{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  BAD DAY 2: Generalized linear models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#for more examples see: http://plantecology.syr.edu/fridley/bio793/glm.html\n",
    "\n",
    "setwd(getwd())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalized linear models (GLMs) extend the linear modeling capability of R to scenarios that involve non-normal error distributions or heteroscedasticity.  \n",
    "\n",
    "All other classic assumptions (particularly independent observations) still apply.  The idea here is that linear functions of the predictor variables are obtained by a link function.  The data are then fit in this transformed scale (using an iterative routine based on least squares), but the expected variance is calculated on the original scale of the predictor variables.  \n",
    "\n",
    "Simple examples of link functions are $log(y)$ [which linearizes $exp(x)$], $sqrt(y) [x^2]$, and $1/y$.  More particularly, GLMs work for the so-called 'exponential' family of error models: Poisson, binomial, Gamma, and normal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count (or count-like) response variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>plotID</th><th scope=col>date</th><th scope=col>plotsize</th><th scope=col>spcode</th><th scope=col>species</th><th scope=col>cover</th><th scope=col>utme</th><th scope=col>utmn</th><th scope=col>elev</th><th scope=col>tci</th><th scope=col>streamdist</th><th scope=col>disturb</th><th scope=col>beers</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>ATBN-01-0403 </td><td>08-28-2001   </td><td> 1000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>1            </td><td>275736       </td><td>3942439      </td><td>1660         </td><td>5.701460     </td><td>490.9        </td><td>CORPLOG      </td><td>0.22442864   </td></tr>\n",
       "\t<tr><td>ATBN-01-0532 </td><td>07-24-2002   </td><td> 1000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>8            </td><td>302847       </td><td>3942772      </td><td>1712         </td><td>3.823586     </td><td>454.0        </td><td>VIRGIN       </td><td>0.83408785   </td></tr>\n",
       "\t<tr><td>ATBN-01-0533 </td><td>07-24-2002   </td><td> 1000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>3            </td><td>303037       </td><td>3943039      </td><td>1722         </td><td>3.893762     </td><td>453.4        </td><td>LT-SEL       </td><td>1.33325863   </td></tr>\n",
       "\t<tr><td>ATBN-01-0536 </td><td>07-25-2002   </td><td> 1000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>3            </td><td>273927       </td><td>3935488      </td><td>1754         </td><td>3.145527     </td><td>492.5        </td><td>SETTLE       </td><td>1.47124839   </td></tr>\n",
       "\t<tr><td>ATBP-01-0001 </td><td>05-11-1999   </td><td>10000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>8            </td><td>273857       </td><td>3937870      </td><td>1945         </td><td>5.682065     </td><td>492.4        </td><td>VIRGIN       </td><td>1.64377141   </td></tr>\n",
       "\t<tr><td>ATBP-01-0005 </td><td>08-25-1999   </td><td>10000        </td><td>ABIEFRA      </td><td>Abies fraseri</td><td>4            </td><td>273876       </td><td>3935462      </td><td>1751         </td><td>5.417182     </td><td>545.9        </td><td>SETTLE       </td><td>0.00032873   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllll}\n",
       " plotID & date & plotsize & spcode & species & cover & utme & utmn & elev & tci & streamdist & disturb & beers\\\\\n",
       "\\hline\n",
       "\t ATBN-01-0403  & 08-28-2001    &  1000         & ABIEFRA       & Abies fraseri & 1             & 275736        & 3942439       & 1660          & 5.701460      & 490.9         & CORPLOG       & 0.22442864   \\\\\n",
       "\t ATBN-01-0532  & 07-24-2002    &  1000         & ABIEFRA       & Abies fraseri & 8             & 302847        & 3942772       & 1712          & 3.823586      & 454.0         & VIRGIN        & 0.83408785   \\\\\n",
       "\t ATBN-01-0533  & 07-24-2002    &  1000         & ABIEFRA       & Abies fraseri & 3             & 303037        & 3943039       & 1722          & 3.893762      & 453.4         & LT-SEL        & 1.33325863   \\\\\n",
       "\t ATBN-01-0536  & 07-25-2002    &  1000         & ABIEFRA       & Abies fraseri & 3             & 273927        & 3935488       & 1754          & 3.145527      & 492.5         & SETTLE        & 1.47124839   \\\\\n",
       "\t ATBP-01-0001  & 05-11-1999    & 10000         & ABIEFRA       & Abies fraseri & 8             & 273857        & 3937870       & 1945          & 5.682065      & 492.4         & VIRGIN        & 1.64377141   \\\\\n",
       "\t ATBP-01-0005  & 08-25-1999    & 10000         & ABIEFRA       & Abies fraseri & 4             & 273876        & 3935462       & 1751          & 5.417182      & 545.9         & SETTLE        & 0.00032873   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "plotID | date | plotsize | spcode | species | cover | utme | utmn | elev | tci | streamdist | disturb | beers | \n",
       "|---|---|---|---|---|---|\n",
       "| ATBN-01-0403  | 08-28-2001    |  1000         | ABIEFRA       | Abies fraseri | 1             | 275736        | 3942439       | 1660          | 5.701460      | 490.9         | CORPLOG       | 0.22442864    | \n",
       "| ATBN-01-0532  | 07-24-2002    |  1000         | ABIEFRA       | Abies fraseri | 8             | 302847        | 3942772       | 1712          | 3.823586      | 454.0         | VIRGIN        | 0.83408785    | \n",
       "| ATBN-01-0533  | 07-24-2002    |  1000         | ABIEFRA       | Abies fraseri | 3             | 303037        | 3943039       | 1722          | 3.893762      | 453.4         | LT-SEL        | 1.33325863    | \n",
       "| ATBN-01-0536  | 07-25-2002    |  1000         | ABIEFRA       | Abies fraseri | 3             | 273927        | 3935488       | 1754          | 3.145527      | 492.5         | SETTLE        | 1.47124839    | \n",
       "| ATBP-01-0001  | 05-11-1999    | 10000         | ABIEFRA       | Abies fraseri | 8             | 273857        | 3937870       | 1945          | 5.682065      | 492.4         | VIRGIN        | 1.64377141    | \n",
       "| ATBP-01-0005  | 08-25-1999    | 10000         | ABIEFRA       | Abies fraseri | 4             | 273876        | 3935462       | 1751          | 5.417182      | 545.9         | SETTLE        | 0.00032873    | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  plotID       date       plotsize spcode  species       cover utme   utmn   \n",
       "1 ATBN-01-0403 08-28-2001  1000    ABIEFRA Abies fraseri 1     275736 3942439\n",
       "2 ATBN-01-0532 07-24-2002  1000    ABIEFRA Abies fraseri 8     302847 3942772\n",
       "3 ATBN-01-0533 07-24-2002  1000    ABIEFRA Abies fraseri 3     303037 3943039\n",
       "4 ATBN-01-0536 07-25-2002  1000    ABIEFRA Abies fraseri 3     273927 3935488\n",
       "5 ATBP-01-0001 05-11-1999 10000    ABIEFRA Abies fraseri 8     273857 3937870\n",
       "6 ATBP-01-0005 08-25-1999 10000    ABIEFRA Abies fraseri 4     273876 3935462\n",
       "  elev tci      streamdist disturb beers     \n",
       "1 1660 5.701460 490.9      CORPLOG 0.22442864\n",
       "2 1712 3.823586 454.0      VIRGIN  0.83408785\n",
       "3 1722 3.893762 453.4      LT-SEL  1.33325863\n",
       "4 1754 3.145527 492.5      SETTLE  1.47124839\n",
       "5 1945 5.682065 492.4      VIRGIN  1.64377141\n",
       "6 1751 5.417182 545.9      SETTLE  0.00032873"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>8971</li>\n",
       "\t<li>13</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 8971\n",
       "\\item 13\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 8971\n",
       "2. 13\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 8971   13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dat = read.csv('./Data/treedata.csv') #choose the treedata.csv dataset\n",
    "head(dat)\n",
    "dim(dat)\n",
    "dat2 = subset(dat,dat$species==\"Tsuga canadensis\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4.65951742627346"
      ],
      "text/latex": [
       "4.65951742627346"
      ],
      "text/markdown": [
       "4.65951742627346"
      ],
      "text/plain": [
       "[1] 4.659517"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(dat2$cover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4.471835471508"
      ],
      "text/latex": [
       "4.471835471508"
      ],
      "text/markdown": [
       "4.471835471508"
      ],
      "text/plain": [
       "[1] 4.471835"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var(dat2$cover)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  1   2   3   4   5   6   7   8   9  10 \n",
       " 39  71 166 110  92  80 108  60  19   1 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(dat2$cover)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If these counts were distributed exactly from a Poisson process, what would they look like, assuming the same mean (and variance)?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b1yG+B75VrS6oGdlEDplJil/2Qn1t43L7H2uIGfU0YAAQQQQCDKAgRIUT77jB0B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DZHh8xRU3Nq6XDlGZ1oflj1U8n2+5CznHmNZJbv6ATz+jg7cwbjCCDg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\nCCCAAAIIIIAAAggggMAeCfx/NfP9WW+ZJmwAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar1 = barplot(as.vector(table(dat2$cover)),names.arg=seq(1:10), col = 'lightblue2',\n",
    "              border = 'white')\n",
    "\n",
    "points(bar1,dpois(seq(1,10),4.66)*sum(table(dat2$cover)),\n",
    "\n",
    "     cex=1,type=\"b\", col = 'mediumpurple', lwd = 3, pch = 19)\n",
    "#?dpois\n",
    "\n",
    "box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#?glm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the data look Poisson-ish but they're not perfect.  (One reason you've probably already guessed: our data are bounded at 10, so variance should actually go down for the highest values.)\n",
    "\n",
    "Now let's fit a GLM to these data with just an intercept (overall mean):\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cover ~ 1, family = poisson, data = dat2)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.0594  -0.8229  -0.3132   1.0085   2.1430  \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)  1.53891    0.01696   90.73   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 749.25  on 745  degrees of freedom\n",
       "Residual deviance: 749.25  on 745  degrees of freedom\n",
       "AIC: 3212.2\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm1 = glm(cover~1,data=dat2,family=poisson)\n",
    "\n",
    "summary(glm1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a continuous predictor variable like elevation to generate a simple Poisson regression.  First we'll graph it:\n",
    "\n",
    "\n",
    "And then we'll fit the new glm and test it against a model with only an intercept:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3U7V126iV3XPZvv70il9E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\nnO5u9fa7IJFM7q466Xg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pvjFkqnY1UX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RvIkc0PLTm+7nVU5gxp1tLI5CeMHhIyQI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\nG33hnddV7JfaN61F3XsD8vA0xMNYp+5HZ3qhnUnf2hRtXot++mmW0lFb2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S+TI8j+hD/RgkyrJ97/c9TlEytNbLy4j2WT6aMiHS3nYjL0\nMeS1Yvk4+c1lX6AjTR9FeidUSqdYPr2JzISdsY8anR66v1iYavxgtO0L4/CfiFvROPLlIz9w\n6sqR1ObTOjrW79ZsIrjtUa0OwsKXUeuvowGEIE00l9LOCYfbWxMTm/cvHSr0Gd3T0X8lbms9\nqVJZIv0sFHwcVwuOkqsFXfENJ+E2p2ugddn+VTTy4r5jOLn5q0qtbenp2O1xeHZUynPZ3MgC\nBfqCtDGfbNEat+mWNjDLytkJJ10e5p22zt2iTfRXtXCALOkTf4V+4CX4BZLqXVfrhv2sJkvG\nzYp9a34qbn70CO5dP2pycvMj+edq28eVo7i5BZl6WhBGTt+izI1YFHsBFsVkQY1ucQisKANp\nwQrg4jBaVlL2gDZySTqBbRzbddhOwFbMHQlPCXdesZP0W4EEItEvoZOX1cmKgzfCNKMDXtcT\n2/jipSWxKYZbzr67Eo0j4SgDGm4taUE5dGO34uQ7CHspK4Trw1yoHfuByhbpn90Z3/TCIPIl\nDK6mvBb1Aavp5Y0jCSv5wqRxDyse+4gc5zkLxtEFmK84t0nm+S/aLuoNVuz1BzCxd27L6o1t\n3ITjf6000RUFJC4MqdWRWOv/BTWOXMW1BePoEvB5fpB0imXWqe9om5FIBJPlxXMwjn4MaYGN\nI0kZ5SdurSxUNJnIJ+HTg+NFqatBc+ZogH+Vwks41OV9cSvk/1QKG/Q8nqVaB5knBylLJA8u\nutfldHQTjKMfIJ2KeosuEhd15vPyjGV3vPf7NRtHjlDnroC+Fq1wxbBxjKM5HvpzMI4uleNa\nHNhKn3gSrkTVvMDntEmj2yCzYt+ar7Nbvqaz1VJym/iiuZ64/V7UmwMgP2A9c670Pa+no/Wc\nRVOCglYcgZVuIMVRYnJ7Bi6vO6tXD+H3Bdh+je3j2Oh2YQKyCob+9ExsMnAEdrjN4b2BAy9C\nwJ4OtRGTU9xas3KdTIZlW8wcymAJmTAOgjoDW8UEXhzB0wP/HUq+c/um9WZ5psLXCMb2ydg/\nGIqGmmj48YP/miwmrHKbnZhi7wurN8JvgKEUeKUV4fF8ttkjVDpFMgMuUdSK0zpbNxxY5HQV\nXn2rEekop2aEjo3JtVEfwjT+34IYCqHFL3IEPA/z7sUSiXL8b8gKXkfdW5Vf1xU7dD3iPUXq\nQ1BdUM9MqxV5C2a6fdWVU8mUdl9keSUTWtwToIe7OhdJd1kYkLKs2jltEW0SWoUaN/0EkT7i\n6bPk+SXfr7bfo5swJr8DcoPXTyTohX9fbWkz9komELjTalAI/wW998H2EWx7Y5MVVHkg93Zs\n0kl8ARvdLkrAaorI7VehbqWUgR4Tv+fL7R9LhQ1PkZyG1bEQE/2l0mxlpeMOmLiFCG/qq5Qz\nvKkK/Yk+qFK4heeN1d1u1sz6W/qlMFkCGx6z8ULuyKQEs6wzZGUeE9BDQ0ZHcIOLmMWfzysl\nC2nKxK52hzZqRa2X1C4IV/3aW95arRzJDzJ0FOKXu027WvGLGs9V1XTiJV7YanPyxjTIe4Yr\nM4wsjUpjnS1XbcLEknqGm243Ir2K7TCoXOm3g4ZdbuGgOwyAxWlLIgfucHkpUbX5tKLos0KO\nm4VpoUJkWlXLiwv9qznG2+qOwy1zbdXEhR7743nEA6qJyzgrn8CidUDLFNXx0OtxbB/DhmcA\nHCevX/1XbHhAWb0D2yPYPottMd1uEIa3pAReYWEDXUz6AWUZ294fK0/oI8M5DDDRztbd9hyb\nVveGi1ldaEwvDmrkAb66XO+cWDIZ6Wnb6ymJKfVQOQ1M1N4f8z7nfp1y4QrPobJlcAVnf98f\nL3M+CFO3qlZifRlBf3Eb4x6tRs2mHTTeMgiH1wWbxdHbUkfUkh+ZXeK2Lxi07psFa5FV77ii\na1fr7s8enXZuK686uYgd2S/EtaPZdABK3rh4INQIPc8A5afgeZXQ7Ws28SI7wqOI9y7p1WpH\n908p9Vg1mccK4X4wYmtkaWx0n/tXk/6COK4+ssgU2giWZ5F0NCd6LMlYvkB3eixrAqE7rmWd\nm4XK7QWv32DzjSM/hLzd5zTv3Kfxez+2xfyGhKQ3jC3oxMdf/cggDt1SEdCWjaQwhod3+GaL\nxF0qt5RpLVWelm0607py2epsxK5m0uhmWl597jq86zgXelT3I4f/RV2H3g3pnLZas+YAIK9T\n3mUcLpwUjn1V5D1adZ2BTVJ13CoUZZSABDL4IFPAoEWC4UbTRXComzXoMKeA3JI5dxR2T65u\nzvXHYWMz/MomsNINJDF8TsbWiq3wVip5YcPLsN2I7QfYHsaGRZVFcfKWKud+/4DSjkO40wOG\nZbBFIoCxe7s8kBFWHFadZkanM3LlcYmcuQtrqYdj/XMJ59JLlLXllgzeeDU5mau4sjqt1b3y\nhHJY9TGSN+Pjltv9eHio5W5ciZJv5rT4fnX83TETsbYvRUKLnAd8/meOWU2ybft2fK+qahGy\n4oyrR/Jdl2Xv5ArMyPT4LbUqaibT95l4q/O9pjCycMUyirnnPVjYlw8Yh7xFWD+IOIeESa9S\n2MW+IlUpveV6XjiYiH1vtfphyNxei0ki6aIfxLf6FqdN4yr8dkjDXDZ8q5Srivhg82x/XC0T\nxluZBKofKRqDxy+gptyD/QlsexZRWYyiU7BNYLsS26nY6HYRAtO53NXoqEMN3Bhasliw+mXY\n7+XUghRf/cNbt5xv9dQiZpeLK/OAMJl2ytaonwd57au8ohbS/xY2Dax2ZhKpHb/y9bJN7qdh\n66AfN9Sv8w0Sc2kyOfA49L4trN5ISz7emg6TpqQhLkycYmHxzF8L7gG4qti5sH4jk49/LWwc\nP7ybF/sP/vFy/vW44y6Gy2Vsq8mNq20JGCt/QUnCOAzlTM7W30PfFW7m6tbVzUhv0e6oAI+6\nP+cXikyIwOAw45VniFjFg0oZog+6paaPtWbtK9Emg94dU1wRLDJkU5M/K3EylHdiKv0H2Eay\n4B3KeUzvSkxtfTBURAbeZQisdANJXg97BzZ51kgawTpshe4f8HgxNun8P+adDNehe5H401gE\nJie37sAK/hA6ypkQmkfwDcuPhwhfc9DhVGIrBrVEFROUmtNeKgGSt3rkD2UbZpIVwXNpspgS\n0NkfRcDgEy9M/Gxj49nE62evZo8mt/wKnc2t0HMRboUqrTYmNJFcVn/JDRFeb+j4bcgI3C86\n+cG3eELxKaI+6oSU30WJqcGHipyuwsv5eOqN0K86w83oD2HC//WwxmIVitYcBRn8SM1CPAE5\nlZOxMTgz15j++thU+hJUmnuBO3g7QZvCh2a/gSH5gqrLaWHG8RibemAR5S1MoU4+eJYL7AwW\nZKqss/P0EhlOWc7zDXPgtcWLoE6YcXMuCdcA/v6E+pE8hrAIblsOfdOnoE+YOibp2nDSh9OR\nQFECK91AkonIsdi+jE0G61INWm5DOAbb1djodiECaaPf5RoflSeoMiBgdPnGyMSW3y4toqvS\nGCM3Ic3gE5SlVbCm1DBky5fNkxAiV2XCGDQl04UclJX+Fsp2BOVW0fiQ8LhF40sY/H9fUmjB\niURy8GKk8dMgk2Wv7tw7krIWGNdYmTkbk8iMw6EgjcU4dPJv9HvGpgfuFXnQ+4f4+YnoVEm+\ny0XdP5xMn4dJyNtxXHESIvkA9yyecni5pBOETzE9XDkmkTZpWeBaNIePQ74C+oValQcrcZsT\nqc3XmGT6Q3jA/GHotyh1NWjGXBWQagUn4dBV3DHifGy1QuCAp0cnhnAVW/0wSFkKF+GjkvaH\nnKuxObsfbSvn6lU+QcS18RKMt8gVDtTTtyD0cJB45aQivtTzb9sp+4X4DaRHOXlLec7lod5s\n2zk8M62xiCRlW53zOPxoODlU8zeVpE2izwrUt+Zr69QNZZ7IJFPvzfevdX9kIiEvxfprUD4O\nV61/NpIaHKw1bcZfuQRWuoEkJScTr7djOwDbZdhKuX/ixFpsz8V2SalA9F9ZBOSWoxmdPQmD\nz45Sg790ptjkFqOBkeRDUpeW3A2nhq5FoutlkPMGuiXXIejgE0Yx4YqBdjiXs1+Yy6rn44re\n/aXLQTRAjDLOLyut7O+NJAf+HUUnZftYaZle2Srz/URy+t1lRBc7ZUaSoxsg+xqolS2lG/xl\ngnaXzk2frNTAguccoeftWKFf6xiJ7sp7sbQW+JVKzw/osZAr4/+bSA3IBGLWwUjYCOPuqsp6\nm//f3r3AOVLVaR+vSvoyPd3DzPSAIAPKCihydxEQUMEL7iusIMi4zgXBVdBV131dVtb1/qK8\nor7q7rquvu+qi84FcHYBQRS8cXEXFESEAeR+l4vQ6W46fU9S7/NPqnqq00knpzvp6SS/M58z\nqVSdOnXO91SlzklVqh/QfVJvUEc33Z/e/E1dQ/pYId8yg06VX7djpVWfEwaGN92eSg9u0MHz\nk9m2M1Wo2ITSayAWPKlx4+uGh7c+HVtUg8mtz2ZyY8dokDRe2dBS2D4XXN4/PPEe23i/t3Uw\nN5l7vb7Z128YS992WFjL1ps9FPKePY0tDdM9q8myHfzYNh9IpVOHVc7VLUUq/fiZGiT/UNsp\nu6+bh7kEmfHXpbzN+due+ka33KwKnCzv0fJe+S8I9FmQ+599w5u+F5Ysm0pnD9T0nAZJ4f6f\n1R+82ZJKP/HBQW/LQ7lMTneL+GXLX649wn2+ZHtG7pbGTbR8auVlX5hMeQyMXHybnvt2stYo\n2/7lcgvLZ18SXaXj/p3l0rnMLxyTk/ps9Z4u16Yz8rNjxQ8ez05OHle7q0fRVn48PuGN251A\nuho/ezsUlgc/Tw3l/z5myTaNcuW1tQVaYYAUb+FqPsBu0Qrb4isx3dwC+rbyHn3beZC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1z56fUTk/oqjqee1RPaxeWjen8n+2b2jjBZrW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\nSm+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IAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII\nIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC\nCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCw2AX+PzJR\nfDkHgY6PAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Hemlock cover vs. elevation”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with(dat2,plot(elev,cover,main=\"Hemlock cover vs. elevation\",      \n",
    "               cex = 1.5, col = 'midnightblue', pch = 19))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the chi-squared test is typically recommended for models with 'known deviance' (Poisson and binomial).  Here the model with elevation adds no explanatory power (fairly obvious from the graph), but we can still add the predicted trend line to our graph:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cover ~ disturb, family = poisson, data = dat2)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.1794  -0.7763  -0.1980   0.8523   2.2006  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)    1.48367    0.03838  38.661   <2e-16 ***\n",
       "disturbLT-SEL  0.03204    0.04685   0.684    0.494    \n",
       "disturbSETTLE  0.08957    0.05485   1.633    0.103    \n",
       "disturbVIRGIN  0.12184    0.05277   2.309    0.021 *  \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 749.25  on 745  degrees of freedom\n",
       "Residual deviance: 742.32  on 742  degrees of freedom\n",
       "AIC: 3211.3\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Pr(&gt;Chi)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>745       </td><td>749.2497  </td><td>NA        </td><td>      NA  </td><td>        NA</td></tr>\n",
       "\t<tr><td>742       </td><td>742.3237  </td><td> 3        </td><td>6.926019  </td><td>0.07429355</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Resid. Df & Resid. Dev & Df & Deviance & Pr(>Chi)\\\\\n",
       "\\hline\n",
       "\t 745        & 749.2497   & NA         &       NA   &         NA\\\\\n",
       "\t 742        & 742.3237   &  3         & 6.926019   & 0.07429355\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Resid. Df | Resid. Dev | Df | Deviance | Pr(>Chi) | \n",
       "|---|---|\n",
       "| 745        | 749.2497   | NA         |       NA   |         NA | \n",
       "| 742        | 742.3237   |  3         | 6.926019   | 0.07429355 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Resid. Df Resid. Dev Df Deviance Pr(>Chi)  \n",
       "1 745       749.2497   NA       NA         NA\n",
       "2 742       742.3237    3 6.926019 0.07429355"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = seq(0,1660)\n",
    "#plot.new()\n",
    "#lines(predict(glm2,list(elev=x),type=\"response\"),lwd=2,col=\"orange\")\n",
    "\n",
    "#What is crucial here is the type argument to predict: \"response\" re-calculates the coefficients to be on the same scale as the original response variable, rather than the scale of the link function.  Let's now try an ANOVA with Poisson error, using disturbance as our predictor:\n",
    "\n",
    "glm3 = glm(cover~disturb, data = dat2, family = poisson)\n",
    "\n",
    "summary(glm3)\n",
    "anova(glm1, glm3, test = \"Chisq\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cover ~ elev, family = poisson, data = dat2)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.0673  -0.8250  -0.3048   0.9991   2.1347  \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)  1.546e+00  5.135e-02  30.115   <2e-16 ***\n",
       "elev        -8.448e-06  5.471e-05  -0.154    0.877    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 749.25  on 745  degrees of freedom\n",
       "Residual deviance: 749.23  on 744  degrees of freedom\n",
       "AIC: 3214.2\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Resid. Df</th><th scope=col>Resid. Dev</th><th scope=col>Df</th><th scope=col>Deviance</th><th scope=col>Pr(&gt;Chi)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>745       </td><td>749.2497  </td><td>NA        </td><td>        NA</td><td>       NA </td></tr>\n",
       "\t<tr><td>744       </td><td>749.2259  </td><td> 1        </td><td>0.02384893</td><td>0.8772699 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Resid. Df & Resid. Dev & Df & Deviance & Pr(>Chi)\\\\\n",
       "\\hline\n",
       "\t 745        & 749.2497   & NA         &         NA &        NA \\\\\n",
       "\t 744        & 749.2259   &  1         & 0.02384893 & 0.8772699 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Resid. Df | Resid. Dev | Df | Deviance | Pr(>Chi) | \n",
       "|---|---|\n",
       "| 745        | 749.2497   | NA         |         NA |        NA  | \n",
       "| 744        | 749.2259   |  1         | 0.02384893 | 0.8772699  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Resid. Df Resid. Dev Df Deviance   Pr(>Chi) \n",
       "1 745       749.2497   NA         NA        NA\n",
       "2 744       749.2259    1 0.02384893 0.8772699"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm2 = glm(cover~elev, data = dat2, family = poisson)\n",
    "summary(glm2)\n",
    "anova(glm1,glm2,test=\"Chisq\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cover ~ disturb * elev, family = poisson, data = dat2)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.4042  -0.7782  -0.2072   0.8090   2.0888  \n",
       "\n",
       "Coefficients:\n",
       "                     Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept)         1.445e+00  1.396e-01  10.352   <2e-16 ***\n",
       "disturbLT-SEL       2.546e-01  1.639e-01   1.554   0.1203    \n",
       "disturbSETTLE      -3.702e-01  2.275e-01  -1.628   0.1036    \n",
       "disturbVIRGIN       9.283e-02  2.625e-01   0.354   0.7236    \n",
       "elev                3.540e-05  1.243e-04   0.285   0.7758    \n",
       "disturbLT-SEL:elev -2.788e-04  1.651e-04  -1.689   0.0912 .  \n",
       "disturbSETTLE:elev  7.319e-04  2.943e-04   2.487   0.0129 *  \n",
       "disturbVIRGIN:elev  2.278e-05  2.269e-04   0.100   0.9200    \n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for poisson family taken to be 1)\n",
       "\n",
       "    Null deviance: 749.25  on 745  degrees of freedom\n",
       "Residual deviance: 729.06  on 738  degrees of freedom\n",
       "AIC: 3206\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "glm4 = glm(cover~disturb*elev,data=dat2,family=poisson)\n",
    "\n",
    "summary(glm4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start:  AIC=3206\n",
      "cover ~ disturb * elev\n",
      "\n",
      "               Df Deviance  AIC\n",
      "<none>              729.06 3206\n",
      "- disturb:elev  3   742.06 3213\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:  glm(formula = cover ~ disturb * elev, family = poisson, data = dat2)\n",
       "\n",
       "Coefficients:\n",
       "       (Intercept)       disturbLT-SEL       disturbSETTLE       disturbVIRGIN  \n",
       "         1.445e+00           2.546e-01          -3.702e-01           9.283e-02  \n",
       "              elev  disturbLT-SEL:elev  disturbSETTLE:elev  disturbVIRGIN:elev  \n",
       "         3.540e-05          -2.788e-04           7.319e-04           2.278e-05  \n",
       "\n",
       "Degrees of Freedom: 745 Total (i.e. Null);  738 Residual\n",
       "Null Deviance:\t    749.2 \n",
       "Residual Deviance: 729.1 \tAIC: 3206"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "step(glm4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ANOVA contrasts suggest a significant difference in slope with elevation in the plots of prior settlement:\n",
    "disturbSETTLE:elev  0.0129 *; \n",
    "the step function shows that the interaction is necessary (large increase in AIC when the interaction is removed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#?step\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#st=step(glm4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#st$anova"
   ]
  }
 ],
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