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<h1 class="title">Regresiones (parte 2)</h1>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
<p>Rodrigo Zepeda </p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">October 5, 2022</p>
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Warning
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<div class="callout-body-container callout-body">
<p>Si no lo has leído aún ve la <a href="https://rodrigozepeda.github.io/CursoR/Regresiones1.html">parte 1 de regresiones</a></p>
</div>
</div>
<section id="paquetes-y-datos-a-utilizar" class="level2">
<h2 class="anchored" data-anchor-id="paquetes-y-datos-a-utilizar">Paquetes y datos a utilizar</h2>
<p>A lo largo de esta sección usaremos los siguientes paquetes:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidymodels)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggfortify) <span class="co">#autoplot para diagnósticos</span></span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rstanarm) <span class="co">#para bayesiana</span></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(poissonreg) <span class="co">#modelo Poisson</span></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="co">#Siempre que uses tidymodels</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">tidymodels_prefer</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>En general usaremos la filosofía <code>tidymodels</code> para combinar los resultados con el <code>tidyverse</code>. Si quieres saber más de <code>tidymodels</code> te recomiendo checar <a href="https://www.tmwr.org">su libro</a> o <a href="https://www.tidymodels.org/learn/">su página web</a>.</p>
</section>
<section id="modelos-lineales-generalizados" class="level1">
<h1>Modelos lineales generalizados</h1>
<section id="regresión-poisson-conteo" class="level2">
<h2 class="anchored" data-anchor-id="regresión-poisson-conteo">Regresión Poisson (conteo)</h2>
<p>Anteriormente planteamos el modelo</p>
<p><span class="math display">\[
y \sim \textrm{Normal}(\beta_0 + \beta_1 x, \sigma^2)
\]</span></p>
<p>podemos cambiar la distribución normal por una Poisson si tenemos datos de conteo, por ejemplo:</p>
<p><span class="math display">\[
y \sim \textrm{Poisson}(\beta_0 + \beta_1 x)
\]</span></p>
<p>¡Y ya tenemos una regresión Poisson! Ésta no es la clásica pues es bien difícil de controlar numéricamente. La Poisson clásica tiene una exponencial adentro:</p>
<p><span class="math display">\[
y \sim \textrm{Poisson}\big(\exp(\beta_0 + \beta_1 x)\big)
\]</span></p>
<p>Usaremos esta última para los modelos. Para ello nos apoyaremos del <code>engine</code> de <code>glm</code> (por generalized linear model)</p>
<p>Para los datos usaremos la información de <em>Generalized Linear Models for Cross-Classified Data from the WFS. World Fertility Survey Technical Bulletins</em> . Podemos leerlos como sigue:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>niños <span class="ot"><-</span> <span class="fu">read_table</span>(<span class="st">"https://data.princeton.edu/wws509/datasets/ceb.dat"</span>,</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> <span class="at">skip =</span> <span class="dv">1</span>, <span class="at">col_names =</span> F) </span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(niños) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Fila"</span>, <span class="st">"dur"</span>, <span class="st">"res"</span>, <span class="st">"educ"</span>, <span class="st">"mean"</span>, <span class="st">"var"</span>, <span class="st">"n"</span>, <span class="st">"y"</span>)</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>niños <span class="ot"><-</span> niños <span class="sc">%>%</span> <span class="fu">mutate</span>(<span class="at">y =</span> <span class="fu">round</span>(y))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>La base contiene la <code>dur</code> la duración del matrimonio, <code>res</code> la residencia (suva, urbano y rural), el nivel educativo <code>educ</code>, la media y la varianza del número de niños nacidos y <code>n</code> la cantidad de mujeres en cada grupo. La variable <code>y</code> contiene el producto de <code>mean*n</code> para obtener el total de niños nacidos para todas las mujeres en cada grupo.</p>
<p>Podemos especificar fácilmente un modelo Poisson para los conteos:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>modelo_poisson <span class="ot"><-</span> <span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> educ, <span class="at">data =</span> niños)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>podemos obtener el summary:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>modelo_poisson <span class="sc">%>%</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ educ, family = stats::poisson, data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-21.757 -8.364 -2.780 3.087 51.573
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.38780 0.01594 338.06 < 2e-16 ***
educnone 0.16257 0.02168 7.50 6.39e-14 ***
educsec+ -2.13937 0.05178 -41.32 < 2e-16 ***
educupper -0.88738 0.02951 -30.07 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 13735.6 on 69 degrees of freedom
Residual deviance: 9076.8 on 66 degrees of freedom
AIC: 9514.3
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<p>o crear un <code>tibble</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>modelo_poisson <span class="sc">%>%</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">tidy</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 4 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 5.39 0.0159 338. 0
2 educnone 0.163 0.0217 7.50 6.39e- 14
3 educsec+ -2.14 0.0518 -41.3 0
4 educupper -0.887 0.0295 -30.1 1.22e-198</code></pre>
</div>
</div>
<p>o también podemos ajustarlo de manera bayesiana:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"stan"</span>) <span class="sc">%>%</span></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> educ, <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Podemos agregar más factores a nuestra regresión. Para ello vale la pena recordar las fórmulas de <code>R</code>:</p>
<table class="table">
<colgroup>
<col style="width: 19%">
<col style="width: 80%">
</colgroup>
<thead>
<tr class="header">
<th>Fórmula</th>
<th>Significado</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>1</code></td>
<td>El intercepto</td>
</tr>
<tr class="even">
<td><code>+x</code></td>
<td>Agregar la variable <code>x</code> a la regresión</td>
</tr>
<tr class="odd">
<td><code>-x</code></td>
<td>Quitar la variable <code>x</code> de la regresión</td>
</tr>
<tr class="even">
<td><code>x1:x2</code></td>
<td>Interacción entre <code>x1</code> y <code>x2</code></td>
</tr>
<tr class="odd">
<td><code>x1*x2</code></td>
<td>Cruzamiento es lo mismo que poner <code>x1 + x2 + x1:x2</code></td>
</tr>
<tr class="even">
<td><code>I()</code></td>
<td>Se utiliza para realizar operaciones aritméticas adentro. Por ejemplo generar una variable que sea <code>x1+ x2 haciendo I(x1 + x2)</code></td>
</tr>
<tr class="odd">
<td><code>(x | grupo)</code></td>
<td>Modelos mixtos donde la pendiente de <code>x</code> depende del grupo (efectos aleatorios)</td>
</tr>
</tbody>
</table>
<p>En nuestro modelo podemos incluir otras variables (digamos la residencia) agregándolas con suma:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> educ <span class="sc">+</span> res, <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ educ + res, family = stats::poisson,
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-24.513 -5.069 0.064 3.826 35.524
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.02352 0.01759 342.40 < 2e-16 ***
educnone 0.16257 0.02168 7.50 6.4e-14 ***
educsec+ -2.10440 0.05178 -40.64 < 2e-16 ***
educupper -0.88738 0.02951 -30.07 < 2e-16 ***
resSuva -1.43392 0.02783 -51.52 < 2e-16 ***
resurban -1.04901 0.02405 -43.62 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 13735.6 on 69 degrees of freedom
Residual deviance: 5058.4 on 64 degrees of freedom
AIC: 5499.9
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<p>La educación, sin embargo, es una variable ordinal donde <code>No educación</code> es menor que <code>Sec+</code> y es menor que <code>Upper</code>. Para ello podemos convertir la variable a factor y especificar el orden:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>niños <span class="ot"><-</span> niños <span class="sc">%>%</span></span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">edf =</span> <span class="fu">factor</span>(educ, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"none"</span>,<span class="st">"lower"</span>, <span class="st">"sec+"</span>,<span class="st">"upper"</span>),</span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a> <span class="at">ordered =</span> T))</span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> edf <span class="sc">+</span> res, <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ edf + res, family = stats::poisson,
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-24.513 -5.069 0.064 3.826 35.524
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.31622 0.01663 319.71 <2e-16 ***
edf.L -1.17488 0.02256 -52.08 <2e-16 ***
edf.Q 0.68980 0.02964 23.27 <2e-16 ***
edf.C 1.17690 0.03533 33.31 <2e-16 ***
resSuva -1.43392 0.02783 -51.52 <2e-16 ***
resurban -1.04901 0.02405 -43.62 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 13735.6 on 69 degrees of freedom
Residual deviance: 5058.4 on 64 degrees of freedom
AIC: 5499.9
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<p>Podemos agregar también la interacción entre educación y residencia:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> edf <span class="sc">+</span> res <span class="sc">+</span> edf<span class="sc">:</span>res, <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ edf + res + edf:res, family = stats::poisson,
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-27.2031 -3.9356 -0.4294 2.5603 31.3342
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.09851 0.02542 200.582 < 2e-16 ***
edf.L -1.57133 0.03346 -46.968 < 2e-16 ***
edf.Q 1.06376 0.05084 20.925 < 2e-16 ***
edf.C 1.55867 0.06364 24.493 < 2e-16 ***
resSuva -1.08021 0.03977 -27.164 < 2e-16 ***
resurban -0.65206 0.03599 -18.117 < 2e-16 ***
edf.L:resSuva 0.72235 0.06338 11.397 < 2e-16 ***
edf.Q:resSuva -0.71608 0.07953 -9.004 < 2e-16 ***
edf.C:resSuva -0.63730 0.09292 -6.859 6.94e-12 ***
edf.L:resurban 1.04382 0.05480 19.048 < 2e-16 ***
edf.Q:resurban -0.77504 0.07198 -10.767 < 2e-16 ***
edf.C:resurban -0.56048 0.08579 -6.533 6.46e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 13735.6 on 69 degrees of freedom
Residual deviance: 4520.8 on 58 degrees of freedom
AIC: 4974.2
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<p>que es lo mismo que el operador de cruzamiento <code>*</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> edf <span class="sc">*</span> res, <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb15-5"><a href="#cb15-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ edf * res, family = stats::poisson,
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-27.2031 -3.9356 -0.4294 2.5603 31.3342
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.09851 0.02542 200.582 < 2e-16 ***
edf.L -1.57133 0.03346 -46.968 < 2e-16 ***
edf.Q 1.06376 0.05084 20.925 < 2e-16 ***
edf.C 1.55867 0.06364 24.493 < 2e-16 ***
resSuva -1.08021 0.03977 -27.164 < 2e-16 ***
resurban -0.65206 0.03599 -18.117 < 2e-16 ***
edf.L:resSuva 0.72235 0.06338 11.397 < 2e-16 ***
edf.Q:resSuva -0.71608 0.07953 -9.004 < 2e-16 ***
edf.C:resSuva -0.63730 0.09292 -6.859 6.94e-12 ***
edf.L:resurban 1.04382 0.05480 19.048 < 2e-16 ***
edf.Q:resurban -0.77504 0.07198 -10.767 < 2e-16 ***
edf.C:resurban -0.56048 0.08579 -6.533 6.46e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 13735.6 on 69 degrees of freedom
Residual deviance: 4520.8 on 58 degrees of freedom
AIC: 4974.2
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<p>En los modelos de Poisson es usual usar un <code>offset</code> dado por el tamaño de la población. Si el <code>offset</code> es <span class="math inline">\(\lambda\)</span> el modelo se ve como:</p>
<p><span class="math display">\[
y \sim \textrm{Poisson}\big(\lambda\cdot \exp(\beta_0 + \beta_1 x)\big)
\]</span></p>
<p>Usualmente el <code>offset</code> es <code>log(n)</code> donde <code>n</code> es el tamaño de la población. Esto se escribe como:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>niños <span class="ot"><-</span> niños <span class="sc">%>%</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">logn =</span> <span class="fu">log</span>(n))</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a><span class="fu">poisson_reg</span>() <span class="sc">%>%</span></span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(y <span class="sc">~</span> edf <span class="sc">*</span> res <span class="sc">+</span> <span class="fu">offset</span>(logn), <span class="at">data =</span> niños) <span class="sc">%>%</span></span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = y ~ edf * res + offset(logn), family = stats::poisson,
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-19.026 -3.495 1.054 3.626 12.824
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.12094 0.02542 44.099 < 2e-16 ***
edf.L -0.66309 0.03346 -19.820 < 2e-16 ***
edf.Q 0.42450 0.05084 8.350 < 2e-16 ***
edf.C 0.54161 0.06364 8.511 < 2e-16 ***
resSuva -0.08300 0.03977 -2.087 0.03688 *
resurban 0.09584 0.03599 2.663 0.00775 **
edf.L:resSuva -0.11854 0.06338 -1.870 0.06144 .
edf.Q:resSuva -0.03023 0.07953 -0.380 0.70392
edf.C:resSuva 0.04214 0.09292 0.454 0.65016
edf.L:resurban 0.17105 0.05480 3.121 0.00180 **
edf.Q:resurban -0.05337 0.07198 -0.741 0.45849
edf.C:resurban -0.01822 0.08579 -0.212 0.83184
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 3731.9 on 69 degrees of freedom
Residual deviance: 2625.9 on 58 degrees of freedom
AIC: 3079.4
Number of Fisher Scoring iterations: 5</code></pre>
</div>
</div>
<section id="ejercicios" class="level3">
<h3 class="anchored" data-anchor-id="ejercicios">Ejercicios</h3>
<ol type="1">
<li><p>Utilice los datos en la <a href="https://data.princeton.edu/wws509/datasets/#smoking">siguiente página</a>. Determine si hay una asociación entre el consumo de cigarros, pipas y puros y la muerte.</p></li>
<li><p>Utilice los datos en la <a href="https://data.princeton.edu/wws509/datasets/#cancer">siguiente página</a>. Determine si la edad influye en la supervivencia de los tuomores.</p></li>
</ol>
</section>
</section>
<section id="regresión-logística-y-probit" class="level2">
<h2 class="anchored" data-anchor-id="regresión-logística-y-probit">Regresión logística y probit</h2>
<p>En las regresiones logística y probit el planteamiento es similar sólo que se utilizan para respuestas binarias (0 ó 1). En este caso los modelos son:</p>
<p><span class="math display">\[
y \sim \textrm{Bernoulli}\big(\textrm{logit}(\beta_0 + \beta_1 x)\big)
\]</span></p>
<p>con</p>
<p><span class="math display">\[
\textrm{logit}(p) = \frac{p}{1-p}
\]</span></p>
<p>y</p>
<p><span class="math display">\[
y \sim \textrm{Bernoulli}\big(\Phi(\beta_0 + \beta_1 x)\big)
\]</span> donde <span class="math inline">\(\Phi\)</span> es la función de distribución acumulada de una normal. En ambos casos la especificación es similar:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>anticonceptivos <span class="ot"><-</span> <span class="fu">read_table</span>(<span class="st">"https://data.princeton.edu/wws509/datasets/cuse.raw"</span>,</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a> <span class="at">skip =</span> <span class="dv">0</span>, <span class="at">col_names =</span> F) <span class="sc">%>%</span> <span class="fu">uncount</span>(X5)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>
── Column specification ────────────────────────────────────────────────────────
cols(
X1 = col_double(),
X2 = col_double(),
X3 = col_double(),
X4 = col_double(),
X5 = col_double()
)</code></pre>
</div>
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(anticonceptivos) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"age"</span>, <span class="st">"education"</span>, <span class="st">"wants_more_children"</span>,</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a> <span class="st">"using_contraceptive"</span>)</span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>anticonceptivos <span class="ot"><-</span> anticonceptivos <span class="sc">%>%</span></span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a> <span class="at">age =</span> <span class="fu">case_when</span>(</span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a> age <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="st">"<25"</span>,</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a> age <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="st">"25-29"</span>, </span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a> age <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="st">"30-39"</span>, </span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a> age <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="st">"40-49"</span></span>
<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a> ) <span class="sc">%>%</span></span>
<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">education =</span> <span class="fu">if_else</span>(education <span class="sc">==</span> <span class="dv">0</span>, <span class="st">"None"</span>, <span class="st">"Some"</span>)) <span class="sc">%>%</span></span>
<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">wants_more_children =</span> <span class="fu">if_else</span>(wants_more_children <span class="sc">==</span> <span class="dv">0</span>, <span class="st">"No"</span>, <span class="st">"More"</span>)) <span class="sc">%>%</span></span>
<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">usa_anticonceptivo =</span> <span class="fu">factor</span>(using_contraceptive, </span>
<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a> <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>), <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"No"</span>,<span class="st">"Yes"</span>))) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Podemos checar si la gente está usando o no anticonceptivos en función del grupo de edad con una regresión logística:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>modelo_logit <span class="ot"><-</span> <span class="fu">logistic_reg</span>() <span class="sc">%>%</span></span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) <span class="sc">%>%</span></span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(usa_anticonceptivo <span class="sc">~</span> age <span class="sc">+</span> education, <span class="at">data =</span> anticonceptivos) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>donde podemos ver sus estadísticas por ejemplo con <code>tidy</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>modelo_logit <span class="sc">%>%</span></span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">tidy</span>(<span class="at">conf.int =</span> T, <span class="at">exponentiate =</span> T)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 5 × 7
term estimate std.error statistic p.value conf.low conf.high
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 0.173 0.166 -10.6 3.61e-26 0.124 0.238
2 age25-29 1.62 0.173 2.78 5.36e- 3 1.16 2.28
3 age30-39 3.14 0.159 7.17 7.77e-13 2.30 4.31
4 age40-49 4.87 0.205 7.72 1.20e-14 3.26 7.30
5 educationSome 1.35 0.122 2.47 1.37e- 2 1.06 1.72 </code></pre>
</div>
</div>
<p>De hecho la logística es sólo un tipo de lineal por lo cual puede hacerse así:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="fu">linear_reg</span>() <span class="sc">%>%</span></span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>, <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"logit"</span>)) <span class="sc">%>%</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(using_contraceptive <span class="sc">~</span> age <span class="sc">+</span> education, <span class="at">data =</span> anticonceptivos) <span class="sc">%>%</span></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = using_contraceptive ~ age + education, family = ~binomial(link = "logit"),
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2313 -0.9304 -0.6474 1.3131 1.9573
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.7562 0.1660 -10.582 < 2e-16 ***
age25-29 0.4821 0.1731 2.784 0.00536 **
age30-39 1.1428 0.1595 7.165 7.77e-13 ***
age40-49 1.5822 0.2051 7.716 1.20e-14 ***
educationSome 0.2999 0.1216 2.465 0.01368 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2003.7 on 1606 degrees of freedom
Residual deviance: 1918.3 on 1602 degrees of freedom
AIC: 1928.3
Number of Fisher Scoring iterations: 4</code></pre>
</div>
</div>
<p>Podemos ponerla directo en <code>Word</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(gtsummary)</span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(flextable)</span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a><span class="fu">linear_reg</span>() <span class="sc">%>%</span></span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>, <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"logit"</span>)) <span class="sc">%>%</span></span>
<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(using_contraceptive <span class="sc">~</span> age <span class="sc">+</span> education, <span class="at">data =</span> anticonceptivos) <span class="sc">%>%</span></span>
<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">tbl_regression</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span>
<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">as_flex_table</span>() <span class="sc">%>%</span></span>
<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">save_as_docx</span>(<span class="at">path =</span> <span class="st">"Tabla.docx"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p><a href="https://www.danieldsjoberg.com/gtsummary/">Acá</a> hay más opciones para personalizar tu tabla de Word. <strong>Ojo</strong> <code>tbl_regression</code> sólo está disponible para los modelos que aparecen <a href="https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html">en la tabla</a></p>
<p>O bien una probit sólo cambiando el modelo:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="fu">linear_reg</span>() <span class="sc">%>%</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>, <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">"probit"</span>)) <span class="sc">%>%</span></span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(using_contraceptive <span class="sc">~</span> age <span class="sc">+</span> education, <span class="at">data =</span> anticonceptivos) <span class="sc">%>%</span></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">summary</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>
Call:
stats::glm(formula = using_contraceptive ~ age + education, family = ~binomial(link = "probit"),
data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.2279 -0.9322 -0.6484 1.3145 1.9668
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.06014 0.09566 -11.082 < 2e-16 ***
age25-29 0.28020 0.09980 2.808 0.00499 **
age30-39 0.68135 0.09271 7.349 1.99e-13 ***
age40-49 0.95330 0.12262 7.774 7.58e-15 ***
educationSome 0.18071 0.07339 2.462 0.01380 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2003.7 on 1606 degrees of freedom
Residual deviance: 1918.4 on 1602 degrees of freedom
AIC: 1928.4
Number of Fisher Scoring iterations: 4</code></pre>
</div>
</div>
<section id="ejercicio" class="level3">
<h3 class="anchored" data-anchor-id="ejercicio">Ejercicio</h3>
<ol type="1">
<li><p>Descargue la base de datos disponible <a href="https://github.com/jenineharris/logistic-regression-tutorial/blob/main/lab%202%20data.dta">en este link</a>. La codificación de <code>imc</code> es <code>0</code> si normal o bajo y <code>1</code> si sobrepeso u obesidad. La variable <code>yearsSmoke</code> son los años que lleva la persona fumando cigarrillos y <code>lungCancer</code> es <code>1</code> si ha tenido un diagnóstico de cáncer de pulmón y <code>0</code> si no. Determine si los años que lleva fumando la persona influyen en el diagnóstico. ¿Qué dice la <span class="math inline">\(R^2\)</span> del ajuste?</p></li>
<li><p>Repite el análisis de la base de anticonceptivos pero ahora con un modelo bayesiano (<code>stan</code>). ¿Qué dice el <code>loo</code>?</p></li>
</ol>
<!--
## Validación de una logística
http://www.sthda.com/english/articles/36-classification-methods-essentials/148-logistic-regression-assumptions-and-diagnostics-in-r
-->
</section>
</section>
</section>
<section id="regresión-multinomial" class="level1">
<h1>Regresión Multinomial</h1>
<p>Mientras que una logística sirve para clasificar en dos categorías, una multinomial clasifica en múltiples. Leamos los datos:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(forcats) <span class="co">#para el as_factor</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(haven)</span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a>ml <span class="ot"><-</span> <span class="fu">read_dta</span>(<span class="st">"https://stats.idre.ucla.edu/stat/data/hsbdemo.dta"</span>)</span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a><span class="co">#Ponerle las etiquetas de stata</span></span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a>ml <span class="ot"><-</span> ml <span class="sc">%>%</span></span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="fu">across</span>(<span class="fu">c</span>(female<span class="sc">:</span>prog, honors), <span class="sc">~</span> <span class="fu">as_factor</span>(.)))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>en particular nos interesa estudiar el programa a partir de los valores de <code>ses</code> (nivel socioeconómico) y <code>write</code> (score de su ensayo)</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>ml <span class="sc">%>%</span></span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">group_by</span>(ses, prog) <span class="sc">%>%</span></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">tally</span>() <span class="sc">%>%</span></span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_wider</span>(<span class="at">id_cols =</span> ses, <span class="at">names_from =</span> prog, <span class="at">values_from =</span> n)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 3 × 4
# Groups: ses [3]
ses general academic vocation
<fct> <int> <int> <int>
1 low 16 19 12
2 middle 20 44 31
3 high 9 42 7</code></pre>
</div>
</div>
<p>Único cambio es ahora usar <code>multinom_reg</code> para la regresión y el <code>engine</code> default recomendado es <code>nnet</code> (ver más <a href="https://parsnip.tidymodels.org/reference/multinom_reg.html">acá</a>)</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>modelo_multi <span class="ot"><-</span> <span class="fu">multinom_reg</span>() <span class="sc">%>%</span></span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"nnet"</span>) <span class="sc">%>%</span></span>
<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(prog <span class="sc">~</span> ses <span class="sc">+</span> write, <span class="at">data =</span> ml)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Y listo:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a>modelo_multi <span class="sc">%>%</span></span>
<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">extract_fit_engine</span>() <span class="sc">%>%</span></span>
<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">tidy</span>(<span class="at">exponentiate =</span> <span class="cn">TRUE</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 8 × 6
y.level term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 academic (Intercept) 0.0577 1.17 -2.45 0.0145
2 academic sesmiddle 1.70 0.444 1.20 0.229
3 academic seshigh 3.20 0.514 2.26 0.0237
4 academic write 1.06 0.0214 2.71 0.00682
5 vocation (Intercept) 10.7 1.17 2.01 0.0439
6 vocation sesmiddle 2.28 0.490 1.68 0.0925
7 vocation seshigh 1.20 0.648 0.278 0.781
8 vocation write 0.946 0.0233 -2.39 0.0170 </code></pre>
</div>
</div>
<section id="ejercicio-1" class="level2">
<h2 class="anchored" data-anchor-id="ejercicio-1">Ejercicio</h2>
<ol type="1">
<li>Obtén la siguiente base de datos de pingüinos:</li>
</ol>
<div class="cell">
<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(palmerpenguins)</span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(penguins)</span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>pinguinos <span class="ot"><-</span> penguins</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Construye un modelo para clasificar un pingüino según su especie (<code>species</code>). Utiliza el modelo para determinar la especie de los siguientes tres pingüinos:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>pinguinos_a_determinar <span class="ot"><-</span> <span class="fu">tibble</span>(</span>
<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a> <span class="at">island =</span> <span class="fu">c</span>(<span class="st">"Torgersen"</span>,<span class="st">"Torgersen"</span>,<span class="st">"Dream"</span>),</span>
<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a> <span class="at">bill_length_mm =</span> <span class="fu">c</span>(<span class="dv">20</span>, <span class="dv">18</span>, <span class="dv">14</span>),</span>
<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a> <span class="at">flipper_length_mm =</span> <span class="fu">c</span>(<span class="dv">180</span>, <span class="dv">200</span>, <span class="dv">190</span>)</span>
<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
</section>
<section id="resumen-y-validación-de-predicciones" class="level1">
<h1>Resumen y validación de predicciones</h1>
<p><strong>Sección incompleta</strong></p>
<p>En muchos casos al hacer modelos predictivos lo que interesa es solamente si puedes predecir bien el caso. En ese sentido las métricas anteriores no son las mejores.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Nos interesa predecir diabetes</span></span>
<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>diabetes <span class="ot"><-</span> <span class="fu">read_csv</span>(<span class="at">file =</span> <span class="st">"https://raw.githubusercontent.com/MicrosoftDocs/ml-basics/master/data/diabetes.csv"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Rows: 15000 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (10): PatientID, Pregnancies, PlasmaGlucose, DiastolicBloodPressure, Tri...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.</code></pre>
</div>
<div class="sourceCode cell-code" id="cb40"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Limpiamos como factor diabetic</span></span>
<span id="cb40-2"><a href="#cb40-2" aria-hidden="true" tabindex="-1"></a>diabetes <span class="ot"><-</span> diabetes <span class="sc">%>%</span></span>
<span id="cb40-3"><a href="#cb40-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">Diabetic =</span> <span class="fu">factor</span>(Diabetic, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"1"</span>,<span class="st">"0"</span>),</span>
<span id="cb40-4"><a href="#cb40-4" aria-hidden="true" tabindex="-1"></a> <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">"Diabetico"</span>,<span class="st">"No diabético"</span>))) <span class="sc">%>%</span> </span>
<span id="cb40-5"><a href="#cb40-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(<span class="sc">-</span>PatientID)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Visualización:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb41"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Pivot data to a long format</span></span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a>diabetes_select_long <span class="ot"><-</span> diabetes <span class="sc">%>%</span> </span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span>(<span class="sc">!</span>Diabetic, <span class="at">names_to =</span> <span class="st">"features"</span>, <span class="at">values_to =</span> <span class="st">"values"</span>)</span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb41-5"><a href="#cb41-5" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(diabetes_select_long, </span>
<span id="cb41-6"><a href="#cb41-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x =</span> Diabetic, <span class="at">y =</span> values, <span class="at">fill =</span> features)) <span class="sc">+</span></span>
<span id="cb41-7"><a href="#cb41-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_boxplot</span>() <span class="sc">+</span> </span>
<span id="cb41-8"><a href="#cb41-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">facet_wrap</span>(<span class="sc">~</span> features, <span class="at">scales =</span> <span class="st">"free"</span>, <span class="at">ncol =</span> <span class="dv">4</span>) <span class="sc">+</span></span>
<span id="cb41-9"><a href="#cb41-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">"plasma"</span>, <span class="at">end =</span> .<span class="dv">7</span>) <span class="sc">+</span></span>
<span id="cb41-10"><a href="#cb41-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>) <span class="sc">+</span></span>
<span id="cb41-11"><a href="#cb41-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_light</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="Regresiones_2_files/figure-html/unnamed-chunk-25-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>Comenzamos dividiendo los datos</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb42"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Seleccionamos 70% para entrenar (usualmente 70-80)</span></span>
<span id="cb42-2"><a href="#cb42-2" aria-hidden="true" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">2056</span>)</span>
<span id="cb42-3"><a href="#cb42-3" aria-hidden="true" tabindex="-1"></a>diabetes_split <span class="ot"><-</span> diabetes <span class="sc">%>%</span> </span>
<span id="cb42-4"><a href="#cb42-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">initial_split</span>(<span class="at">prop =</span> <span class="fl">0.70</span>)</span>
<span id="cb42-5"><a href="#cb42-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb42-6"><a href="#cb42-6" aria-hidden="true" tabindex="-1"></a>diabetes_train <span class="ot"><-</span> <span class="fu">training</span>(diabetes_split)</span>
<span id="cb42-7"><a href="#cb42-7" aria-hidden="true" tabindex="-1"></a>diabetes_test <span class="ot"><-</span> <span class="fu">testing</span>(diabetes_split)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Construimos el modelo:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb43"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>modelo_logreg <span class="ot"><-</span> <span class="fu">logistic_reg</span>() <span class="sc">%>%</span> </span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>) </span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Ajustamos:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb44"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb44-1"><a href="#cb44-1" aria-hidden="true" tabindex="-1"></a>logreg_fit <span class="ot"><-</span> modelo_logreg <span class="sc">%>%</span> </span>
<span id="cb44-2"><a href="#cb44-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(Diabetic <span class="sc">~</span> ., <span class="at">data =</span> diabetes_train) <span class="co">#. significa vs todos</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Checamos contra los observados:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb45"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb45-1"><a href="#cb45-1" aria-hidden="true" tabindex="-1"></a>predichos <span class="ot"><-</span> logreg_fit <span class="sc">%>%</span> </span>
<span id="cb45-2"><a href="#cb45-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">predict</span>(<span class="at">new_data =</span> diabetes_test)</span>
<span id="cb45-3"><a href="#cb45-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb45-4"><a href="#cb45-4" aria-hidden="true" tabindex="-1"></a>results <span class="ot"><-</span> diabetes_test <span class="sc">%>%</span> </span>
<span id="cb45-5"><a href="#cb45-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(Diabetic) <span class="sc">%>%</span> </span>
<span id="cb45-6"><a href="#cb45-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_cols</span>(predichos)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Midamos precisión (positive predictive value), accuracy, sensibilidad (recall), especificidad:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb46"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb46-1"><a href="#cb46-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine metrics and evaluate them all at once</span></span>
<span id="cb46-2"><a href="#cb46-2" aria-hidden="true" tabindex="-1"></a>eval_metrics <span class="ot"><-</span> <span class="fu">metric_set</span>(precision, sensitivity, </span>
<span id="cb46-3"><a href="#cb46-3" aria-hidden="true" tabindex="-1"></a> accuracy, specificity)</span>
<span id="cb46-4"><a href="#cb46-4" aria-hidden="true" tabindex="-1"></a><span class="fu">eval_metrics</span>(<span class="at">data =</span> results, <span class="at">truth =</span> Diabetic, <span class="at">estimate =</span> .pred_class)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code># A tibble: 4 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 precision binary 0.754
2 sensitivity binary 0.577
3 accuracy binary 0.789
4 specificity binary 0.901</code></pre>
</div>
</div>
<p>También podemos sacar la matriz de confusión:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb48"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" aria-hidden="true" tabindex="-1"></a><span class="fu">conf_mat</span>(<span class="at">data =</span> results, <span class="at">truth =</span> Diabetic, <span class="at">estimate =</span> .pred_class)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> Truth
Prediction Diabetico No diabético
Diabetico 897 293
No diabético 657 2653</code></pre>
</div>
</div>
<p>También podemos armar nuestra curva <code>roc</code>:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb50"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb50-1"><a href="#cb50-1" aria-hidden="true" tabindex="-1"></a>predice_proba <span class="ot"><-</span> logreg_fit <span class="sc">%>%</span> </span>
<span id="cb50-2"><a href="#cb50-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">predict</span>(<span class="at">new_data =</span> diabetes_test, <span class="at">type =</span> <span class="st">"prob"</span>)</span>
<span id="cb50-3"><a href="#cb50-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb50-4"><a href="#cb50-4" aria-hidden="true" tabindex="-1"></a>results <span class="ot"><-</span> results <span class="sc">%>%</span> </span>
<span id="cb50-5"><a href="#cb50-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">bind_cols</span>(predice_proba)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb51"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb51-1"><a href="#cb51-1" aria-hidden="true" tabindex="-1"></a>results <span class="sc">%>%</span></span>
<span id="cb51-2"><a href="#cb51-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">roc_curve</span>(<span class="at">truth =</span> Diabetic, .pred_Diabetico) <span class="sc">%>%</span> </span>
<span id="cb51-3"><a href="#cb51-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">autoplot</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="Regresiones_2_files/figure-html/unnamed-chunk-33-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<section id="ejercicio-2" class="level2">
<h2 class="anchored" data-anchor-id="ejercicio-2">Ejercicio</h2>
<ol type="1">
<li>Construye tres modelos distintos para predecir diabetes a partir de los siguientes datos <strong>sin usar la variable <code>glucose</code></strong>:</li>
</ol>
<div class="cell">
<div class="sourceCode cell-code" id="cb52"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb52-1"><a href="#cb52-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(mlbench)</span>
<span id="cb52-2"><a href="#cb52-2" aria-hidden="true" tabindex="-1"></a><span class="fu">data</span>(PimaIndiansDiabetes)</span>
<span id="cb52-3"><a href="#cb52-3" aria-hidden="true" tabindex="-1"></a>diabedatos <span class="ot"><-</span> PimaIndiansDiabetes</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Tus modelos pueden ser logísticos pero al menos uno de los tres que sea un árbol en <code>mode = "classification"</code> (opciones: <a href="https://parsnip.tidymodels.org/reference/bart.html"><code>bart</code></a>, <a href="https://parsnip.tidymodels.org/reference/rand_forest.html"><code>random_forest</code></a>, <a href="https://parsnip.tidymodels.org/reference/boost_tree.html"><code>boost_tree</code></a>, <a href="https://parsnip.tidymodels.org/reference/decision_tree.html"><code>decision_tree</code></a>, <a href="https://parsnip.tidymodels.org/reference/bag_tree.html"><code>bag_tree</code></a>)</p>
<p>Utiliza métricas (como sensibilidad, especificidad, etc) sobre el para decidir cuál es el mejor modelo.</p>
</section>
</section>
<section id="series-de-tiempo" class="level1">
<h1>Series de tiempo</h1>
<p><strong>Sección incompleta</strong></p>
<section id="análisis-preliminar" class="level2">
<h2 class="anchored" data-anchor-id="análisis-preliminar">Análisis preliminar</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb53"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb53-1"><a href="#cb53-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modeltime)</span>
<span id="cb53-2"><a href="#cb53-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(timetk)</span>
<span id="cb53-3"><a href="#cb53-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Casos de campylobacter en Alemania (copia y pega)</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb54"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb54-1"><a href="#cb54-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(readxl)</span>
<span id="cb54-2"><a href="#cb54-2" aria-hidden="true" tabindex="-1"></a>url <span class="ot"><-</span> <span class="st">"https://github.com/epirhandbook/Epi_R_handbook/raw/master/data/time_series/campylobacter_germany.xlsx"</span></span>
<span id="cb54-3"><a href="#cb54-3" aria-hidden="true" tabindex="-1"></a>destfile <span class="ot"><-</span> <span class="st">"campylobacter_germany.xlsx"</span></span>
<span id="cb54-4"><a href="#cb54-4" aria-hidden="true" tabindex="-1"></a>curl<span class="sc">::</span><span class="fu">curl_download</span>(url, destfile)</span>
<span id="cb54-5"><a href="#cb54-5" aria-hidden="true" tabindex="-1"></a>campylobacter_germany <span class="ot"><-</span> <span class="fu">read_excel</span>(destfile)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<p>Veamos la info:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb55"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb55-1"><a href="#cb55-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(campylobacter_germany) <span class="sc">+</span></span>
<span id="cb55-2"><a href="#cb55-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_line</span>(<span class="fu">aes</span>(<span class="at">x =</span> date, <span class="at">y =</span> case)) <span class="sc">+</span></span>
<span id="cb55-3"><a href="#cb55-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme_light</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<p><img src="Regresiones_2_files/figure-html/unnamed-chunk-37-1.png" class="img-fluid" width="672"></p>
</div>
</div>
<p>Otra forma:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb56"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb56-1"><a href="#cb56-1" aria-hidden="true" tabindex="-1"></a>campylobacter_germany <span class="sc">%>%</span></span>
<span id="cb56-2"><a href="#cb56-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_time_series</span>(date, case, <span class="at">.interactive =</span> T)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output-display">
<div id="htmlwidget-23ab54b3f386479f99cf" style="width:100%;height:464px;" class="plotly html-widget"></div>
<script type="application/json" data-for="htmlwidget-23ab54b3f386479f99cf">{"x":{"data":[{"x":["2001-12-31 00:00:00.000000","2002-01-07 00:00:00.000000","2002-01-14 00:00:00.000000","2002-01-21 00:00:00.000000","2002-01-28 00:00:00.000000","2002-02-04 00:00:00.000000","2002-02-11 00:00:00.000000","2002-02-18 00:00:00.000000","2002-02-25 00:00:00.000000","2002-03-04 00:00:00.000000","2002-03-11 00:00:00.000000","2002-03-18 00:00:00.000000","2002-03-25 00:00:00.000000","2002-04-01 00:00:00.000000","2002-04-08 00:00:00.000000","2002-04-15 00:00:00.000000","2002-04-22 00:00:00.000000","2002-04-29 00:00:00.000000","2002-05-06 00:00:00.000000","2002-05-13 00:00:00.000000","2002-05-20 00:00:00.000000","2002-05-27 00:00:00.000000","2002-06-03 00:00:00.000000","2002-06-10 00:00:00.000000","2002-06-17 00:00:00.000000","2002-06-24 00:00:00.000000","2002-07-01 00:00:00.000000","2002-07-08 00:00:00.000000","2002-07-15 00:00:00.000000","2002-07-22 00:00:00.000000","2002-07-29 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00:00:00.000000","2003-03-31 00:00:00.000000","2003-04-07 00:00:00.000000","2003-04-14 00:00:00.000000","2003-04-21 00:00:00.000000","2003-04-28 00:00:00.000000","2003-05-05 00:00:00.000000","2003-05-12 00:00:00.000000","2003-05-19 00:00:00.000000","2003-05-26 00:00:00.000000","2003-06-02 00:00:00.000000","2003-06-09 00:00:00.000000","2003-06-16 00:00:00.000000","2003-06-23 00:00:00.000000","2003-06-30 00:00:00.000000","2003-07-07 00:00:00.000000","2003-07-14 00:00:00.000000","2003-07-21 00:00:00.000000","2003-07-28 00:00:00.000000","2003-08-04 00:00:00.000000","2003-08-11 00:00:00.000000","2003-08-18 00:00:00.000000","2003-08-25 00:00:00.000000","2003-09-01 00:00:00.000000","2003-09-08 00:00:00.000000","2003-09-15 00:00:00.000000","2003-09-22 00:00:00.000000","2003-09-29 00:00:00.000000","2003-10-06 00:00:00.000000","2003-10-13 00:00:00.000000","2003-10-20 00:00:00.000000","2003-10-27 00:00:00.000000","2003-11-03 00:00:00.000000","2003-11-10 00:00:00.000000","2003-11-17 00:00:00.000000","2003-11-24 00:00:00.000000","2003-12-01 00:00:00.000000","2003-12-08 00:00:00.000000","2003-12-15 00:00:00.000000","2003-12-22 00:00:00.000000","2003-12-29 00:00:00.000000","2004-01-05 00:00:00.000000","2004-01-12 00:00:00.000000","2004-01-19 00:00:00.000000","2004-01-26 00:00:00.000000","2004-02-02 00:00:00.000000","2004-02-09 00:00:00.000000","2004-02-16 00:00:00.000000","2004-02-23 00:00:00.000000","2004-03-01 00:00:00.000000","2004-03-08 00:00:00.000000","2004-03-15 00:00:00.000000","2004-03-22 00:00:00.000000","2004-03-29 00:00:00.000000","2004-04-05 00:00:00.000000","2004-04-12 00:00:00.000000","2004-04-19 00:00:00.000000","2004-04-26 00:00:00.000000","2004-05-03 00:00:00.000000","2004-05-10 00:00:00.000000","2004-05-17 00:00:00.000000","2004-05-24 00:00:00.000000","2004-05-31 00:00:00.000000","2004-06-07 00:00:00.000000","2004-06-14 00:00:00.000000","2004-06-21 00:00:00.000000","2004-06-28 00:00:00.000000","2004-07-05 00:00:00.000000","2004-07-12 00:00:00.000000","2004-07-19 00:00:00.000000","2004-07-26 00:00:00.000000","2004-08-02 00:00:00.000000","2004-08-09 00:00:00.000000","2004-08-16 00:00:00.000000","2004-08-23 00:00:00.000000","2004-08-30 00:00:00.000000","2004-09-06 00:00:00.000000","2004-09-13 00:00:00.000000","2004-09-20 00:00:00.000000","2004-09-27 00:00:00.000000","2004-10-04 00:00:00.000000","2004-10-11 00:00:00.000000","2004-10-18 00:00:00.000000","2004-10-25 00:00:00.000000","2004-11-01 00:00:00.000000","2004-11-08 00:00:00.000000","2004-11-15 00:00:00.000000","2004-11-22 00:00:00.000000","2004-11-29 00:00:00.000000","2004-12-06 00:00:00.000000","2004-12-13 00:00:00.000000","2004-12-20 00:00:00.000000","2004-12-27 00:00:00.000000","2005-01-03 00:00:00.000000","2005-01-10 00:00:00.000000","2005-01-17 00:00:00.000000","2005-01-24 00:00:00.000000","2005-01-31 00:00:00.000000","2005-02-07 00:00:00.000000","2005-02-14 00:00:00.000000","2005-02-21 00:00:00.000000","2005-02-28 00:00:00.000000","2005-03-07 00:00:00.000000","2005-03-14 00:00:00.000000","2005-03-21 00:00:00.000000","2005-03-28 00:00:00.000000","2005-04-04 00:00:00.000000","2005-04-11 00:00:00.000000","2005-04-18 00:00:00.000000","2005-04-25 00:00:00.000000","2005-05-02 00:00:00.000000","2005-05-09 00:00:00.000000","2005-05-16 00:00:00.000000","2005-05-23 00:00:00.000000","2005-05-30 00:00:00.000000","2005-06-06 00:00:00.000000","2005-06-13 00:00:00.000000","2005-06-20 00:00:00.000000","2005-06-27 00:00:00.000000","2005-07-04 00:00:00.000000","2005-07-11 00:00:00.000000","2005-07-18 00:00:00.000000","2005-07-25 00:00:00.000000","2005-08-01 00:00:00.000000","2005-08-08 00:00:00.000000","2005-08-15 00:00:00.000000","2005-08-22 00:00:00.000000","2005-08-29 00:00:00.000000","2005-09-05 00:00:00.000000","2005-09-12 00:00:00.000000","2005-09-19 00:00:00.000000","2005-09-26 00:00:00.000000","2005-10-03 00:00:00.000000","2005-10-10 00:00:00.000000","2005-10-17 00:00:00.000000","2005-10-24 00:00:00.000000","2005-10-31 00:00:00.000000","2005-11-07 00:00:00.000000","2005-11-14 00:00:00.000000","2005-11-21 00:00:00.000000","2005-11-28 00:00:00.000000","2005-12-05 00:00:00.000000","2005-12-12 00:00:00.000000","2005-12-19 00:00:00.000000","2005-12-26 00:00:00.000000","2006-01-02 00:00:00.000000","2006-01-09 00:00:00.000000","2006-01-16 00:00:00.000000","2006-01-23 00:00:00.000000","2006-01-30 00:00:00.000000","2006-02-06 00:00:00.000000","2006-02-13 00:00:00.000000","2006-02-20 00:00:00.000000","2006-02-27 00:00:00.000000","2006-03-06 00:00:00.000000","2006-03-13 00:00:00.000000","2006-03-20 00:00:00.000000","2006-03-27 00:00:00.000000","2006-04-03 00:00:00.000000","2006-04-10 00:00:00.000000","2006-04-17 00:00:00.000000","2006-04-24 00:00:00.000000","2006-05-01 00:00:00.000000","2006-05-08 00:00:00.000000","2006-05-15 00:00:00.000000","2006-05-22 00:00:00.000000","2006-05-29 00:00:00.000000","2006-06-05 00:00:00.000000","2006-06-12 00:00:00.000000","2006-06-19 00:00:00.000000","2006-06-26 00:00:00.000000","2006-07-03 00:00:00.000000","2006-07-10 00:00:00.000000","2006-07-17 00:00:00.000000","2006-07-24 00:00:00.000000","2006-07-31 00:00:00.000000","2006-08-07 00:00:00.000000","2006-08-14 00:00:00.000000","2006-08-21 00:00:00.000000","2006-08-28 00:00:00.000000","2006-09-04 00:00:00.000000","2006-09-11 00:00:00.000000","2006-09-18 00:00:00.000000","2006-09-25 00:00:00.000000","2006-10-02 00:00:00.000000","2006-10-09 00:00:00.000000","2006-10-16 00:00:00.000000","2006-10-23 00:00:00.000000","2006-10-30 00:00:00.000000","2006-11-06 00:00:00.000000","2006-11-13 00:00:00.000000","2006-11-20 00:00:00.000000","2006-11-27 00:00:00.000000","2006-12-04 00:00:00.000000","2006-12-11 00:00:00.000000","2006-12-18 00:00:00.000000","2006-12-25 00:00:00.000000","2007-01-01 00:00:00.000000","2007-01-08 00:00:00.000000","2007-01-15 00:00:00.000000","2007-01-22 00:00:00.000000","2007-01-29 00:00:00.000000","2007-02-05 00:00:00.000000","2007-02-12 00:00:00.000000","2007-02-19 00:00:00.000000","2007-02-26 00:00:00.000000","2007-03-05 00:00:00.000000","2007-03-12 00:00:00.000000","2007-03-19 00:00:00.000000","2007-03-26 00:00:00.000000","2007-04-02 00:00:00.000000","2007-04-09 00:00:00.000000","2007-04-16 00:00:00.000000","2007-04-23 00:00:00.000000","2007-04-30 00:00:00.000000","2007-05-07 00:00:00.000000","2007-05-14 00:00:00.000000","2007-05-21 00:00:00.000000","2007-05-28 00:00:00.000000","2007-06-04 00:00:00.000000","2007-06-11 00:00:00.000000","2007-06-18 00:00:00.000000","2007-06-25 00:00:00.000000","2007-07-02 00:00:00.000000","2007-07-09 00:00:00.000000","2007-07-16 00:00:00.000000","2007-07-23 00:00:00.000000","2007-07-30 00:00:00.000000","2007-08-06 00:00:00.000000","2007-08-13 00:00:00.000000","2007-08-20 00:00:00.000000","2007-08-27 00:00:00.000000","2007-09-03 00:00:00.000000","2007-09-10 00:00:00.000000","2007-09-17 00:00:00.000000","2007-09-24 00:00:00.000000","2007-10-01 00:00:00.000000","2007-10-08 00:00:00.000000","2007-10-15 00:00:00.000000","2007-10-22 00:00:00.000000","2007-10-29 00:00:00.000000","2007-11-05 00:00:00.000000","2007-11-12 00:00:00.000000","2007-11-19 00:00:00.000000","2007-11-26 00:00:00.000000","2007-12-03 00:00:00.000000","2007-12-10 00:00:00.000000","2007-12-17 00:00:00.000000","2007-12-24 00:00:00.000000","2007-12-31 00:00:00.000000","2008-01-07 00:00:00.000000","2008-01-14 00:00:00.000000","2008-01-21 00:00:00.000000","2008-01-28 00:00:00.000000","2008-02-04 00:00:00.000000","2008-02-11 00:00:00.000000","2008-02-18 00:00:00.000000","2008-02-25 00:00:00.000000","2008-03-03 00:00:00.000000","2008-03-10 00:00:00.000000","2008-03-17 00:00:00.000000","2008-03-24 00:00:00.000000","2008-03-31 00:00:00.000000","2008-04-07 00:00:00.000000","2008-04-14 00:00:00.000000","2008-04-21 00:00:00.000000","2008-04-28 00:00:00.000000","2008-05-05 00:00:00.000000","2008-05-12 00:00:00.000000","2008-05-19 00:00:00.000000","2008-05-26 00:00:00.000000","2008-06-02 00:00:00.000000","2008-06-09 00:00:00.000000","2008-06-16 00:00:00.000000","2008-06-23 00:00:00.000000","2008-06-30 00:00:00.000000","2008-07-07 00:00:00.000000","2008-07-14 00:00:00.000000","2008-07-21 00:00:00.000000","2008-07-28 00:00:00.000000","2008-08-04 00:00:00.000000","2008-08-11 00:00:00.000000","2008-08-18 00:00:00.000000","2008-08-25 00:00:00.000000","2008-09-01 00:00:00.000000","2008-09-08 00:00:00.000000","2008-09-15 00:00:00.000000","2008-09-22 00:00:00.000000","2008-09-29 00:00:00.000000","2008-10-06 00:00:00.000000","2008-10-13 00:00:00.000000","2008-10-20 00:00:00.000000","2008-10-27 00:00:00.000000","2008-11-03 00:00:00.000000","2008-11-10 00:00:00.000000","2008-11-17 00:00:00.000000","2008-11-24 00:00:00.000000","2008-12-01 00:00:00.000000","2008-12-08 00:00:00.000000","2008-12-15 00:00:00.000000","2008-12-22 00:00:00.000000","2008-12-29 00:00:00.000000","2009-01-05 00:00:00.000000","2009-01-12 00:00:00.000000","2009-01-19 00:00:00.000000","2009-01-26 00:00:00.000000","2009-02-02 00:00:00.000000","2009-02-09 00:00:00.000000","2009-02-16 00:00:00.000000","2009-02-23 00:00:00.000000","2009-03-02 00:00:00.000000","2009-03-09 00:00:00.000000","2009-03-16 00:00:00.000000","2009-03-23 00:00:00.000000","2009-03-30 00:00:00.000000","2009-04-06 00:00:00.000000","2009-04-13 00:00:00.000000","2009-04-20 00:00:00.000000","2009-04-27 00:00:00.000000","2009-05-04 00:00:00.000000","2009-05-11 00:00:00.000000","2009-05-18 00:00:00.000000","2009-05-25 00:00:00.000000","2009-06-01 00:00:00.000000","2009-06-08 00:00:00.000000","2009-06-15 00:00:00.000000","2009-06-22 00:00:00.000000","2009-06-29 00:00:00.000000","2009-07-06 00:00:00.000000","2009-07-13 00:00:00.000000","2009-07-20 00:00:00.000000","2009-07-27 00:00:00.000000","2009-08-03 00:00:00.000000","2009-08-10 00:00:00.000000","2009-08-17 00:00:00.000000","2009-08-24 00:00:00.000000","2009-08-31 00:00:00.000000","2009-09-07 00:00:00.000000","2009-09-14 00:00:00.000000","2009-09-21 00:00:00.000000","2009-09-28 00:00:00.000000","2009-10-05 00:00:00.000000","2009-10-12 00:00:00.000000","2009-10-19 00:00:00.000000","2009-10-26 00:00:00.000000","2009-11-02 00:00:00.000000","2009-11-09 00:00:00.000000","2009-11-16 00:00:00.000000","2009-11-23 00:00:00.000000","2009-11-30 00:00:00.000000","2009-12-07 00:00:00.000000","2009-12-14 00:00:00.000000","2009-12-21 00:00:00.000000","2009-12-28 00:00:00.000000","2010-01-04 00:00:00.000000","2010-01-11 00:00:00.000000","2010-01-18 00:00:00.000000","2010-01-25 00:00:00.000000","2010-02-01 00:00:00.000000","2010-02-08 00:00:00.000000","2010-02-15 00:00:00.000000","2010-02-22 00:00:00.000000","2010-03-01 00:00:00.000000","2010-03-08 00:00:00.000000","2010-03-15 00:00:00.000000","2010-03-22 00:00:00.000000","2010-03-29 00:00:00.000000","2010-04-05 00:00:00.000000","2010-04-12 00:00:00.000000","2010-04-19 00:00:00.000000","2010-04-26 00:00:00.000000","2010-05-03 00:00:00.000000","2010-05-10 00:00:00.000000","2010-05-17 00:00:00.000000","2010-05-24 00:00:00.000000","2010-05-31 00:00:00.000000","2010-06-07 00:00:00.000000","2010-06-14 00:00:00.000000","2010-06-21 00:00:00.000000","2010-06-28 00:00:00.000000","2010-07-05 00:00:00.000000","2010-07-12 00:00:00.000000","2010-07-19 00:00:00.000000","2010-07-26 00:00:00.000000","2010-08-02 00:00:00.000000","2010-08-09 00:00:00.000000","2010-08-16 00:00:00.000000","2010-08-23 00:00:00.000000","2010-08-30 00:00:00.000000","2010-09-06 00:00:00.000000","2010-09-13 00:00:00.000000","2010-09-20 00:00:00.000000","2010-09-27 00:00:00.000000","2010-10-04 00:00:00.000000","2010-10-11 00:00:00.000000","2010-10-18 00:00:00.000000","2010-10-25 00:00:00.000000","2010-11-01 00:00:00.000000","2010-11-08 00:00:00.000000","2010-11-15 00:00:00.000000","2010-11-22 00:00:00.000000","2010-11-29 00:00:00.000000","2010-12-06 00:00:00.000000","2010-12-13 00:00:00.000000","2010-12-20 00:00:00.000000","2010-12-27 00:00:00.000000","2011-01-03 00:00:00.000000","2011-01-10 00:00:00.000000","2011-01-17 00:00:00.000000","2011-01-24 00:00:00.000000","2011-01-31 00:00:00.000000","2011-02-07 00:00:00.000000","2011-02-14 00:00:00.000000","2011-02-21 00:00:00.000000","2011-02-28 00:00:00.000000","2011-03-07 00:00:00.000000","2011-03-14 00:00:00.000000","2011-03-21 00:00:00.000000","2011-03-28 00:00:00.000000","2011-04-04 00:00:00.000000","2011-04-11 00:00:00.000000","2011-04-18 00:00:00.000000","2011-04-25 00:00:00.000000","2011-05-02 00:00:00.000000","2011-05-09 00:00:00.000000","2011-05-16 00:00:00.000000","2011-05-23 00:00:00.000000","2011-05-30 00:00:00.000000","2011-06-06 00:00:00.000000","2011-06-13 00:00:00.000000","2011-06-20 00:00:00.000000","2011-06-27 00:00:00.000000","2011-07-04 00:00:00.000000","2011-07-11 00:00:00.000000","2011-07-18 00:00:00.000000","2011-07-25 00:00:00.000000","2011-08-01 00:00:00.000000","2011-08-08 00:00:00.000000","2011-08-15 00:00:00.000000","2011-08-22 00:00:00.000000","2011-08-29 00:00:00.000000","2011-09-05 00:00:00.000000","2011-09-12 00:00:00.000000","2011-09-19 00:00:00.000000","2011-09-26 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</div>
</div>
<p>Partes de una serie de tiempo clásica:</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb57"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb57-1"><a href="#cb57-1" aria-hidden="true" tabindex="-1"></a>campylobacter_germany <span class="sc">%>%</span> </span>
<span id="cb57-2"><a href="#cb57-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_acf_diagnostics</span>(date, case, <span class="at">.interactive =</span> T, <span class="at">.lags =</span> <span class="dv">52</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>Warning: `gather_()` was deprecated in tidyr 1.2.0.
Please use `gather()` instead.</code></pre>
</div>
<div class="cell-output-display">
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<div class="sourceCode cell-code" id="cb59"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb59-1"><a href="#cb59-1" aria-hidden="true" tabindex="-1"></a>campylobacter_germany <span class="sc">%>%</span> </span>
<span id="cb59-2"><a href="#cb59-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_seasonal_diagnostics</span>(date, case, <span class="at">.interactive =</span> T)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
</div>
<div class="cell">
<div class="sourceCode cell-code" id="cb60"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" aria-hidden="true" tabindex="-1"></a>campylobacter_germany <span class="sc">%>%</span> </span>
<span id="cb60-2"><a href="#cb60-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">plot_stl_diagnostics</span>(date, case, <span class="at">.interactive =</span> F)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>frequency = 13 observations per 1 quarter</code></pre>
</div>
<div class="cell-output cell-output-stderr">
<pre><code>trend = 53 observations per 1 year</code></pre>
</div>
<div class="cell-output-display">
<p><img src="Regresiones_2_files/figure-html/unnamed-chunk-41-1.png" class="img-fluid" width="672"></p>
</div>
<div class="sourceCode cell-code" id="cb63"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb63-1"><a href="#cb63-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Como no se ve completo</span></span>
<span id="cb63-2"><a href="#cb63-2" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="st">"Diags.pdf"</span>, <span class="at">width =</span> <span class="dv">10</span>, <span class="at">height =</span> <span class="dv">25</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
<section id="modelos" class="level2">
<h2 class="anchored" data-anchor-id="modelos">Modelos</h2>
<p>En esta sección haremos <code>arima_reg()</code> y <code>linear_reg()</code>. En teoría el <code>arima</code> debería ser mejor que una regresión lineal.</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb64"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb64-1"><a href="#cb64-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Para decidir el modelo primero obtenemos splits</span></span>
<span id="cb64-2"><a href="#cb64-2" aria-hidden="true" tabindex="-1"></a>splits <span class="ot"><-</span> <span class="fu">initial_time_split</span>(campylobacter_germany, <span class="at">prop =</span> <span class="fl">0.8</span>)</span>
<span id="cb64-3"><a href="#cb64-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb64-4"><a href="#cb64-4" aria-hidden="true" tabindex="-1"></a>entrena <span class="ot"><-</span> <span class="fu">training</span>(splits)</span>
<span id="cb64-5"><a href="#cb64-5" aria-hidden="true" tabindex="-1"></a>testea <span class="ot"><-</span> <span class="fu">testing</span>(splits)</span>
<span id="cb64-6"><a href="#cb64-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb64-7"><a href="#cb64-7" aria-hidden="true" tabindex="-1"></a><span class="co">#Ajuste de un ARIMA</span></span>
<span id="cb64-8"><a href="#cb64-8" aria-hidden="true" tabindex="-1"></a>modelo_ARIMA <span class="ot"><-</span> <span class="fu">arima_reg</span>() <span class="sc">%>%</span></span>
<span id="cb64-9"><a href="#cb64-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="at">engine =</span> <span class="st">"auto_arima"</span>) <span class="sc">%>%</span></span>
<span id="cb64-10"><a href="#cb64-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(case <span class="sc">~</span> date <span class="sc">+</span> <span class="fu">factor</span>(<span class="fu">month</span>(date, <span class="at">label =</span> <span class="cn">TRUE</span>), </span>
<span id="cb64-11"><a href="#cb64-11" aria-hidden="true" tabindex="-1"></a> <span class="at">ordered =</span> <span class="cn">FALSE</span>), <span class="at">data =</span> entrena)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stderr">
<pre><code>frequency = 13 observations per 1 quarter</code></pre>
</div>
<div class="sourceCode cell-code" id="cb66"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb66-1"><a href="#cb66-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Regresión lineal con mes como cofactor</span></span>
<span id="cb66-2"><a href="#cb66-2" aria-hidden="true" tabindex="-1"></a>model_lm <span class="ot"><-</span> <span class="fu">linear_reg</span>() <span class="sc">%>%</span></span>
<span id="cb66-3"><a href="#cb66-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"lm"</span>) <span class="sc">%>%</span></span>
<span id="cb66-4"><a href="#cb66-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">fit</span>(case <span class="sc">~</span> <span class="fu">as.numeric</span>(date) <span class="sc">+</span> </span>
<span id="cb66-5"><a href="#cb66-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">factor</span>(<span class="fu">month</span>(date, <span class="at">label =</span> <span class="cn">TRUE</span>), <span class="at">ordered =</span> <span class="cn">FALSE</span>),</span>
<span id="cb66-6"><a href="#cb66-6" aria-hidden="true" tabindex="-1"></a> <span class="at">data =</span> entrena)</span>
<span id="cb66-7"><a href="#cb66-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-8"><a href="#cb66-8" aria-hidden="true" tabindex="-1"></a><span class="co">#Junto mis modelos</span></span>
<span id="cb66-9"><a href="#cb66-9" aria-hidden="true" tabindex="-1"></a>models_tbl <span class="ot"><-</span> <span class="fu">modeltime_table</span>(</span>
<span id="cb66-10"><a href="#cb66-10" aria-hidden="true" tabindex="-1"></a> modelo_ARIMA, model_lm</span>
<span id="cb66-11"><a href="#cb66-11" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb66-12"><a href="#cb66-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb66-13"><a href="#cb66-13" aria-hidden="true" tabindex="-1"></a><span class="co">#Veo contra el test</span></span>