     </td><td>9.711988e+00                      </td><td> 0.8885883                        </td><td>0.3742255                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)6 </td><td>-3.584726e-01                     </td><td>3.976813e-01                      </td><td>-0.9014068                        </td><td>0.3673732                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)7 </td><td> 1.075606e-02                     </td><td>1.178513e-02                      </td><td> 0.9126800                        </td><td>0.3614120                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)8 </td><td>-2.278584e-04                     </td><td>2.470073e-04                      </td><td>-0.9224766                        </td><td>0.3562812                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)9 </td><td> 3.187705e-06                     </td><td>3.424360e-06                      </td><td> 0.9308907                        </td><td>0.3519113                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)10</td><td>-2.398106e-08                     </td><td>2.556525e-08                      </td><td>-0.9380335                        </td><td>0.3482284                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)12</td><td> 1.235003e-12                     </td><td>1.301384e-12                      </td><td> 0.9489920                        </td><td>0.3426259                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)15</td><td>-2.947183e-18                     </td><td>3.073478e-18                      </td><td>-0.9589081                        </td><td>0.3376062                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)16</td><td> 3.345744e-20                     </td><td>3.481876e-20                      </td><td> 0.9609027                        </td><td>0.3366023                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)19</td><td>-2.876932e-26                     </td><td>2.984011e-26                      </td><td>-0.9641157                        </td><td>0.3349891                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)20</td><td> 2.093682e-28                     </td><td>2.170821e-28                      </td><td> 0.9644653                        </td><td>0.3348139                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)26</td><td>-3.794291e-41                     </td><td>3.946067e-41                      </td><td>-0.9615374                        </td><td>0.3362833                         </td></tr>\n",
       "\t<tr><td>genderMale:poly(age, K, raw = T)28</td><td> 2.404838e-45                     </td><td>2.507426e-45                      </td><td> 0.9590863                        </td><td>0.3375165                         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " term & estimate & std.error & statistic & p.value\\\\\n",
       "\\hline\n",
       "\t (Intercept)                        & -1.303723e+05                      & 5.801182e+05                       & -0.2247340                         & 0.8221865                         \\\\\n",
       "\t genderMale                         & -1.678235e+05                      & 2.093948e+05                       & -0.8014689                         & 0.4228612                         \\\\\n",
       "\t poly(age, K, raw = T)1             &  5.969850e+04                      & 2.748227e+05                       &  0.2172255                         & 0.8280329                         \\\\\n",
       "\t poly(age, K, raw = T)2             & -1.268910e+04                      & 5.980100e+04                       & -0.2121888                         & 0.8319600                         \\\\\n",
       "\t poly(age, K, raw = T)3             &  1.653247e+03                      & 7.903331e+03                       &  0.2091836                         & 0.8343051                         \\\\\n",
       "\t poly(age, K, raw = T)4             & -1.465994e+02                      & 7.059029e+02                       & -0.2076764                         & 0.8354819                         \\\\\n",
       "\t poly(age, K, raw = T)5             &  9.268892e+00                      & 4.476673e+01                       &  0.2070487                         & 0.8359721                         \\\\\n",
       "\t poly(age, K, raw = T)6             & -4.242073e-01                      & 2.053246e+00                       & -0.2066032                         & 0.8363200                         \\\\\n",
       "\t poly(age, K, raw = T)7             &  1.388301e-02                      & 6.753549e-02                       &  0.2055661                         & 0.8371301                         \\\\\n",
       "\t poly(age, K, raw = T)8             & -3.075867e-04                      & 1.514638e-03                       & -0.2030760                         & 0.8390759                         \\\\\n",
       "\t poly(age, K, raw = T)9             &  3.831871e-06                      & 1.933803e-05                       &  0.1981520                         & 0.8429263                         \\\\\n",
       "\t poly(age, K, raw = T)11            & -8.257038e-10                      & 4.691756e-09                       & -0.1759903                         & 0.8603017                         \\\\\n",
       "\t poly(age, K, raw = T)12            &  1.047858e-11                      & 6.751863e-11                       &  0.1551954                         & 0.8766675                         \\\\\n",
       "\t poly(age, K, raw = T)14            & -5.664393e-16                      & 7.196874e-15                       & -0.0787063                         & 0.9372663                         \\\\\n",
       "\t poly(age, K, raw = T)17            & -1.171520e-20                      & 6.436421e-20                       & -0.1820141                         & 0.8555718                         \\\\\n",
       "\t poly(age, K, raw = T)18            &  5.372602e-22                      & 1.973349e-21                       &  0.2722580                         & 0.7854239                         \\\\\n",
       "\t poly(age, K, raw = T)19            & -8.045331e-24                      & 2.490759e-23                       & -0.3230073                         & 0.7466901                         \\\\\n",
       "\t poly(age, K, raw = T)21            &  1.313111e-27                      & 3.930058e-27                       &  0.3341200                         & 0.7382894                         \\\\\n",
       "\t poly(age, K, raw = T)22            & -1.217826e-29                      & 3.797813e-29                       & -0.3206650                         & 0.7484646                         \\\\\n",
       "\t poly(age, K, raw = T)25            &  9.998679e-36                      & 3.778098e-35                       &  0.2646485                         & 0.7912805                         \\\\\n",
       "\t poly(age, K, raw = T)26            & -8.864173e-38                      & 3.621607e-37                       & -0.2447580                         & 0.8066440                         \\\\\n",
       "\t poly(age, K, raw = T)28            &  2.094231e-42                      & 1.021774e-41                       &  0.2049603                         & 0.8376034                         \\\\\n",
       "\t poly(age, K, raw = T)30            & -3.203491e-47                      & 1.934642e-46                       & -0.1655857                         & 0.8684832                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)1  &  7.457309e+04                      & 9.074985e+04                       &  0.8217434                         & 0.4112240                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)2  & -1.508358e+04                      & 1.794170e+04                       & -0.8406994                         & 0.4005174                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)3  &  1.836561e+03                      & 2.139989e+03                       &  0.8582104                         & 0.3907774                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)4  & -1.498901e+02                      & 1.714620e+02                       & -0.8741885                         & 0.3820167                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)5  &  8.629959e+00                      & 9.711988e+00                       &  0.8885883                         & 0.3742255                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)6  & -3.584726e-01                      & 3.976813e-01                       & -0.9014068                         & 0.3673732                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)7  &  1.075606e-02                      & 1.178513e-02                       &  0.9126800                         & 0.3614120                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)8  & -2.278584e-04                      & 2.470073e-04                       & -0.9224766                         & 0.3562812                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)9  &  3.187705e-06                      & 3.424360e-06                       &  0.9308907                         & 0.3519113                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)10 & -2.398106e-08                      & 2.556525e-08                       & -0.9380335                         & 0.3482284                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)12 &  1.235003e-12                      & 1.301384e-12                       &  0.9489920                         & 0.3426259                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)15 & -2.947183e-18                      & 3.073478e-18                       & -0.9589081                         & 0.3376062                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)16 &  3.345744e-20                      & 3.481876e-20                       &  0.9609027                         & 0.3366023                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)19 & -2.876932e-26                      & 2.984011e-26                       & -0.9641157                         & 0.3349891                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)20 &  2.093682e-28                      & 2.170821e-28                       &  0.9644653                         & 0.3348139                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)26 & -3.794291e-41                      & 3.946067e-41                       & -0.9615374                         & 0.3362833                         \\\\\n",
       "\t genderMale:poly(age, K, raw = T)28 &  2.404838e-45                      & 2.507426e-45                       &  0.9590863                         & 0.3375165                         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| term | estimate | std.error | statistic | p.value |\n",
       "|---|---|---|---|---|\n",
       "| (Intercept)                        | -1.303723e+05                      | 5.801182e+05                       | -0.2247340                         | 0.8221865                          |\n",
       "| genderMale                         | -1.678235e+05                      | 2.093948e+05                       | -0.8014689                         | 0.4228612                          |\n",
       "| poly(age, K, raw = T)1             |  5.969850e+04                      | 2.748227e+05                       |  0.2172255                         | 0.8280329                          |\n",
       "| poly(age, K, raw = T)2             | -1.268910e+04                      | 5.980100e+04                       | -0.2121888                         | 0.8319600                          |\n",
       "| poly(age, K, raw = T)3             |  1.653247e+03                      | 7.903331e+03                       |  0.2091836                         | 0.8343051                          |\n",
       "| poly(age, K, raw = T)4             | -1.465994e+02                      | 7.059029e+02                       | -0.2076764                         | 0.8354819                          |\n",
       "| poly(age, K, raw = T)5             |  9.268892e+00                      | 4.476673e+01                       |  0.2070487                         | 0.8359721                          |\n",
       "| poly(age, K, raw = T)6             | -4.242073e-01                      | 2.053246e+00                       | -0.2066032                         | 0.8363200                          |\n",
       "| poly(age, K, raw = T)7             |  1.388301e-02                      | 6.753549e-02                       |  0.2055661                         | 0.8371301                          |\n",
       "| poly(age, K, raw = T)8             | -3.075867e-04                      | 1.514638e-03                       | -0.2030760                         | 0.8390759                          |\n",
       "| poly(age, K, raw = T)9             |  3.831871e-06                      | 1.933803e-05                       |  0.1981520                         | 0.8429263                          |\n",
       "| poly(age, K, raw = T)11            | -8.257038e-10                      | 4.691756e-09                       | -0.1759903                         | 0.8603017                          |\n",
       "| poly(age, K, raw = T)12            |  1.047858e-11                      | 6.751863e-11                       |  0.1551954                         | 0.8766675                          |\n",
       "| poly(age, K, raw = T)14            | -5.664393e-16                      | 7.196874e-15                       | -0.0787063                         | 0.9372663                          |\n",
       "| poly(age, K, raw = T)17            | -1.171520e-20                      | 6.436421e-20                       | -0.1820141                         | 0.8555718                          |\n",
       "| poly(age, K, raw = T)18            |  5.372602e-22                      | 1.973349e-21                       |  0.2722580                         | 0.7854239                          |\n",
       "| poly(age, K, raw = T)19            | -8.045331e-24                      | 2.490759e-23                       | -0.3230073                         | 0.7466901                          |\n",
       "| poly(age, K, raw = T)21            |  1.313111e-27                      | 3.930058e-27                       |  0.3341200                         | 0.7382894                          |\n",
       "| poly(age, K, raw = T)22            | -1.217826e-29                      | 3.797813e-29                       | -0.3206650                         | 0.7484646                          |\n",
       "| poly(age, K, raw = T)25            |  9.998679e-36                      | 3.778098e-35                       |  0.2646485                         | 0.7912805                          |\n",
       "| poly(age, K, raw = T)26            | -8.864173e-38                      | 3.621607e-37                       | -0.2447580                         | 0.8066440                          |\n",
       "| poly(age, K, raw = T)28            |  2.094231e-42                      | 1.021774e-41                       |  0.2049603                         | 0.8376034                          |\n",
       "| poly(age, K, raw = T)30            | -3.203491e-47                      | 1.934642e-46                       | -0.1655857                         | 0.8684832                          |\n",
       "| genderMale:poly(age, K, raw = T)1  |  7.457309e+04                      | 9.074985e+04                       |  0.8217434                         | 0.4112240                          |\n",
       "| genderMale:poly(age, K, raw = T)2  | -1.508358e+04                      | 1.794170e+04                       | -0.8406994                         | 0.4005174                          |\n",
       "| genderMale:poly(age, K, raw = T)3  |  1.836561e+03                      | 2.139989e+03                       |  0.8582104                         | 0.3907774                          |\n",
       "| genderMale:poly(age, K, raw = T)4  | -1.498901e+02                      | 1.714620e+02                       | -0.8741885                         | 0.3820167                          |\n",
       "| genderMale:poly(age, K, raw = T)5  |  8.629959e+00                      | 9.711988e+00                       |  0.8885883                         | 0.3742255                          |\n",
       "| genderMale:poly(age, K, raw = T)6  | -3.584726e-01                      | 3.976813e-01                       | -0.9014068                         | 0.3673732                          |\n",
       "| genderMale:poly(age, K, raw = T)7  |  1.075606e-02                      | 1.178513e-02                       |  0.9126800                         | 0.3614120                          |\n",
       "| genderMale:poly(age, K, raw = T)8  | -2.278584e-04                      | 2.470073e-04                       | -0.9224766                         | 0.3562812                          |\n",
       "| genderMale:poly(age, K, raw = T)9  |  3.187705e-06                      | 3.424360e-06                       |  0.9308907                         | 0.3519113                          |\n",
       "| genderMale:poly(age, K, raw = T)10 | -2.398106e-08                      | 2.556525e-08                       | -0.9380335                         | 0.3482284                          |\n",
       "| genderMale:poly(age, K, raw = T)12 |  1.235003e-12                      | 1.301384e-12                       |  0.9489920                         | 0.3426259                          |\n",
       "| genderMale:poly(age, K, raw = T)15 | -2.947183e-18                      | 3.073478e-18                       | -0.9589081                         | 0.3376062                          |\n",
       "| genderMale:poly(age, K, raw = T)16 |  3.345744e-20                      | 3.481876e-20                       |  0.9609027                         | 0.3366023                          |\n",
       "| genderMale:poly(age, K, raw = T)19 | -2.876932e-26                      | 2.984011e-26                       | -0.9641157                         | 0.3349891                          |\n",
       "| genderMale:poly(age, K, raw = T)20 |  2.093682e-28                      | 2.170821e-28                       |  0.9644653                         | 0.3348139                          |\n",
       "| genderMale:poly(age, K, raw = T)26 | -3.794291e-41                      | 3.946067e-41                       | -0.9615374                         | 0.3362833                          |\n",
       "| genderMale:poly(age, K, raw = T)28 |  2.404838e-45                      | 2.507426e-45                       |  0.9590863                         | 0.3375165                          |\n",
       "\n"
      ],
      "text/plain": [
       "   term                               estimate      std.error    statistic \n",
       "1  (Intercept)                        -1.303723e+05 5.801182e+05 -0.2247340\n",
       "2  genderMale                         -1.678235e+05 2.093948e+05 -0.8014689\n",
       "3  poly(age, K, raw = T)1              5.969850e+04 2.748227e+05  0.2172255\n",
       "4  poly(age, K, raw = T)2             -1.268910e+04 5.980100e+04 -0.2121888\n",
       "5  poly(age, K, raw = T)3              1.653247e+03 7.903331e+03  0.2091836\n",
       "6  poly(age, K, raw = T)4             -1.465994e+02 7.059029e+02 -0.2076764\n",
       "7  poly(age, K, raw = T)5              9.268892e+00 4.476673e+01  0.2070487\n",
       "8  poly(age, K, raw = T)6             -4.242073e-01 2.053246e+00 -0.2066032\n",
       "9  poly(age, K, raw = T)7              1.388301e-02 6.753549e-02  0.2055661\n",
       "10 poly(age, K, raw = T)8             -3.075867e-04 1.514638e-03 -0.2030760\n",
       "11 poly(age, K, raw = T)9              3.831871e-06 1.933803e-05  0.1981520\n",
       "12 poly(age, K, raw = T)11            -8.257038e-10 4.691756e-09 -0.1759903\n",
       "13 poly(age, K, raw = T)12             1.047858e-11 6.751863e-11  0.1551954\n",
       "14 poly(age, K, raw = T)14            -5.664393e-16 7.196874e-15 -0.0787063\n",
       "15 poly(age, K, raw = T)17            -1.171520e-20 6.436421e-20 -0.1820141\n",
       "16 poly(age, K, raw = T)18             5.372602e-22 1.973349e-21  0.2722580\n",
       "17 poly(age, K, raw = T)19            -8.045331e-24 2.490759e-23 -0.3230073\n",
       "18 poly(age, K, raw = T)21             1.313111e-27 3.930058e-27  0.3341200\n",
       "19 poly(age, K, raw = T)22            -1.217826e-29 3.797813e-29 -0.3206650\n",
       "20 poly(age, K, raw = T)25             9.998679e-36 3.778098e-35  0.2646485\n",
       "21 poly(age, K, raw = T)26            -8.864173e-38 3.621607e-37 -0.2447580\n",
       "22 poly(age, K, raw = T)28             2.094231e-42 1.021774e-41  0.2049603\n",
       "23 poly(age, K, raw = T)30            -3.203491e-47 1.934642e-46 -0.1655857\n",
       "24 genderMale:poly(age, K, raw = T)1   7.457309e+04 9.074985e+04  0.8217434\n",
       "25 genderMale:poly(age, K, raw = T)2  -1.508358e+04 1.794170e+04 -0.8406994\n",
       "26 genderMale:poly(age, K, raw = T)3   1.836561e+03 2.139989e+03  0.8582104\n",
       "27 genderMale:poly(age, K, raw = T)4  -1.498901e+02 1.714620e+02 -0.8741885\n",
       "28 genderMale:poly(age, K, raw = T)5   8.629959e+00 9.711988e+00  0.8885883\n",
       "29 genderMale:poly(age, K, raw = T)6  -3.584726e-01 3.976813e-01 -0.9014068\n",
       "30 genderMale:poly(age, K, raw = T)7   1.075606e-02 1.178513e-02  0.9126800\n",
       "31 genderMale:poly(age, K, raw = T)8  -2.278584e-04 2.470073e-04 -0.9224766\n",
       "32 genderMale:poly(age, K, raw = T)9   3.187705e-06 3.424360e-06  0.9308907\n",
       "33 genderMale:poly(age, K, raw = T)10 -2.398106e-08 2.556525e-08 -0.9380335\n",
       "34 genderMale:poly(age, K, raw = T)12  1.235003e-12 1.301384e-12  0.9489920\n",
       "35 genderMale:poly(age, K, raw = T)15 -2.947183e-18 3.073478e-18 -0.9589081\n",
       "36 genderMale:poly(age, K, raw = T)16  3.345744e-20 3.481876e-20  0.9609027\n",
       "37 genderMale:poly(age, K, raw = T)19 -2.876932e-26 2.984011e-26 -0.9641157\n",
       "38 genderMale:poly(age, K, raw = T)20  2.093682e-28 2.170821e-28  0.9644653\n",
       "39 genderMale:poly(age, K, raw = T)26 -3.794291e-41 3.946067e-41 -0.9615374\n",
       "40 genderMale:poly(age, K, raw = T)28  2.404838e-45 2.507426e-45  0.9590863\n",
       "   p.value  \n",
       "1  0.8221865\n",
       "2  0.4228612\n",
       "3  0.8280329\n",
       "4  0.8319600\n",
       "5  0.8343051\n",
       "6  0.8354819\n",
       "7  0.8359721\n",
       "8  0.8363200\n",
       "9  0.8371301\n",
       "10 0.8390759\n",
       "11 0.8429263\n",
       "12 0.8603017\n",
       "13 0.8766675\n",
       "14 0.9372663\n",
       "15 0.8555718\n",
       "16 0.7854239\n",
       "17 0.7466901\n",
       "18 0.7382894\n",
       "19 0.7484646\n",
       "20 0.7912805\n",
       "21 0.8066440\n",
       "22 0.8376034\n",
       "23 0.8684832\n",
       "24 0.4112240\n",
       "25 0.4005174\n",
       "26 0.3907774\n",
       "27 0.3820167\n",
       "28 0.3742255\n",
       "29 0.3673732\n",
       "30 0.3614120\n",
       "31 0.3562812\n",
       "32 0.3519113\n",
       "33 0.3482284\n",
       "34 0.3426259\n",
       "35 0.3376062\n",
       "36 0.3366023\n",
       "37 0.3349891\n",
       "38 0.3348139\n",
       "39 0.3362833\n",
       "40 0.3375165"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model <- lm(form, model_data)\n",
    "glance(model)\n",
    "tidy(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is so flexible that there's not even a numerically stable solution for a unique coefficient for each feature, as indicated by the `NA`s above.\n",
    "\n",
    "Likewise, plotting the model shows a very complex function with patterns (e.g., oscillations in viewing activity for people in their 60s) that we don't believe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in predict.lm(model, data):\n",
      "“prediction from a rank-deficient fit may be misleading”"
     ]
    },
    {
     "data": {
      "image/png": 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eTnlON+jsvt4YyFzn7nk43D5Wkg7CKC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\nqsi5555LrTnsLkg0CDME5Hn48OFqe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s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\nY/Yn8sfUU94bShV3O2OkkmNIqx5fitUx4DOhRrBCCyiTBlCoCNKS5O9eNYX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3IY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\nlq22bcWTrJcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data <- model_data %>%\n",
    "  group_by(age, gender) %>%\n",
    "  summarize(count = n(),\n",
    "            geom_mean_daily_views = 10^(mean(log10(daily.views)))) %>%\n",
    "  add_predictions(model) %>%\n",
    "  mutate(pred = 10^pred)\n",
    "\n",
    "options(repr.plot.width=6, repr.plot.height=3)\n",
    "ggplot(plot_data, aes(x = age, y = pred, color = gender)) +\n",
    "  geom_line(aes(y = pred)) +\n",
    "  geom_point(aes(y = geom_mean_daily_views, size = count)) +\n",
    "  scale_size_area(guide = F)"
   ]
  },
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   "outputs": [],
   "source": []
  }
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