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<h1>Predict antimicrobial resistance</h1>
<div class="hidden name"><code>resistance_predict.Rd</code></div>
</div>
<div class="ref-description">
<p>Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns <code>se_min</code> and <code>se_max</code>. See Examples for a real live example.</p>
</div>
<pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
<span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>, <span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>tbl</th>
<td><p>a <code>data.frame</code> containing isolates.</p></td>
</tr>
<tr>
<th>col_ab</th>
<td><p>column name of <code>tbl</code> with antimicrobial interpretations (<code>R</code>, <code>I</code> and <code>S</code>)</p></td>
</tr>
<tr>
<th>col_date</th>
<td><p>column name of the date, will be used to calculate years if this column doesn't consist of years already</p></td>
</tr>
<tr>
<th>year_min</th>
<td><p>lowest year to use in the prediction model, dafaults the lowest year in <code>col_date</code></p></td>
</tr>
<tr>
<th>year_max</th>
<td><p>highest year to use in the prediction model, defaults to 15 years after today</p></td>
</tr>
<tr>
<th>year_every</th>
<td><p>unit of sequence between lowest year found in the data and <code>year_max</code></p></td>
</tr>
<tr>
<th>minimum</th>
<td><p>minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.</p></td>
</tr>
<tr>
<th>model</th>
<td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
</tr>
<tr>
<th>I_as_R</th>
<td><p>treat <code>I</code> as <code>R</code></p></td>
</tr>
<tr>
<th>preserve_measurements</th>
<td><p>logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be <code>NA</code>.</p></td>
</tr>
<tr>
<th>info</th>
<td><p>print textual analysis with the name and <code><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></code> of the model.</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p><code>data.frame</code> with columns:</p><ul>
<li><p><code>year</code></p></li>
<li><p><code>value</code>, the same as <code>estimated</code> when <code>preserve_measurements = FALSE</code>, and a combination of <code>observed</code> and <code>estimated</code> otherwise</p></li>
<li><p><code>se_min</code>, the lower bound of the standard error with a minimum of <code>0</code></p></li>
<li><p><code>se_max</code> the upper bound of the standard error with a maximum of <code>1</code></p></li>
<li><p><code>observations</code>, the total number of observations, i.e. S + I + R</p></li>
<li><p><code>observed</code>, the original observed values</p></li>
<li><p><code>estimated</code>, the estimated values, calculated by the model</p></li>
</ul>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>The <code><a href='portion.html'>portion</a></code> function to calculate resistance, <br /> <code><a href='https://www.rdocumentation.org/packages/stats/topics/lm'>lm</a></code> <code><a href='https://www.rdocumentation.org/packages/stats/topics/glm'>glm</a></code></p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># NOT RUN {</span>
<span class='co'># use it with base R:</span>
<span class='fu'>resistance_predict</span>(<span class='kw'>tbl</span> <span class='kw'>=</span> <span class='no'>tbl</span>[<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/which'>which</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span> <span class='kw'>&amp;</span> <span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>),],
<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amcl"</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>)
<span class='co'># or use dplyr so you can actually read it:</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>tbl</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='http://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>,
<span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>) <span class='kw'>%&gt;%</span>
<span class='fu'>resistance_predict</span>(<span class='no'>amcl</span>, <span class='no'>date</span>)
<span class='co'># }</span><div class='input'>
<span class='co'># real live example:</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
<span class='co'># get bacteria properties like genus and species</span>
<span class='fu'><a href='join.html'>left_join_microorganisms</a></span>(<span class='st'>"mo"</span>) <span class='kw'>%&gt;%</span>
<span class='co'># calculate first isolates</span>
<span class='fu'><a href='http://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>first_isolate</span> <span class='kw'>=</span> <span class='fu'><a href='first_isolate.html'>first_isolate</a></span>(<span class='no'>.</span>)) <span class='kw'>%&gt;%</span>
<span class='co'># filter on first E. coli isolates</span>
<span class='fu'><a href='http://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Escherichia"</span>,
<span class='no'>species</span> <span class='kw'>==</span> <span class='st'>"coli"</span>,
<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>) <span class='kw'>%&gt;%</span>
<span class='co'># predict resistance of cefotaxime for next years</span>
<span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"cfot"</span>,
<span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='fl'>2025</span>,
<span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
<span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>0</span>)</div><div class='output co'>#&gt; <span class='message'><span style='color: #0000BB;'>NOTE: Using column `</span><span style='color: #0000BB;font-weight: bold;'>mo</span><span style='color: #0000BB;'>` as input for `col_mo`.</span><span></span></div><div class='output co'>#&gt; <span class='message'></span><span style='color: #0000BB;'>NOTE: Using column `</span><span style='color: #0000BB;font-weight: bold;'>date</span><span style='color: #0000BB;'>` as input for `col_date`.</span><span></span></div><div class='output co'>#&gt; <span class='message'></span><span style='color: #0000BB;'>NOTE: Using column `</span><span style='color: #0000BB;font-weight: bold;'>patient_id</span><span style='color: #0000BB;'>` as input for `col_patient_id`.</span><span></span></div><div class='output co'>#&gt; <span class='message'>=&gt; Found </span><span style='font-weight: bold;'>1,315 first isolates</span><span> (65.8% of total)</span></div><div class='output co'>#&gt;
#&gt; Logistic regression model (logit) with binomial distribution
#&gt; ------------------------------------------------------------
#&gt;
#&gt; Call:
#&gt; glm(formula = cbind(R, S) ~ year, family = binomial)
#&gt;
#&gt; Deviance Residuals:
#&gt; Min 1Q Median 3Q Max
#&gt; -1.0796 -0.4714 -0.2849 -0.1534 1.5711
#&gt;
#&gt; Coefficients:
#&gt; Estimate Std. Error z value Pr(&gt;|z|)
#&gt; (Intercept) -687.3844 340.9570 -2.016 0.0438 *
#&gt; year 0.3396 0.1692 2.007 0.0448 *
#&gt; ---
#&gt; Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#&gt;
#&gt; (Dispersion parameter for binomial family taken to be 1)
#&gt;
#&gt; Null deviance: 13.8352 on 15 degrees of freedom
#&gt; Residual deviance: 7.0501 on 14 degrees of freedom
#&gt; AIC: 19.351
#&gt;
#&gt; Number of Fisher Scoring iterations: 6
#&gt; </div><div class='output co'>#&gt; year value se_min se_max observations observed estimated
#&gt; 1 2002 0.00000000 NA NA 12 0.00000000 0.0005290038
#&gt; 2 2003 0.00000000 NA NA 13 0.00000000 0.0007427521
#&gt; 3 2004 0.00000000 NA NA 12 0.00000000 0.0010427770
#&gt; 4 2005 0.00000000 NA NA 15 0.00000000 0.0014638156
#&gt; 5 2006 0.00000000 NA NA 16 0.00000000 0.0020545058
#&gt; 6 2007 0.00000000 NA NA 17 0.00000000 0.0028828678
#&gt; 7 2008 0.00000000 NA NA 17 0.00000000 0.0040438659
#&gt; 8 2009 0.00000000 NA NA 18 0.00000000 0.0056697665
#&gt; 9 2010 0.00000000 NA NA 13 0.00000000 0.0079441719
#&gt; 10 2011 0.04761905 NA NA 21 0.04761905 0.0111207424
#&gt; 11 2012 0.00000000 NA NA 10 0.00000000 0.0155475955
#&gt; 12 2013 0.00000000 NA NA 13 0.00000000 0.0216979872
#&gt; 13 2014 0.00000000 NA NA 19 0.00000000 0.0302067265
#&gt; 14 2015 0.15384615 NA NA 13 0.15384615 0.0419091804
#&gt; 15 2016 0.04761905 NA NA 21 0.04761905 0.0578747498
#&gt; 16 2017 0.05000000 NA NA 20 0.05000000 0.0794183382
#&gt; 17 2018 0.10806159 0.03882360 0.1772996 NA NA 0.1080615861
#&gt; 18 2019 0.14540370 0.03910717 0.2517002 NA NA 0.1454037037
#&gt; 19 2020 0.19285970 0.03682518 0.3488942 NA NA 0.1928597004
#&gt; 20 2021 0.25125053 0.03388540 0.4686157 NA NA 0.2512505261
#&gt; 21 2022 0.32030440 0.03456588 0.6060429 NA NA 0.3203043985
#&gt; 22 2023 0.39824269 0.04543193 0.7510534 NA NA 0.3982426891
#&gt; 23 2024 0.48170517 0.07378334 0.8896270 NA NA 0.4817051695
#&gt; 24 2025 0.56620119 0.12484502 1.0000000 NA NA 0.5662011910</div><div class='input'>
<span class='co'># create nice plots with ggplot</span>
<span class='kw'>if</span> (!<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>require</a></span>(<span class='no'>ggplot2</span>)) {
<span class='no'>data</span> <span class='kw'>&lt;-</span> <span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='http://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>mo</span> <span class='kw'>==</span> <span class='fu'><a href='as.mo.html'>as.mo</a></span>(<span class='st'>"E. coli"</span>)) <span class='kw'>%&gt;%</span>
<span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amox"</span>,
<span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>15</span>)
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span>(<span class='no'>data</span>,
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>x</span> <span class='kw'>=</span> <span class='no'>year</span>)) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/geom_bar.html'>geom_col</a></span>(<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>value</span>),
<span class='kw'>fill</span> <span class='kw'>=</span> <span class='st'>"grey75"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/geom_linerange.html'>geom_errorbar</a></span>(<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>ymin</span> <span class='kw'>=</span> <span class='no'>se_min</span>,
<span class='kw'>ymax</span> <span class='kw'>=</span> <span class='no'>se_max</span>),
<span class='kw'>colour</span> <span class='kw'>=</span> <span class='st'>"grey50"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/scale_continuous.html'>scale_y_continuous</a></span>(<span class='kw'>limits</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>),
<span class='kw'>breaks</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span>),
<span class='kw'>labels</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste0</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>, <span class='fl'>10</span>), <span class='st'>"%"</span>)) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/labs.html'>labs</a></span>(<span class='kw'>title</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/expression'>expression</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Forecast of amoxicillin resistance in "</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/grDevices/topics/plotmath'>italic</a></span>(<span class='st'>"E. coli"</span>))),
<span class='kw'>y</span> <span class='kw'>=</span> <span class='st'>"%IR"</span>,
<span class='kw'>x</span> <span class='kw'>=</span> <span class='st'>"Year"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggtheme.html'>theme_minimal</a></span>(<span class='kw'>base_size</span> <span class='kw'>=</span> <span class='fl'>13</span>)
}</div></span></pre>
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<li><a href="#arguments">Arguments</a></li>
<li><a href="#value">Value</a></li>
<li><a href="#see-also">See also</a></li>
<li><a href="#examples">Examples</a></li>
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