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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9033</small>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.2.9034</small>
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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@ -171,8 +171,13 @@
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<div class="section level2">
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<h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
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</h2>
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<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></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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<p>As with many uses in R, we need some additional packages for AMR data
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analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a>. The
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tidyverse tremendously improves the way we conduct data science - it
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allows for a very natural way of writing syntaxes and creating beautiful
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plots in R.</p>
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<p>Our <code>AMR</code> package depends on these packages and even
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extends their use and functions.</p>
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<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
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<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
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<div class="section level2">
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<h2 id="prediction-analysis">Prediction analysis<a class="anchor" aria-label="anchor" href="#prediction-analysis"></a>
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</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 data 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>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>,
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which takes the same input as functions for <a href="./AMR.html">other
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AMR data analysis</a>. Based on a date column, it calculates cases per
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year and uses a regression model to predict antimicrobial
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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="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></span>
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<span> col_ab <span class="op">=</span> <span class="st">"TZP"</span>,</span>
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<span> model <span class="op">=</span> <span class="st">"binomial"</span></span>
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<span> <span class="op">)</span></span></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>resistance_predict(..., info = FALSE)</code>.</p>
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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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<p>The function will look for a date column itself if
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<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
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will be printed unless using
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<code>resistance_predict(..., info = FALSE)</code>.</p>
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<p>This text is only a printed summary - the actual result (output) of
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the function is a <code>data.frame</code> containing for each year: the
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number of observations, the actual observed resistance, the estimated
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resistance and the standard error below and above the estimation:</p>
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<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="va">predict_TZP</span></span>
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<span><span class="co"># <span style="color: #949494;"># A tibble: 31 × 7</span></span></span>
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<span><span class="co"># <span style="color: #BCBCBC;"> 9</span> <span style="text-decoration: underline;">2</span>010 0.056<span style="text-decoration: underline;">6</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 53 0.056<span style="text-decoration: underline;">6</span> 0.116 </span></span>
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<span><span class="co"># <span style="color: #BCBCBC;">10</span> <span style="text-decoration: underline;">2</span>011 0.183 <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> 93 0.183 0.127 </span></span>
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<span><span class="co"># <span style="color: #949494;"># … with 21 more rows</span></span></span></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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<p>The function <code>plot</code> is available in base R, and can be
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extended by other packages to depend the output based on the type of
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input. We extended its function to cope with resistance predictions:</p>
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<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></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>
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<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>
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<p>This is the fastest way to plot the result. It automatically adds the
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right axes, error bars, titles, number of available observations and
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type of model.</p>
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<p>We also support the <code>ggplot2</code> package with our custom
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function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing
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plots:</p>
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<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
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<div class="section level3">
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<h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
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</h3>
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<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
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<p>Resistance is not easily predicted; if we look at vancomycin
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resistance in Gram-positive bacteria, the spread (i.e. standard error)
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is enormous:</p>
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<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
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<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
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<span> <span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></span>
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<span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></span></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
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<p>Vancomycin resistance could be 100% in ten years, but might remain very low.</p>
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<p>You can define the model with the <code>model</code> parameter. The model chosen above 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>
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<p>Vancomycin resistance could be 100% in ten years, but might remain
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very low.</p>
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<p>You can define the model with the <code>model</code> parameter. The
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model chosen above is a generalised linear regression model using a
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binomial distribution, assuming that a period of zero resistance was
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followed by a period of increasing resistance leading slowly to more and
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more resistance.</p>
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<p>Valid values are:</p>
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<table class="table">
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<colgroup>
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@ -264,7 +292,8 @@
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<tbody>
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<tr class="odd">
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<td>
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<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
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<code>"binomial"</code> or <code>"binom"</code> or
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<code>"logit"</code>
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</td>
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<td><code>glm(..., family = binomial)</code></td>
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<td>Generalised linear model with binomial distribution</td>
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@ -285,7 +314,8 @@
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</tr>
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</tbody>
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</table>
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<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate:</p>
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<p>For the vancomycin resistance in Gram-positive bacteria, a linear
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model might be more appropriate:</p>
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<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
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<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
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@ -293,7 +323,8 @@
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<span> <span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></span>
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<span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></span></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
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<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
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<p>The model itself is also available from the object, as an
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<code>attribute</code>:</p>
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<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span></span>
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<span></span>
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