psinger/gist:5bc27d078cc07250a477

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: Matrix\n",
      "Loading required package: grid\n"
     ]
    }
   ],
   "source": [
    "library(lme4)\n",
    "options(rgl.useNULL=TRUE)\n",
    "library(LMERConvenienceFunctions)\n",
    "\n",
    "library(ggplot2)\n",
    "options(jupyter.plot_mimetypes = 'image/png')\n",
    "library(repr)\n",
    "options(repr.plot.width=6, repr.plot.height=6)\n",
    "\n",
    "library(car)\n",
    "library(MASS)\n",
    "\n",
    "library(vcd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Valence\n",
    "\n",
    "In this notebook, we document our various steps taken to study the effect of the course of a session on the valence of a comment. Note that we only do this on a sample of 1 mio. data points here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = read.csv(\"/home/psinger/Reddit-depletion/data/sample.csv\", header=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1000000"
      ],
      "text/latex": [
       "1000000"
      ],
      "text/markdown": [
       "1000000"
      ],
      "text/plain": [
       "[1] 1000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nrow(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>6.791</li>\n",
       "\t<li>6.975</li>\n",
       "\t<li>5.81</li>\n",
       "\t<li>5.82166666667</li>\n",
       "\t<li>5.545</li>\n",
       "\t<li>6.046</li>\n",
       "\t<li>5.99818181818</li>\n",
       "\t<li>6.41444444444</li>\n",
       "\t<li>5.88553846154</li>\n",
       "\t<li>5.84333333333</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 6.791\n",
       "\\item 6.975\n",
       "\\item 5.81\n",
       "\\item 5.82166666667\n",
       "\\item 5.545\n",
       "\\item 6.046\n",
       "\\item 5.99818181818\n",
       "\\item 6.41444444444\n",
       "\\item 5.88553846154\n",
       "\\item 5.84333333333\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 6.791\n",
       "2. 6.975\n",
       "3. 5.81\n",
       "4. 5.82166666667\n",
       "5. 5.545\n",
       "6. 6.046\n",
       "7. 5.99818181818\n",
       "8. 6.41444444444\n",
       "9. 5.88553846154\n",
       "10. 5.84333333333\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 6.791000 6.975000 5.810000 5.821667 5.545000 6.046000 5.998182 6.414444\n",
       " [9] 5.885538 5.843333"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data$valence[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.3"
      ],
      "text/latex": [
       "1.3"
      ],
      "text/markdown": [
       "1.3"
      ],
      "text/plain": [
       "[1] 1.3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(data$valence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data inspection\n",
    "\n",
    "Let us start to study the data by looking at the histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(data$valence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data looks quite normally distributed with a skew to the right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression model\n",
    "\n",
    "We use mixed-effects models where in the basic form (that we analyze in this notebook), we are interested in studying valence ~ 1 + session_index + session_comments + (1|author)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear mixed-effects regression\n",
    "\n",
    "We start with a linear mixed-effects regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_lmer = lmer(valence~1+session_index+session_comments+(1|author), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: valence ~ 1 + session_index + session_comments + (1 | author)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       " 2273601  2273660 -1136795  2273591   999995 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-5.8437 -0.4559  0.0336  0.5062  3.7924 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " author   (Intercept) 0.0270   0.1643  \n",
       " Residual             0.5432   0.7370  \n",
       "Number of obs: 1000000, groups:  author, 527435\n",
       "\n",
       "Fixed effects:\n",
       "                   Estimate Std. Error t value\n",
       "(Intercept)       5.8074926  0.0009238    6287\n",
       "session_index     0.0010617  0.0003327       3\n",
       "session_comments -0.0018775  0.0002101      -9\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "            (Intr) sssn_n\n",
       "session_ndx -0.182       \n",
       "sssn_cmmnts -0.149 -0.790"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(m_lmer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcp.fnc(m_lmer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we have slight problems with heteroscedasticity and that the residuals deviate from normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m_lmer2 = lmer(sqrt(valence)~1+session_index+session_comments+(1|author), data=data, REML=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed model fit by maximum likelihood  ['lmerMod']\n",
       "Formula: sqrt(valence) ~ 1 + session_index + session_comments + (1 | author)\n",
       "   Data: data\n",
       "\n",
       "      AIC       BIC    logLik  deviance  df.resid \n",
       "-781712.4 -781653.4  390861.2 -781722.4    999995 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-7.5425 -0.4105  0.0652  0.5101  3.3944 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance Std.Dev.\n",
       " author   (Intercept) 0.00117  0.03421 \n",
       " Residual             0.02568  0.16025 \n",
       "Number of obs: 1000000, groups:  author, 527435\n",
       "\n",
       "Fixed effects:\n",
       "                   Estimate Std. Error t value\n",
       "(Intercept)       2.404e+00  1.999e-04   12027\n",
       "session_index     2.059e-04  7.229e-05       3\n",
       "session_comments -3.457e-04  4.551e-05      -8\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "            (Intr) sssn_n\n",
       "session_ndx -0.183       \n",
       "sssn_cmmnts -0.147 -0.792"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(m_lmer2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcp.fnc(m_lmer2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let us focus on generalized linear models for potentionally better fitting the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gamma GLMER\n",
    "\n",
    "As we have continuous data, we work with the Gamma regression, as it is usually suited for positive and right skewed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.151956 (tol = 0.001, component 1)Warning message:\n",
      "In checkConv(attr(opt, \"derivs\"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue\n",
      " - Rescale variables?"
     ]
    }
   ],
   "source": [
    "m_gamma = glmer(valence~1+session_index+session_comments+(1|author),data=data,family=Gamma())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Generalized linear mixed model fit by maximum likelihood (Laplace\n",
       "  Approximation) [glmerMod]\n",
       " Family: Gamma  ( inverse )\n",
       "Formula: valence ~ 1 + session_index + session_comments + (1 | author)\n",
       "   Data: data\n",
       "\n",
       "     AIC      BIC   logLik deviance df.resid \n",
       " 2310763  2310822 -1155376  2310753   999995 \n",
       "\n",
       "Scaled residuals: \n",
       "    Min      1Q  Median      3Q     Max \n",
       "-6.2598 -0.3998  0.0614  0.5051  4.8588 \n",
       "\n",
       "Random effects:\n",
       " Groups   Name        Variance  Std.Dev.\n",
       " author   (Intercept) 0.0001515 0.01231 \n",
       " Residual             0.0141857 0.11910 \n",
       "Number of obs: 1000000, groups:  author, 527435\n",
       "\n",
       "Fixed effects:\n",
       "                   Estimate Std. Error t value Pr(>|z|)    \n",
       "(Intercept)       1.733e-01  3.944e-05    4395  < 2e-16 ***\n",
       "session_index    -2.901e-05  9.671e-06      -3  0.00271 ** \n",
       "session_comments  3.896e-05  6.908e-06       6 1.69e-08 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "Correlation of Fixed Effects:\n",
       "            (Intr) sssn_n\n",
       "session_ndx -0.120       \n",
       "sssn_cmmnts -0.151 -0.705\n",
       "convergence code: 0\n",
       "Model failed to converge with max|grad| = 0.151956 (tol = 0.001, component 1)\n",
       "Model is nearly unidentifiable: very large eigenvalue\n",
       " - Rescale variables?\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(m_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcp.fnc(m_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(MASS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "iteration 1\n",
      "iteration 2\n"
     ]
    }
   ],
   "source": [
    "m_gamma_pql = glmmPQL(valence~1+session_index+session_comments, random = list(~1 | author),data=data,family=Gamma())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear mixed-effects model fit by maximum likelihood\n",
       " Data: data \n",
       "  AIC BIC logLik\n",
       "   NA  NA     NA\n",
       "\n",
       "Random effects:\n",
       " Formula: ~1 | author\n",
       "        (Intercept)  Residual\n",
       "StdDev: 0.004664531 0.1278371\n",
       "\n",
       "Variance function:\n",
       " Structure: fixed weights\n",
       " Formula: ~invwt \n",
       "Fixed effects: valence ~ 1 + session_index + session_comments \n",
       "                       Value    Std.Error     DF  t-value p-value\n",
       "(Intercept)       0.17220977 2.748924e-05 527434 6264.624  0.0000\n",
       "session_index    -0.00003295 1.012006e-05 472563   -3.256  0.0011\n",
       "session_comments  0.00005762 6.399975e-06 472563    9.003  0.0000\n",
       " Correlation: \n",
       "                 (Intr) sssn_n\n",
       "session_index    -0.179       \n",
       "session_comments -0.148 -0.798\n",
       "\n",
       "Standardized Within-Group Residuals:\n",
       "        Min          Q1         Med          Q3         Max \n",
       "-3.82961882 -0.50377825 -0.03374882  0.45597063  6.03209001 \n",
       "\n",
       "Number of Observations: 1000000\n",
       "Number of Groups: 527435 "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(m_gamma_pql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.2.2"
  }
 },
 "nbformat": 4,
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}