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<h1>How to predict antimicrobial resistance</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">18 February 2019</h4>
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<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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<div id="needed-r-packages" class="section level2">
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<h2 class="hasAnchor">
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<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
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<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by <a href="https://www.linkedin.com/in/hadleywickham/">Dr Hadley Wickham</a>. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
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<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
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<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
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<a class="sourceLine" id="cb1-2" title="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
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<a class="sourceLine" id="cb1-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a>
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<a class="sourceLine" id="cb1-4" title="4"></a>
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<a class="sourceLine" id="cb1-5" title="5"><span class="co"># (if not yet installed, install with:)</span></a>
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<a class="sourceLine" id="cb1-6" title="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
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<div id="prediction-analysis" class="section level2">
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<h2 class="hasAnchor">
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<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
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<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
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<p>It is basically as easy as:</p>
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<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="co"># resistance prediction of piperacillin/tazobactam (pita):</span></a>
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<a class="sourceLine" id="cb2-2" title="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
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<a class="sourceLine" id="cb2-3" title="3"></a>
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<a class="sourceLine" id="cb2-4" title="4"><span class="co"># or:</span></a>
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<a class="sourceLine" id="cb2-5" title="5">septic_patients <span class="op">%>%</span><span class="st"> </span></a>
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<a class="sourceLine" id="cb2-6" title="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
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<a class="sourceLine" id="cb2-7" title="7"></a>
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<a class="sourceLine" id="cb2-8" title="8"><span class="co"># to bind it to object 'predict_pita' for example:</span></a>
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<a class="sourceLine" id="cb2-9" title="9">predict_pita <-<span class="st"> </span>septic_patients <span class="op">%>%</span><span class="st"> </span></a>
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<a class="sourceLine" id="cb2-10" title="10"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a></code></pre></div>
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<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
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<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
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<pre><code>#> NOTE: Using column `date` as input for `col_date`.
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#>
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#> Logistic regression model (logit) with binomial distribution
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#> ------------------------------------------------------------
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#>
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#> Call:
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#> glm(formula = df_matrix ~ year, family = binomial)
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#>
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#> Deviance Residuals:
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#> Min 1Q Median 3Q Max
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#> -2.9224 -1.3120 0.0170 0.7586 3.1932
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#>
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#> Coefficients:
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#> Estimate Std. Error z value Pr(>|z|)
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#> (Intercept) -222.92857 45.93922 -4.853 1.22e-06 ***
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#> year 0.10994 0.02284 4.814 1.48e-06 ***
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#> ---
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#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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#>
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#> (Dispersion parameter for binomial family taken to be 1)
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#>
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#> Null deviance: 59.794 on 14 degrees of freedom
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#> Residual deviance: 35.191 on 13 degrees of freedom
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#> AIC: 93.464
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#>
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#> Number of Fisher Scoring iterations: 4</code></pre>
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<p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
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<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">predict_pita</a>
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<a class="sourceLine" id="cb4-2" title="2"><span class="co">#> year value se_min se_max observations observed estimated</span></a>
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<a class="sourceLine" id="cb4-3" title="3"><span class="co">#> 1 2003 0.06250000 NA NA 32 0.06250000 0.06177594</span></a>
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<a class="sourceLine" id="cb4-4" title="4"><span class="co">#> 2 2004 0.08536585 NA NA 82 0.08536585 0.06846343</span></a>
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<a class="sourceLine" id="cb4-5" title="5"><span class="co">#> 3 2005 0.10000000 NA NA 60 0.10000000 0.07581637</span></a>
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<a class="sourceLine" id="cb4-6" title="6"><span class="co">#> 4 2006 0.05084746 NA NA 59 0.05084746 0.08388789</span></a>
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<a class="sourceLine" id="cb4-7" title="7"><span class="co">#> 5 2007 0.12121212 NA NA 66 0.12121212 0.09273250</span></a>
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<a class="sourceLine" id="cb4-8" title="8"><span class="co">#> 6 2008 0.04166667 NA NA 72 0.04166667 0.10240539</span></a>
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<a class="sourceLine" id="cb4-9" title="9"><span class="co">#> 7 2009 0.01639344 NA NA 61 0.01639344 0.11296163</span></a>
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<a class="sourceLine" id="cb4-10" title="10"><span class="co">#> 8 2010 0.09433962 NA NA 53 0.09433962 0.12445516</span></a>
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<a class="sourceLine" id="cb4-11" title="11"><span class="co">#> 9 2011 0.18279570 NA NA 93 0.18279570 0.13693759</span></a>
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<a class="sourceLine" id="cb4-12" title="12"><span class="co">#> 10 2012 0.30769231 NA NA 65 0.30769231 0.15045682</span></a>
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<a class="sourceLine" id="cb4-13" title="13"><span class="co">#> 11 2013 0.08620690 NA NA 58 0.08620690 0.16505550</span></a>
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<a class="sourceLine" id="cb4-14" title="14"><span class="co">#> 12 2014 0.15254237 NA NA 59 0.15254237 0.18076926</span></a>
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<a class="sourceLine" id="cb4-15" title="15"><span class="co">#> 13 2015 0.27272727 NA NA 55 0.27272727 0.19762493</span></a>
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<a class="sourceLine" id="cb4-16" title="16"><span class="co">#> 14 2016 0.25000000 NA NA 84 0.25000000 0.21563859</span></a>
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<a class="sourceLine" id="cb4-17" title="17"><span class="co">#> 15 2017 0.16279070 NA NA 86 0.16279070 0.23481370</span></a>
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<a class="sourceLine" id="cb4-18" title="18"><span class="co">#> 16 2018 0.25513926 0.2228376 0.2874409 NA NA 0.25513926</span></a>
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<a class="sourceLine" id="cb4-19" title="19"><span class="co">#> 17 2019 0.27658825 0.2386811 0.3144954 NA NA 0.27658825</span></a>
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||
<a class="sourceLine" id="cb4-20" title="20"><span class="co">#> 18 2020 0.29911630 0.2551715 0.3430611 NA NA 0.29911630</span></a>
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||
<a class="sourceLine" id="cb4-21" title="21"><span class="co">#> 19 2021 0.32266085 0.2723340 0.3729877 NA NA 0.32266085</span></a>
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<a class="sourceLine" id="cb4-22" title="22"><span class="co">#> 20 2022 0.34714076 0.2901847 0.4040968 NA NA 0.34714076</span></a>
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<a class="sourceLine" id="cb4-23" title="23"><span class="co">#> 21 2023 0.37245666 0.3087318 0.4361815 NA NA 0.37245666</span></a>
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<a class="sourceLine" id="cb4-24" title="24"><span class="co">#> 22 2024 0.39849187 0.3279750 0.4690088 NA NA 0.39849187</span></a>
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<a class="sourceLine" id="cb4-25" title="25"><span class="co">#> 23 2025 0.42511415 0.3479042 0.5023241 NA NA 0.42511415</span></a>
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||
<a class="sourceLine" id="cb4-26" title="26"><span class="co">#> 24 2026 0.45217796 0.3684992 0.5358568 NA NA 0.45217796</span></a>
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||
<a class="sourceLine" id="cb4-27" title="27"><span class="co">#> 25 2027 0.47952757 0.3897276 0.5693275 NA NA 0.47952757</span></a>
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||
<a class="sourceLine" id="cb4-28" title="28"><span class="co">#> 26 2028 0.50700045 0.4115444 0.6024565 NA NA 0.50700045</span></a>
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<a class="sourceLine" id="cb4-29" title="29"><span class="co">#> 27 2029 0.53443111 0.4338908 0.6349714 NA NA 0.53443111</span></a></code></pre></div>
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<p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
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||
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_pita)</a></code></pre></div>
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||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
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||
<p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
|
||
<p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
|
||
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita)</a></code></pre></div>
|
||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
|
||
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"></a>
|
||
<a class="sourceLine" id="cb7-2" title="2"><span class="co"># choose for error bars instead of a ribbon</span></a>
|
||
<a class="sourceLine" id="cb7-3" title="3"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita, <span class="dt">ribbon =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
|
||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
|
||
<div id="choosing-the-right-model" class="section level3">
|
||
<h3 class="hasAnchor">
|
||
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
|
||
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p>
|
||
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||
<a class="sourceLine" id="cb8-2" title="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%>%</span></a>
|
||
<a class="sourceLine" id="cb8-3" title="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="op">%>%</span><span class="st"> </span></a>
|
||
<a class="sourceLine" id="cb8-4" title="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a>
|
||
<a class="sourceLine" id="cb8-5" title="5"><span class="co">#> </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
|
||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
|
||
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
|
||
<p>You can define the model with the <code>model</code> parameter. The default model is a generalised linear regression model using a binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance.</p>
|
||
<p>Valid values are:</p>
|
||
<table class="table">
|
||
<colgroup>
|
||
<col width="32%">
|
||
<col width="25%">
|
||
<col width="42%">
|
||
</colgroup>
|
||
<thead><tr class="header">
|
||
<th>Input values</th>
|
||
<th>Function used by R</th>
|
||
<th>Type of model</th>
|
||
</tr></thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td>
|
||
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
|
||
</td>
|
||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = binomial)</a></code></td>
|
||
<td>Generalised linear model with binomial distribution</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td>
|
||
<code>"loglin"</code> or <code>"poisson"</code>
|
||
</td>
|
||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = poisson)</a></code></td>
|
||
<td>Generalised linear model with poisson distribution</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td>
|
||
<code>"lin"</code> or <code>"linear"</code>
|
||
</td>
|
||
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/lm">lm()</a></code></td>
|
||
<td>Linear model</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">septic_patients <span class="op">%>%</span></a>
|
||
<a class="sourceLine" id="cb9-2" title="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%>%</span></a>
|
||
<a class="sourceLine" id="cb9-3" title="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"linear"</span>) <span class="op">%>%</span><span class="st"> </span></a>
|
||
<a class="sourceLine" id="cb9-4" title="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a>
|
||
<a class="sourceLine" id="cb9-5" title="5"><span class="co">#> </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
|
||
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||
<p>This seems more likely, doesn’t it?</p>
|
||
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
|
||
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">model <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/attributes">attributes</a></span>(predict_pita)<span class="op">$</span>model</a>
|
||
<a class="sourceLine" id="cb10-2" title="2"></a>
|
||
<a class="sourceLine" id="cb10-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>family</a>
|
||
<a class="sourceLine" id="cb10-4" title="4"><span class="co">#> </span></a>
|
||
<a class="sourceLine" id="cb10-5" title="5"><span class="co">#> Family: binomial </span></a>
|
||
<a class="sourceLine" id="cb10-6" title="6"><span class="co">#> Link function: logit</span></a>
|
||
<a class="sourceLine" id="cb10-7" title="7"></a>
|
||
<a class="sourceLine" id="cb10-8" title="8"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>coefficients</a>
|
||
<a class="sourceLine" id="cb10-9" title="9"><span class="co">#> Estimate Std. Error z value Pr(>|z|)</span></a>
|
||
<a class="sourceLine" id="cb10-10" title="10"><span class="co">#> (Intercept) -222.9285736 45.93922388 -4.852685 1.218012e-06</span></a>
|
||
<a class="sourceLine" id="cb10-11" title="11"><span class="co">#> year 0.1099391 0.02283501 4.814500 1.475690e-06</span></a></code></pre></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
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<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
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<div id="tocnav">
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<h2 class="hasAnchor">
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<a href="#tocnav" class="anchor"></a>Contents</h2>
|
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<ul class="nav nav-pills nav-stacked">
|
||
<li><a href="#needed-r-packages">Needed R packages</a></li>
|
||
<li><a href="#prediction-analysis">Prediction analysis</a></li>
|
||
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<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>.</p>
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