{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión Logística sobre Tabla de Contingencia\n",
    "## Autor: Sergio García Prado\n",
    "## Fecha: Diciembre de 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descripción: \n",
    "La respuesta binaria a cierto estimulo está relacionada con unas variables x (numerica), t (cualitativa)  y z (cualitativa). Se han realizado 100 experimentos aleatorios en cada una de las combinaciones correspondientes a diferentes niveles de las variables anteriores. La tabla datos contiene las variables explicativas junto con la variable y (número de ocurrencias de una de las respuestas en los 100 experimentos realizados). Identifica el modelo más interesante para ajustar. Consigue intervalos de confianza para las odds ratio (Wald y verosimilitud perfil), para la dosis letal 40% (Wald) y para las predicciones en los niveles de las variables explicativas estudiados (Wald). Representa las curvas correspondientes a la predicción."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configuración de Entorno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm(list = ls())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(magrittr, warn.conflicts = FALSE)\n",
    "library(dplyr, warn.conflicts = FALSE)\n",
    "library(ggplot2, warn.conflicts = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "options(repr.plot.width = 8, repr.plot.height = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lectura de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tries <- 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos <- read.table('datos.txt', header = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(datos)[2] <- \"x\"\n",
    "datos$z <- as.factor(datos$z)\n",
    "datos$t <- as.factor(datos$t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análisis Exploratorio de los Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       y               x        z      t     \n",
       " Min.   : 0.00   Min.   : 1.0   0:50   1:20  \n",
       " 1st Qu.: 8.00   1st Qu.: 3.0   1:50   2:20  \n",
       " Median :36.00   Median : 5.5          3:20  \n",
       " Mean   :39.61   Mean   : 5.5          4:20  \n",
       " 3rd Qu.:65.50   3rd Qu.: 8.0          5:20  \n",
       " Max.   :98.00   Max.   :10.0                "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3961"
      ],
      "text/latex": [
       "3961"
      ],
      "text/markdown": [
       "3961"
      ],
      "text/plain": [
       "[1] 3961"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum(datos$y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ", , z = 0\n",
       "\n",
       "    t\n",
       "x     1  2  3  4  5\n",
       "  1   2  1  2  0  0\n",
       "  2   3  2  3  2  0\n",
       "  3   1  4  1  1  1\n",
       "  4   7  3  6  4  1\n",
       "  5   7 14  8  4  9\n",
       "  6   8  8  6  8 17\n",
       "  7  14 20 17 17 18\n",
       "  8  27 28 26 28 36\n",
       "  9  41 32 38 38 39\n",
       "  10 48 49 50 50 56\n",
       "\n",
       ", , z = 1\n",
       "\n",
       "    t\n",
       "x     1  2  3  4  5\n",
       "  1  18 17 16 22 18\n",
       "  2  25 29 37 27 22\n",
       "  3  38 41 38 36 43\n",
       "  4  54 47 54 53 47\n",
       "  5  61 64 60 56 64\n",
       "  6  70 76 71 76 78\n",
       "  7  83 81 73 82 80\n",
       "  8  87 88 90 90 87\n",
       "  9  94 98 92 98 94\n",
       "  10 98 96 94 96 97\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xtabs(y ~ x + t + z, datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0BMm83Nqq44lDnf5ZUIhvQs04Cj8MzF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\nAAQgAAEIDD0C/w+0pvin/ybo/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairs(y ~ x + t + z, datos, pch = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ajuste del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos.tunned <- datos %>% \n",
    "    rename(sucesses = y) %>%\n",
    "    mutate(failures = tries - sucesses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo Logístico Completo (full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.logit.full <- glm(cbind(sucesses, failures) ~ x + t + z, data = datos.tunned, \n",
    "                     family = binomial(link = logit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cbind(sucesses, failures) ~ x + t + z, family = binomial(link = logit), \n",
       "    data = datos.tunned)\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-2.28796  -0.60134   0.04575   0.49385   2.41219  \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -5.163053   0.117652 -43.884   <2e-16 ***\n",
       "x            0.515782   0.012217  42.218   <2e-16 ***\n",
       "t2           0.044675   0.086293   0.518    0.605    \n",
       "t3          -0.014940   0.086429  -0.173    0.863    \n",
       "t4           0.007461   0.086376   0.086    0.931    \n",
       "t5           0.078046   0.086224   0.905    0.365    \n",
       "z1           3.101047   0.067702  45.804   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 5187.820  on 99  degrees of freedom\n",
       "Residual deviance:   87.865  on 93  degrees of freedom\n",
       "AIC: 504.62\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(model.logit.full)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como vemos, en el modelo con todas las variables, la influencia de las categorías formadas por la variable cualitativa *t* no aporta información significativa para el ajuste de la variable $y$. Por lo tanto, se eliminará del modelo para así tratar de simplificar el mismo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo Logístico Restringido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.logit <- glm(cbind(sucesses, failures) ~ x + z, data = datos.tunned, \n",
    "             family = binomial(link = logit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cbind(sucesses, failures) ~ x + z, family = binomial(link = logit), \n",
       "    data = datos.tunned)\n",
       "\n",
       "Deviance Residuals: \n",
       "     Min        1Q    Median        3Q       Max  \n",
       "-2.43976  -0.67094   0.02646   0.51833   2.36379  \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -5.13896    0.10388  -49.47   <2e-16 ***\n",
       "x            0.51568    0.01221   42.22   <2e-16 ***\n",
       "z1           3.10041    0.06769   45.80   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 5187.820  on 99  degrees of freedom\n",
       "Residual deviance:   89.401  on 97  degrees of freedom\n",
       "AIC: 498.15\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(model.logit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con el modelo ajustado únicamente por las variables $X$ y $Z$ se consigue que ahora todos los parámetros sean significativos. A pesar de que en este caso la deviance residual del modelo es mayor ($89.401$ frente a $87.865$ del modelo con todas las variables predictoras), el AIC es menor. Esto es porque la reducción del número de parámetro a estimar \"compensa\" el peor ajuste del modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelo Normal Inverso (probit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para comprobar el ajuste sobre un modelo lineal generalizado con respuesta de tipo binomial diferente al logístico, se va a realizar un ajuste utilizando la función de enlace normal estándar inversa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.probit <- glm(cbind(sucesses, failures) ~ x + z, data = datos.tunned,\n",
    "                    family = binomial(link = probit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "glm(formula = cbind(sucesses, failures) ~ x + z, family = binomial(link = probit), \n",
       "    data = datos.tunned)\n",
       "\n",
       "Deviance Residuals: \n",
       "    Min       1Q   Median       3Q      Max  \n",
       "-2.0931  -0.6003  -0.1092   0.6668   2.3895  \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error z value Pr(>|z|)    \n",
       "(Intercept) -2.937276   0.053728  -54.67   <2e-16 ***\n",
       "x            0.293968   0.006519   45.09   <2e-16 ***\n",
       "z1           1.777507   0.035824   49.62   <2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
       "\n",
       "(Dispersion parameter for binomial family taken to be 1)\n",
       "\n",
       "    Null deviance: 5187.820  on 99  degrees of freedom\n",
       "Residual deviance:   92.839  on 97  degrees of freedom\n",
       "AIC: 501.59\n",
       "\n",
       "Number of Fisher Scoring iterations: 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(model.probit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, el ajuste sigue siendo similar al logístico, obteniendo una deviance residual similiar, junto con un AIC menor que el obtenido por el modelo logístico completo (por el uso de un menor número de variables). Sin embargo, en este caso el modelo no obtiene mejores resultados que el modelo logístico restringido al uso de las variables $X$ y $Z$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación se incluyen las tablas resumen que recogen los valores del Criterio de Akaike y del Criterio Bayesiano, donde ambos indican el mejor ajuste del modelo logístico restringido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>df</th><th scope=col>AIC</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>model.logit.full</th><td>7       </td><td>504.6179</td></tr>\n",
       "\t<tr><th scope=row>model.logit</th><td>3       </td><td>498.1535</td></tr>\n",
       "\t<tr><th scope=row>model.probit</th><td>3       </td><td>501.5914</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & df & AIC\\\\\n",
       "\\hline\n",
       "\tmodel.logit.full & 7        & 504.6179\\\\\n",
       "\tmodel.logit & 3        & 498.1535\\\\\n",
       "\tmodel.probit & 3        & 501.5914\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | df | AIC | \n",
       "|---|---|---|\n",
       "| model.logit.full | 7        | 504.6179 | \n",
       "| model.logit | 3        | 498.1535 | \n",
       "| model.probit | 3        | 501.5914 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                 df AIC     \n",
       "model.logit.full 7  504.6179\n",
       "model.logit      3  498.1535\n",
       "model.probit     3  501.5914"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AIC(model.logit.full, model.logit, model.probit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>df</th><th scope=col>BIC</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>model.logit.full</th><td>7       </td><td>522.8541</td></tr>\n",
       "\t<tr><th scope=row>model.logit</th><td>3       </td><td>505.9690</td></tr>\n",
       "\t<tr><th scope=row>model.probit</th><td>3       </td><td>509.4069</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & df & BIC\\\\\n",
       "\\hline\n",
       "\tmodel.logit.full & 7        & 522.8541\\\\\n",
       "\tmodel.logit & 3        & 505.9690\\\\\n",
       "\tmodel.probit & 3        & 509.4069\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | df | BIC | \n",
       "|---|---|---|\n",
       "| model.logit.full | 7        | 522.8541 | \n",
       "| model.logit | 3        | 505.9690 | \n",
       "| model.probit | 3        | 509.4069 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                 df BIC     \n",
       "model.logit.full 7  522.8541\n",
       "model.logit      3  505.9690\n",
       "model.probit     3  509.4069"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BIC(model.logit.full, model.logit, model.probit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por lo tanto, el modelo que se ha elegido es el siguiente (donde $y$ en este caso representaría la observacion sin agrupar y $logit(p) = log(\\frac{p}{1-p})$):\n",
    "\n",
    "$$E[logit(y)] = X + Z$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intervalos de Confianza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los intervalos de confianza se van a calcular utilizando un nivel de confianza $\\alpha = 0.01$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha <- 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit <- function(p) {\n",
    "    log(p / (1 - p))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv.logit <- function(x) {\n",
    "    exp(x) / (1 + exp(x))\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Odds-Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los intervalos de confianza para las odd ratios se pueden conseguir a partir de las funciones *confint* (verosimilitud perfil) y *confint.default* (Wald) de manera sencilla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verosimilitud Perfil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Waiting for profiling to be done...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-5.3450602</td><td>-4.9378172</td></tr>\n",
       "\t<tr><th scope=row>x</th><td> 0.4919734</td><td> 0.5398608</td></tr>\n",
       "\t<tr><th scope=row>z1</th><td> 2.9690507</td><td> 3.2344230</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -5.3450602 & -4.9378172\\\\\n",
       "\tx &  0.4919734 &  0.5398608\\\\\n",
       "\tz1 &  2.9690507 &  3.2344230\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % | \n",
       "|---|---|---|\n",
       "| (Intercept) | -5.3450602 | -4.9378172 | \n",
       "| x |  0.4919734 |  0.5398608 | \n",
       "| z1 |  2.9690507 |  3.2344230 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            2.5 %      97.5 %    \n",
       "(Intercept) -5.3450602 -4.9378172\n",
       "x            0.4919734  0.5398608\n",
       "z1           2.9690507  3.2344230"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confint(model.logit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wald"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>2.5 %</th><th scope=col>97.5 %</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>(Intercept)</th><td>-5.3425549</td><td>-4.9353591</td></tr>\n",
       "\t<tr><th scope=row>x</th><td> 0.4917373</td><td> 0.5396189</td></tr>\n",
       "\t<tr><th scope=row>z1</th><td> 2.9677440</td><td> 3.2330787</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       "  & 2.5 \\% & 97.5 \\%\\\\\n",
       "\\hline\n",
       "\t(Intercept) & -5.3425549 & -4.9353591\\\\\n",
       "\tx &  0.4917373 &  0.5396189\\\\\n",
       "\tz1 &  2.9677440 &  3.2330787\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 2.5 % | 97.5 % | \n",
       "|---|---|---|\n",
       "| (Intercept) | -5.3425549 | -4.9353591 | \n",
       "| x |  0.4917373 |  0.5396189 | \n",
       "| z1 |  2.9677440 |  3.2330787 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            2.5 %      97.5 %    \n",
       "(Intercept) -5.3425549 -4.9353591\n",
       "x            0.4917373  0.5396189\n",
       "z1           2.9677440  3.2330787"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confint.default(model.logit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los intervalos basados en ambos métodos son muy similares, por lo que interpretaremos de manera indistinta ambos. La interpretación del término indica que cuando la variable $X$ toma el valor $0$ y la variable categórica $Z$ está en la categoría $\\{Z = 0\\}$, la probabilidad de $y$ es prácticamente $0$. En cuanto al *oddratio* para la variable $X$, podemos decir que por cada unidad que esta aumenta, la ventaja aumenta un $50\\%$. En cuanto a la variable categórica $Z$, diremos que cuando esta se fija en la categoría $\\{Z = 1\\}$ la ventaja aumenta un $300\\%$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dosis Letal (40%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación se muestra el intervalo de confianza de wald de la variable regresora $X$ cuando la variable $Y$ alcanza la dosis letal del $40\\%$. Para ello nos podemos apoyar en la función *dose.p* del paquete *MASS*. Sin embargo, está pensada para casos sencillos en que únicamente hay término independiente y una variable continua (en este caso además tenemos una variable binaria)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "coefs <- as.vector(model.logit$coefficients)\n",
    "coefs.V <- vcov(model.logit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez tenemos los valores de los parámetros, así como su matriz de varianzas convarianzas, podemos aplicar el método delta para obtener la función de Dosis Letal del $40\\%$. En este caso se ha hecho para los dos valores que puede tomar la variable $Z$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "g <- function(x) {\n",
    "    c((logit(0.4) - x[1]) / x[2],\n",
    "      (logit(0.4) - x[1] - x[3]) / x[2])\n",
    "}\n",
    "\n",
    "G <- function(x) {\n",
    "    matrix(c(1 / x[2], - (logit(0.4) - x[1]) / x[2] ^ 2, 0,\n",
    "             1 / x[2], - (logit(0.4) - x[1] - x[3]) / x[2] ^ 2, 1 / x[3]), \n",
    "           2, 3, byrow = TRUE)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez definidas las transformaciones $g$ (función de transformación) y $G$ (matriz de derivadas), podemos calcular fácilmente los intervalos de confianza de Wald."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>8.747333</td><td>9.448086</td></tr>\n",
       "\t<tr><td>2.897935</td><td>3.335498</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 8.747333 & 9.448086\\\\\n",
       "\t 2.897935 & 3.335498\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 8.747333 | 9.448086 | \n",
       "| 2.897935 | 3.335498 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]     [,2]    \n",
       "[1,] 8.747333 9.448086\n",
       "[2,] 2.897935 3.335498"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " g(coefs) + rbind(c(-1, 1), c(-1, 1)) * qnorm(1 - alpha / 2) * (G(coefs) %*% coefs.V %*% t(G(coefs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La primera fila se refiere al intervalo de confianza para la dosis letal (al $40\\%$) de la variable $X$, cuando la variable $Z$ toma el valor $\\{Z = 0\\}$, mientras que la segunda fila se refiere a la misma situación, cuando la variable $Z$ toma el valor $\\{Z = 1\\}$. Los intervalos de confianza se han calculado con un nivel de significacia del $99\\%$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Niveles de Variables Explicativas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso se calcula el nivel de la variable respuesta $Y$ respecto de $X$ y el valor que toma la variable categórica $Z$. Además, se han incluido las bandas de confianza de nivel $99\\%$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos.support <- data.frame(x = rep(seq(1, 10), 2), z = as.factor(c(rep(0, 10), rep(1, 10))))\n",
    "\n",
    "predictions <- predict(model.logit, datos.support, type = \"response\", se.fit = TRUE)\n",
    "datos.support$predictions <- predictions$fit\n",
    "\n",
    "datos.support$lower <- predictions$fit - qnorm(1 - alpha / 2) * predictions$se.fit\n",
    "datos.support$upper <- predictions$fit + qnorm(1 - alpha / 2) * predictions$se.fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QQQQAABBBBAAIEMCYy227K/O32u+3Kza8Fd1qOuywp4t3ZhbhsYHhRrOqGG4q2t\nuV6rbmmZF+w2lBRboQtyVQrdOF8Fu/6/srIyKykp8YLg+vp6b5lM/0cAnGlxtocAAggggAAC\nCCCAAAIIpFhAY3D9Flx1W44cqxu0KQXGa1yr79akVD2m1lwFvEHdlye6ltp9ve7LrkXXdV9W\n6+4017Jb4gJcv4xzyyi4VZCrgNcPdv3Xc+WWADhXjgT7gQACCCCAAAIIIIAAAgiMUkAB7KZN\nm2zjxo3ev56enrjv7Bzqvry1NVdjdhXw9sV0Xy60ncaXua7LW4NcBbpq3dVY3ciiYNcPcP2A\nV8FvPpThNcmHPWYfEUAAAQQQQAABBBBAAIExJqCAV0FuZMCr5yJLr7Iv+92WvRZdl5jKdWHu\niBrTq3bbRhewbh2f67ox+92Xi4u97smR6yxywa8f5Potu3ouX0v+7nm+irPfCCCAAAIIIIAA\nAggggMAIAgpuFex2dHRYW1ubF/xGB7war/v6xk32cvdG+3t3t9eFeXhIbFZTNM5mVyj78tbW\n3BkuIdVUF/wWR3Rf9nel2AXAfpDr36q1N0yFADhMR5O6IIAAAggggAACCCCAQF4K+C28frdm\n3Y4fP978TM1+pZa5VuC/ewHvRlvsgt/+ba3A41ziqV2Gui9vTUilrswT4rTWqsuyglw/0NVt\n2IJd3yzylgA4UoP7CCCAAAIIIIAAAggggECGBPwuzQp29U+JrKJLR/+APdfRaYtc0Puya+Vt\nj8jG3OiyMM+eMN72rahwc+yOt7I4rbXRga4eK0PzWCwEwGPxqFNnBBBAAAEEEEAAAQQQyLjA\naALeAXVr3rS1lXeRa+F92wXGfqlwQetBlRO8Ls2zJ1RYneuyHFn8OXYjW3V1X89TtgoQAPNJ\nQAABBBBAAAEEEEAAAQTSIKBpiTSO1+/WHNTCq82ucHPubu3W3G2vuqC3b1u35kIXt+7hph96\nV3mZ7ecC3p3dFEP+HLv+7irArXAtwPqnLtOUxAIEwIl9eBUBBBBAAAEEEEAAAQQQGJWAH/Aq\n6FVr7+ao7Mv+SrrctESvdCt5VbeXwGq9e+yXBteqO3tCuWvlrbAD6iZZiQuGNa+vX9Sa6we8\nus3njMx+nTJ5SwCcSW22hQACCCCAAAIIIIAAAqERUGDqT0uk23gtvAMuiH0zIlvzEtfi62dr\nLnfdmt9bWWH7uWBW2ZobIubTLXdjepUES9mZy11L8IQJE7xbujRv/0eIAHj77XgnAggggAAC\nCCCAAAIIjCEBBbx+d2YFvPFaeEWyys3Hq27NauVVa2/vlq0JrpR6StmaFewqedWu7r4yOEcX\nzb1bX1/vzcEbPf1R9LI8Hr0AAfDorVgSAQQQQAABBBBAAAEExpBAMgFvt8vOvMgFxZqPV/Py\ntrrszX6pKy6y91VM8ALed7mW3Ao3N2900RREauX1uzfrcWVlpfX393vdqaOX5/H2CRAAb58b\n70IAAQQQQAABBBBAAIEQCqhVt6Ojw/un4DNeUbfmt7xszVsD3rfdfX8So7KCQnuPS1qlFt7Z\nFeNtiktUFVQ0F68CXnVtJoFVkFDqnyMATr0pa0QAAQQQQAABBBBAAIE8E+h2LbcKfDds2BB3\nz9e4LtB+t+ZXXbfmjdvm7VUH5p3KSrcFvOW2m5uTtyigW7PG7ka28mpsLyWzAgTAmfVmawgg\ngAACCCCAAAIIIJAjAmrhVdDb2dnpJZuK3q2NrjVY0xJ52Zo3bLS1ES3CtUVFduC2OXnf5cbz\nVrrHQUVZmv1uzQp+C13SK0r2BIKPUvb2hy0jgAACCCCAAAIIIIAAAmkTUEKprq4uL+hVIqvo\nMuBadZ/u2mAPtXfYay749bs1l7huzftvS1y1r7ud5lp84xV1Z/aDXs3TS8kdAQLg3DkW7AkC\nCCCAAAIIIIAAAgikSUBz9Kq1V8FvUPZmdW9+uL3THmlvt87NW8PemS543Zqtudz2VLfmOK23\natX1pylS4KsEVpTcFCAAzs3jwl4hgAACCCCAAAIIIIDADgoo0PVbe3t6emLWttm1Br+wodse\nbGv3Mjdrbl7Ny3tszUQ7pro6YSuvxu8qeZUCXgW/lPwQIADOj+PEXiKAAAIIIIAAAggggMAo\nBTRXr9/aGzSHbtvAgGvtbbeH2jptvbuvoiRWR7vA97CqKisLaOlVAqvIrs3K4EzJPwEC4IBj\nls2B6dncdgBFSp7y66SThn8/JSvOkZX49Qpj3UTs1y9HuFO2G6qXShiPW5jrpuM1Fj6Tmf5c\nZnp7+tvTceRYSiL/StiPW1jrp09aNv7W0/0J9+uk4zboxu76Ca00f69f9JqKAuFX3JjeB9a3\n2bNujK86OZe4146srrIP1dTYzuPLvOUi/4tMYKWWXn97kcuk877/ecz0dtNZp8h1+/WLfG57\n7/vHeaT3F7gPglr6KRECkX8wEU+n/a66USSaayztO5CmDejDqLqpC0rQeIs0bTZjq9WJUfUK\n45+Srmzqy2Rg25XRjKFmYEP+F4nqF7aiz6T+7sJ6PtG4qjB+JlUv/dNxS9X5ZDStE5ruYzTL\npfrvxP/RE8bvhXQcy1T778j6wvp7Rd8LOn/q/BLW74Ywnjv1WdbY3nXr1nnBb9D5s8sd0wfW\nttr9a9dac0+v9/Gf7oLd4xoa7Jj6OqsoGj5eV+enyspKq62tzXrXZp1PVKcwfiZ1LlFJ1e8V\nfb5H0xWdFmCPffh/+gPKRmlwf4TZ2nY666sfVpMmTTJl2Us0r1o69yGd665xVww1tiSMXypT\npkzxTkrr169PJ2FW1q2ruPpCCcr+mJUdSuFG9femL5Uwnk9ULx27dtdtLWxFP7Y0lkytF6n6\nMaC/4ZGKLvrqHJbpou8GdSVUfcNWqlz3Sf9zGsbvhvr6+lCeX8rKykzf6ZoLNqzfDW1tbaEJ\npHTu0vlDvy0nTpxoGt8b/TvzDdfa+2Bbhz3pznH97jt/nAtsD3Hn2mNcN+e9XRZnlYHuDeaf\nhRRs6u+32o391feNulHrXzaLvhv0nRA0fjmb+5WKbetcoosNqfq9ouNHAJyKI8M6EEAAAQQQ\nQAABBBBAIOsCagVVkKvA1w9MFUBFlk2uV97jnV220CW1Wta7tRt0vQtmj3LdnI+qnmgTXSt/\ndNEFOV38UPAbvb7oZXmc/wKxn4D8rxM1QAABBBBAAAEEEEAAgZAIqPVTQa96q8TrCrzEtfb+\nbtUae7yjy3q2DFqhq/t7JlR4Sa3209jdqEBZNOp1o8BXvVEoY0eAAHjsHGtqigACCCCAAAII\nIIBAXggoP4ACXgW+GuMbVPpci/DTXd328PJmW7xtKEe1G887Z2KtfdB1c65zLb/RJbqbc/Tr\nPA6/AAFw+I8xNUQAAQQQQAABBBBAIC8ENP7aH9sblNBKlVjlujY/2N5hf3X/urcls5ztxsoe\nWTXBDqicYEUBrb10c86Lw5+RnSQAzggzG0EAAQQQQAABBBBAAIEgASWLU9Db2dkZNwnggEti\n9ZybukhJrRa5IFmlwmXuPr62xj620yyrKyyISYKlZejmLAVKpAABcKQG9xFAAAEEEEAAAQQQ\nQCDtAmrdVcZtBb66jVdaXQbkh11Lr/61D2z2FtvVTWF0tEtodWhVpZW6zL+TyscPy5JMN+d4\nmjwvAQJgPgcIIIAAAggggAACCCCQEQF/+iK19sabB3zQBccvu6B4oWvtfXFDtw26PSstKLQP\nukzOx9RU2yw3ZVVQKS0t9aYwIptzkA7P+QIEwL4EtwgggAACCCCAAAIIIJAWASWyamlpSTjH\ncofrCv3X9k5vfG+La/lVmVFa4jI5V9vhrrV3vGvtjS6atkgBb2Njoym4piAwkgAB8EhCvI4A\nAggggAACCCCAAALbJaDxva2trd743ngrWNy90Rvb+5Sb43eza/1VEqv3uYBXrb17uO7NQcXv\n5lxbW2vTpk3z5gUmAA6S4rloAQLgaBEeI4AAAggggAACCCCAwA4JaL7e9evXW1tbmwVlc+52\n0xw91uFae10355XbWm4nu2mLNLb3A66rc2VRcJgS3c250CXCoiCQjEDwJyuZNbAsAggggAAC\nCCCAAAIIILBNoL293datWxc4xvftTT1ubG+7PdHZZX2utVfh64Fu6iIFvvtWlJu6NAcVsjkH\nqfDc9ggQAG+PGu9BAAEEEEAAAQQQQACBYQIbXBdmjfPt3zZ+N/LFV10351+1tNpbLgBWqXUt\nvEe5lt6jqquttjg4JPG7OVe7ZYpd6zAFgVQIBH/aUrFm1oEAAggggAACCCCAAAKhF+jp6fEC\n302bNsXUdY3r3vyrta32tJvDV2W2a+XV2N53T6iwcXFae6O7OceslCcQ2AEBAuAdwOOtCCCA\nAAIIIIAAAgiMVQG19CrBVVdXVwzBRjfG97et6+1+1915wHV13rms1D45uSFuUiutgG7OMYw8\nkQYBAuA0oLJKBBBAAAEEEEAAAQTCKqD5e5XgSmN9oxNcaQ7fRzo67Ndr11mnW66maJx9vL7O\njphYFTi+l27OYf2U5G69CIBz99iwZwgggAACCCCAAAII5IyAgl0FvQp+FQRHl1fcON/b1qy1\nZb19VuK6N8+dVOv+1VhZwPy9dHOO1uNxpgQIgDMlzXYQQAABBBBAAAEEEMhTASW4UnfnoLl2\nV7uA95cu+dWzXd1e7Q6prLSzJtdZXUDiKro55+kHIES7TQAcooNJVRBAAAEEEEAAAQQQSKWA\nElsp8A1KcKW5fO9z43z/5Mb5bt42zvdTbpzv7uXjY3ahvLzcGhoarKSkJOY1nkAgkwIEwJnU\nZlsIIIAAAggggAACCOSBgBJcaUojtfxGFwW7D7d32D1uWqPOzYPelEan10+ywwPG+Wr6ovr6\nei/BVfR6eIxANgQIgLOhzjYRQAABBBBAAAEEEMhBgUQJrrS7izZ0221rW2z5tnG+p9bV2olu\nrG9ZYeGw2hS4McC1tbXeP92nIJArAgTAuXIk2A8EEEAAAQQQQAABBLIkoARXbW1t3r+gBFer\nXMB7hwt8n3cBsMphVZV2ZkOdTYozzletvmr9pSCQawIEwLl2RNgfBBBAAAEEEEAAAQQyKKB5\nfDXOV92eo0v3wGb7XzfO98/tbW6cr9mu48vsU5Pr3W3sOF+N79U4X433pSCQqwIEwLl6ZNgv\nBBBAAAEEEEAAAQTSKKDEVhrn29PTE7MVjfN9aNs4365t43zPcC2+GucbXTSXr7o719TURL/E\nYwRyToAAOOcOCTuEAAIIIIAAAggggED6BDSVkQLf7u6t3Zmjt/SyxvmuabGVbrmSgkL76LZx\nvqVR43z1vqqqKi/JlYJgCgL5IEAAnA9HiX1EAAEEEEAAAQQQQGAHBQYGBryuzu3t7YFrau7t\ndeN8W+2FbeN8D3fBrVp9a4tjQ4bxrgu0xvmWlZUFrosnEchVgdhPc67uKfuFAAIIIIAAAggg\ngAACSQsowZXG+C5ZssSCElxtcIHxb9w43wdcYKxxvru7cb7/4ubz1Xjf6KKWXgW+avmlIJCP\nAgTA+XjU2GcEEEAAAQQQQAABBEYh0NnZ6QW/FRUVNjg4OOwdAxrn27Z1Pt8N7rU619J7Zn2d\nHRowzldTGVVXV9ukSZOsMKAr9LAV8wCBHBYgAM7hg8OuIYAAAggggAACCCCwPQIbN270xvn2\num7NQeXFDRvs9jWt1uzG+Za6cb4fr59kx9fWWElAcKvgWa2+yvJMQSDfBQiA8/0Isv8IIIAA\nAggggAACCGwTUIIrdXfe4ALcoLKiR+N8W+yl7o1W4BY4wrX2nu7G+dYUxYYFCnjr6upswoQJ\nQaviOQTyUiD2k56X1WCnEUAAAQQQQAABBBAYuwIa26vAt6OjIxChy43zvWXVGnugrd3UEXoP\nl8TqU431tlNAEit1cfanNVLXZwoCYRLImQB42bJl9sQTT3h/bIcddljcK01r1661F154IfAY\n7LrrrrbLLruYJvP+29/+FrPMUUcdZcXFxTHP8wQCCCCAAAIIIIAAAvkooHG9bW1t3r/oMb6q\nj8b5LnRB72/eeMs2uCC53v0WPtO1+B5SVRlY3crKSq+7c1FAi3DgG3gSgTwTyIkA+LbbbrOb\nbrrJPvCBD1hzc7Pp8bx58wIn01agfOONNw5jVkr3devW2QUXXOAFwC+99JJddtllXpeNyAUP\nPfRQAuBIEO4jgAACCCCAAAII5K2AGn00n69+CweVF1w36Du2jfMd71p1NaXRcTXVVhwwzre0\ntNQaGhpM0xtREAizQNYDYAW0N998s/3oRz+y/fff3/sDPu+88+yuu+4y3UaXAw44wO65555h\nT1999dX27LPP2ty5c73n33zzTdtnn31s/vz5w5bjAQIIIIAAAggggAAC+S6gll71ilSG56Ci\ncb63u3G+L28b53ukG+f7uV12tnE9m0xTIkUWTWukcb4TJ06MfJr7CIRWIOsB8NNPP21NTU1e\n8CtldbeYM2eO/epXvwoMgKOPhALf3//+93b99dcPTcStAHiPPfaIXjTwsRIERJ4I1EU6m2Md\nsrntQKAUPOnXSbf+/RSsNqdWEea6CTqsxy2sdfOPl3+bU38sO7gzfp382x1cXU693a+Tbv37\nmdrBTG9P9fK36d9mqq6Z2I5fJ9369zOx3UxuI4z18uuU68dt06ZNtnr1alOyq+iiLs73tKzz\nujxrnO/e5eVuPt86m+XG+U4sKbau3p6h372qpz+tkYLgfC35ctx2xDfXP5M7Uje91z+GO7qe\n0b4/6wHwqlWrbOrUqcP2VwGxBvHr6laiecaU1v373/++nXHGGbbnnnsOrUMBsLpxXHzxxfba\na6/ZXnvt5XWPjt6O3nDkkUd6Y4b9N6sV+YorrvAfZvy2sbEx49vM1AaVQTCsWQTLAhJIZMo1\n3dvR31KYP5dhvuId5uMW5i56mmMzk0XnLyW7yVYpdz/Qw1rUqhbWEubzS1VVlelfLha1+mp6\no+j9G3C/mX/XvNpuW7bcul0QPKWs1D6/00w7POoz6P+ta1ojHcMw/X5RXcL8uczFz2Oq9ilV\nxy3oolDQPmY9ANYVrOg/Yg2+V/CrLHY1NTVB++0998gjj3iB8sc+9rGhZTQWQusU5JlnnmmH\nH36412X6/PPPt9tvvz0mAFPCLZ1I/KJAuqenx3+Y0VsFGvHmasvojqR4Y7qIoTT6Gp8Sb4xK\nijeZ0dWp14AyLwYlnsjojqRhY/oyUd36+/vTsPbsrlK9TdT7Q/ULW9Hfm66mhvV8opaKsH4m\n9bnUcYvsmbQjn8/R/LjVuSsb33v6buBY7sjRzd57dY4Z7Q/N7O1l8lv2f6/o/JJr3w3ap5Ur\nVw77zerX8BmX4Oqm5Sus2XV7Lnd/V5+ZPtVOapxsxVHfA34vR43z9X97Z+Nv39/vVN7yeyWV\nmplbV6p/r+jvVuscqWQ9ANYfY3RQ5D8e6aqwuj4rcVbk1XK1MN59993e1WwfYO+997ZPf/rT\n9uCDDw6NE/ZhlGwruqhVOhtFJyRl8Qtb0XHQMVKXnXhz0uVznXWRRhde/M9tPtclet+nTJni\n1SuMn0td/VaQEXkBLLr++fpYf286t4bxuKleOnbt7e35enji7rcu/uo7TOeTVAX4+hseqSiQ\n0TYzXfTdoJb8eFO2ZHp/Urk9BRe6mKHxmWH8bqivrw/l+UVBlD6X+l7Ipe8G/X2uWbMm5kJ7\nqwuKb2xeY393+6uJij5YXWWn1buxvO6z1+OG+EU25+ii6M47uzHA7gKigoQwfT/owoWOnc5l\nYf1u0HdCWC5WRJ5rdS7RZzNVn0d9vvUbYaSS9QBY3YPeeeedYfupLwwFFWoRjVeUPEvZnq+7\n7rphiwgxuhldf/ACzlZgO2wHeYAAAggggAACCCCAwAgC6p0RL9HVM50b7H9WrbaNbhmN8/2k\nG+c70wWBQUUBgRpZ/IaWMPZYC6o3zyEQT6Aw3guZen6nnXbyxulGXiF95ZVXYsYFR+/PU089\n5Q3c32+//Ya9pGBarb3Lly8fel6Br1LEB40BHlqIOwgggAACCCCAAAII5ICAes0tXbo0Jstz\nnwt4b169xq5Z2Wz9rhfT2ZMb7L9mTgsMftWaPW3aNO/3r3rPUBBAYKtA1gPgY445xtuTO+64\nw+va8fbbb9uCBQvsk5/85NAxevTRR+3+++8feqw7OikoeI4us2bN8rpB/PSnP/Wa0xX8/uQn\nP/FalI8++ujoxXmMAAIIIIAAAggggEDOCKxfv95ryIkeCqGpjf5ryTJ7oK3Dprrg9tJZ0+3Y\n2uqY/VaXYPV8nDlzpo00nDDmzTyBwBgQyHoXaHVzvvTSS+2SSy4xBcEaD3TqqaeaklP5ZeHC\nhdbc3GzHHXec/5SppXfXXXcdehx558ILL7Tvfve7dsopp3hPqwu0ukpzEohU4j4CCCCAAAII\nIIBArggo4FUiV7X+RpeHXKKrW9e0WJ9r9T3KjfX9lGv5LXWBbnTR+HMFvxoLSUEAgWCBrAfA\n2q13v/vddt9993kD/PVHqytXkUXBbHSJHvsb+boyOf/yl7/0MkSry0eYpzmJrDf3EUAAAQQQ\nQAABBPJPQImuNN43Ovv0BjeDxk2r19rTXRu8DM/nNTXaIVWVMRVUEiiN8dUtBQEEEgvkRADs\n7+LkyZP9uym5DfP8eykBYiUIIIAAAggggAACWRNQQirlqQnKhv76xk12XfMqW9c/YLuPL7Pz\nm6ZYfcnwsbxq6dXvXRp7snYI2XAeCuRUAJyHfuwyAggggAACCCCAAAJJC2haG+WqiR7rO+i6\nOf923Xq7t2WdbXFrnTup1j5aP8mK3EwnkUVD+zTVGd2dI1W4j8DIAgTAIxuxBAIIIIAAAggg\ngAACKRNQoqvW1taY9a13rb3zV66yxW4ccE3ROK/Vd++K8pjl1OpbW1sb8zxPIIDAyAIEwCMb\nsQQCCCCAAAIIIIAAAjssoGk/1eoblOjqWTfO94bm1bbBdYt+z4QKO3fKZKssGv5Tvcg9Vquv\nksZSEEBg+wSG/1Vt3zp4FwIIIIAAAggggAACCCQQiJfoqt8FvHesbbW/uEzP6ub8qcn1Nqe2\nJmZNEyZMMOXLoctzDA1PIJCUAAFwUlwsjAACCCCAAAIIIIDA6AWU6EoZnjs7O2PepLl9lehq\nWW+fNbm5fS+Y2mizojI5F7igWF2ea2pig+KYFfIEAgiMKEAAPCIRCyCAAAIIIIAAAgggkLyA\nEl1pbt++vr6YNz/c3m6/WL11bt8jJlbZ2a7ltyxq/l5N56kuz0xvFMPHEwhstwAB8HbT8UYE\nEEAAAQQQQAABBIIF4iW66t682W5atcaecmN+xxcU2gVNk+0wFwBHF7o8R4vwGIHUCBAAp8aR\ntSCAAAIIIIAAAgggYIkSXb2xbW7fVpfteRc3t++XmhqtwXV9jizq8lxfX2/V1dWRT3MfAQRS\nJEAAnCJIVoMAAggggAACCCAwtgU2bNhga9assc2ulTeyaG7f37m5fe9xc/sOuhdOckmuTmuo\ni5nbV12em5qarLS0NPLt3EcAgRQKEACnEJNVIYAAAggggAACCIw9gUSJrjS370/c9Eavbtxo\nE90Y3y+6Vt993TRH0aWqqsoaGhqssLAw+iUeI4BACgUIgFOIyaoQQAABBBBAAAEExpZAokRX\nL7gW4Z+64Ldr86DtV1Fu57ngd2LU3L4KeNXleeLEiWMLjtoikCUBAuAswbNZBBBAAAEEEEAA\ngfwWiJfoSnP7/srN7fsnN7fvODem9xOuu/PxrtuzxvdGlhI3/lddnnVLQQCBzAgQAGfGma0g\ngAACCCCAAAIIhERAia40vdFG1605ujT39tqPV662pe620Y3p/dK0KbZT1Ny+eo9afNXlOToo\njl4fjxFAILUCBMCp9WRtCCCAAAIIIIAAAiEWiJfoSlX+a3uH3ezN7Tto73djej/TGDu3r7o8\nT5482SorK0OsRNUQyF0BAuDcPTbsGQIIIIAAAggggECOCCjRVUtLi3V0dMTs0UaX9fnnq9ba\nE11dVubm9v3ilEY7vDp2bt8y1xLc2NhIl+cYQZ5AIHMCBMCZs2ZLCCCAAAIIIIAAAnko0Ou6\nMzc3N1t/f3/M3v9jU49dt3KVrXWv7VxWahdMnWKNAWN6Na+vkl3R5TmGkCcQyKgAAXBGudkY\nAggggAACCCCAQD4JKNHVunXrbIubyzey6PEf1rXZr1tbbbN76QSX5Or0gLl9x7mpj9TlecKE\nCZFv5z4CCGRJgAA4S/BsFgEEEEAAAQQQQCB3BRIlump3SbCud4mu/u6SYFW5APdfmybbfgEB\n7vjx470uz8UuGRYFAQRyQ4AAODeOA3uBAAIIIIAAAgggkCMCiRJdvejN7bvGOt24333d3L7/\n6ub2rY6a21fVqKmp8bo850iV2A0EENgmQADMRwEBBBBAAAEEEEAAASegbs2a3kj/osuAS4J1\nZ8s6W7C+zc3ta3ZWfZ2dMCl2bl91eVaiq4qKiuhV8BgBBHJAgAA4Bw4Cu4AAAggggAACCCCQ\nXQEluHrrrbesvb09ZkdW9/bZj5tX2ZKeXmtw3ZmV6GrX8WUxy6nL85QpU6wooEU4ZmGeQACB\nrAhsdwC82XX70BUuFY2RePTRR23VqlV23HHHWW1tbVYqw0YRQAABBBBAAAEEEEhWYNOmTdbq\nklkFtdo+5ub2/bmb27d3y6AdVlVp5zQ22Phtv4EjtzNp0iTTPwoCCOS2QOH27N4111xjU6dO\ntZ6eHu/t55xzjh199NH2L//yLzZz5kx75ZVXtme1vAcBBBBAAAEEEEAAgYwKaF7f5cuXmxp3\nIssm93i+m97o+lVrvKfPnTLZa/mNDn7VIDRt2jSC30g87iOQwwJJB8CPPfaYXXTRRdbQ0GC6\nWvbcc8/ZrbfeakcccYT9+te/tlmzZnmBcA7XmV1DAAEEEEAAAQQQGOMCGu+7Zs0a7180xVtu\nbt9vLFlmj3d22czSUrtsp+n2geqJ0Yt5Lcb67VteXh7zGk8ggEBuCiTdBXrBggXe2IYXX3zR\nCgsL7b777vNqduWVV9qBBx7oTRCuluCuri6rrKzMzVqzVwgggAACCCCAAAJjVkCtvc3NzV5j\nTiSCguLfrmmx292wPs3tO6em2s7S3L7uN290qaurY9hfNAqPEcgDgaQD4DfeeMMOO+wwL/hV\n/e6//34vxfsBBxzgVXefffbxMui98847tu++++YBAbuIAAIIIIAAAgggMFYEent7veBXSa8i\nS6fLaXP5K4vtubZ2N7dvoZ3rpjd6d8DcvkpwpURXSnhFQQCB/BNIOgBWgqunnnrKq6mSXj3/\n/PN21llnWUGBywfvykMPPeTd6sRAQQABBBBAAAEEEEAgVwQ0v6+mOBp0UxpFlhUuu/OVK1bZ\n2v4+m11ZYec2TraagEzOSpKlKY78RLCR6+A+Agjkh0DSAfCcOXPsZz/7mZ1//vlesit1FfnE\nJz7hJQ5Qcqzvf//7dvDBB5u6heRryeZJLZvbTtfxUld5Fd2GsX66+BPWuum4qX5hPG46Zjp/\nhbFu/gXJMNZNxy3Mn0n9zem4Rf841/PpLNn4rIT5WEb+Deo8E7aSr3+D69atM/1T0efPLy90\nbbB5y113aJfl+WNTm7wuz319ff7L3q2W12/bmpqaYc/n0wP/uPmfz3za90T76h9Lv36Jls3H\n11Qv1TEb5+l0e6luqTxuWtdoSoE7MSd1ZtaX8oUXXmjz58/3DsZXv/pVu+KKK7wAWFfFlA1a\ngfDuu+8+mu3n5DLRJ71M7WSxm1cuujtOpradzu3ow6i6abxNdIbFdG43U+tWVyjVK8k/pUzt\n3g5tp6SkxPshrqnOwlb8L8xMBxqZcNRnUn93YT2f6EdAGD+Tqpf+6bil6nyiv+GRilrERrPc\nSOtJ9nV9RvV3GMbvhXQcy2R907l8vv1e0Xle4307OztjWO5zGZ5vWrrMxrnP45d32cnmTGn0\nzi+Rf4Oqr7I853uXZ303hPHcqYMa5t8rOp/o8xjG3yv621JJ1e8Vfb5Hk5Au6QDY20v3n5Jc\nqUQmulJirP333997Pp//U9fubBRl1l67dm02Np3WbeqkpHnx9JnRD62wFV0NVt3C+KWioQwa\nK7V+/fqwHTYvc6e+UDZu3Bi6uunvTV8q6uYXtqJ66WJre3t72KrmfZ9OcOMNNRdpqn4MjGY4\nkv6+9Xee6aLvBgUUmoImbKWqqsr7nLa0tITyu6G+vt5Ut3wo+m5euXJlzGd8wJ3/f+6C30c6\nOt1433F24bQm29dledax028Vf6pP/c6dPHnysBbjfKh30D7qu6GtrS10gZQupOkYaXaasH43\n6DvB/0wGHdt8fU7nEl0MTVX8o4sFiqdGKkl3gfZXGBn4+s+FIfj168ItAggggAACCCCAQP4K\nKCBSy290LwMlu7rWjfd9zb0+vbTEvjZtqtWXbG2J8murH+X6IT1xYuzUR/4y3CKAQH4KbFcA\nfO+999pVV11lS5cu9a62RHYT8Rl0hYmCAAIIIIAAAggggECmBdSzQK1K0b9RlezqhyuarcW1\nqL1nQoVd4DI9l7lWo8ii3glqUSx18/9SEEAgfAJJB8BPPPGEnX766V63pf3228+7OqarZBQE\nEEAAAQQQQAABBLItoO7ZQQ0xL7iuzT9esdp6XLKrE2pr7Ew3v29h1G/Y6upqbwxhGLubZvu4\nsH0EckUg6QD47rvvtrKyMm/6o9122y1X6sF+IIAAAggggAACCIxhAXV1Vh6XoNwOf1y33n65\nttVLdnXelMl2hBvvG100HnHq1KmhHJceXVceIzCWBZIOgHViOeCAA4zgdyx/bKg7AggggAAC\nCCCQOwKawUPjfaNn8hhwGaB/tnqt/TUi2dUe5eOH7bh6MiphnJJEURBAIPwC/5wEbZR1VfD7\n/PPPB15dG+UqWAwBBBBAAAEEEEAAgZQIKGvzsmXLYoJfJbv672UrveB3hkt29b2dZlh08Kus\nsdOnTzdlYKcggMDYEEg6AD777LOtqanJvvOd78ScaMYGGbVEAAEEEEAAAQQQyAUBTeOllt/o\nOVKX9/TYN99Zbq+7TM/vdcmuvjNzutVtm3PU328luZo5c6Y3tM9/jlsEEAi/QNJdoB9++GHT\nGIkf/vCHNm/ePG9icM3JGF1eeuml6Kd4jAACCCCAAAIIIIDADgsou/OaNWuss7MzZl3Pd22w\n61ZuTXZ1kkt2dYZLdhWdsFW/XdXtWXPIUhBAYGwJJB0AK6teb2+vHXjggWNLitoigAACCCCA\nAAIIZF1gwHVtVqtvUKbmP7hkV7/aluzqi1Ma7fDqqpj9ra2ttbq6upjneQIBBMaGQNIB8Be+\n8AXTPwoCCCCAAAIIIIAAApkUUNC7cuVKU8bnyKJkVze5ZFePbkt29W/Tmmz3qGRXWl7z+06c\nGJsBOnJd3EcAgXALJB0Ah5uD2iGAAAIIIIAAAgjkooC6O6vbs7o/R5YO1yJ8zYpV9oYb76tk\nV1+bPjVmvK+SXanLc3l5eeRbuY8AAmNQYLsDYHU/eeSRR+z111+3/v5+23///b1/mkCcggAC\nCCCAAAIIIIBAqgRaWlpMw/CiyzLXInzlimZr7R+w91ZW2Pmu23OZC3YjS0n28JhVAABAAElE\nQVRJiZfAVbcUBBBAYLsC4Oeee86UDXrRokUxgpdddpl9/etfj3meJxBAAAEEEEAAAQQQSEZA\nXZ1Xr15t3d3dMW97bluyq94tg3bypFo7vX5STLKr8ePHe8GvWoApCCCAgASSDoDb29tt7ty5\nphbgq6++2g4++GBv7rR33nnHfv7zn9s3vvENL538hRdeiDACCCCAAAIIIIAAAtsl0NfX5433\nVU/D6PKHVpfsqqXVxhUUWLxkV1VVVd6Y3+gM0NHr4jECCIwtgaQD4BtvvNEUBD///PO2++67\nD2nNnj3bTj75ZDv33HPt+uuvNwLgIRruIIAAAggggAACCCQhoBZftfwmSnY10bXqKtnVbgHJ\nrpTlWdmeKQgggEC0QNKTn2l+3yOPPHJY8Bu5UmWIfvPNN7309JHPcx8BBBBAAAEEEEAAgZEE\nNNY3KNOzkl19b9kKL9Ozkl1dutOMmOBX8/o2NTUR/I6EzOsIjGGBpFuANYZCXVLiFf+16Ct2\n8ZbneQQQQAABBBBAAAEElN1ZWZ6V7Tm6LHXJrq7aluzqwMoJ9q9TJsckuyoqKrKpU6daaWlp\n9Nt5jAACCAwJJN0CfMABB9hf//pXe/rpp4dW4t/RieuKK67wJhefPn26/zS3CCCAAAIIIIAA\nAgjEFVDDyYoVKwKD32ddsqvvvLPCy/Q81yW7+urUKTHBb1lZmc2YMYPgN64wLyCAgC+QdAvw\n5z73OS/5lbpBf/7zn7eDDjrIlGRASbBuueUWb2ywkmFREEAAAQQQQAABBBAYSaDHte42Nzd7\nCVajl/29S3Z1p0t2VaRkV02NdvjEquhFvGSsmuOXZFcxNDyBAAIBAkkHwEon//jjj9s555xj\n8+bNG7bKmpoamz9/vn3mM58Z9jwPEEAAAQQQQAABBBCIFujq6vKSXakXYWTpHxy0m1attcdc\nd2glu7poepPt6n6DRhclulLCKwoCCCAwWoGkA2CtWMkF7r//fq+ryuLFi23du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q1cZRvdhZ3ja2vsLDfet9ANh4ssfrJSZa6lIIAAArko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vbW0lynMkHVZCAAIQqF4CCMDV2zfUDAIQ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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(datos.support, aes(y = predictions,ymin = lower, ymax = upper, x = x, colour = z)) + \n",
    "    geom_line() + \n",
    "    geom_ribbon(alpha = 0.2, colour = NA) + \n",
    "    facet_grid(~ z) +\n",
    "    coord_cartesian(ylim = c(0, 1)) +\n",
    "    theme(legend.position = \"top\")"
   ]
  }
 ],
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