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(v1.4.0.9044) mo tibble printing, mo_shortname() fix

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dr. M.S. (Matthijs) Berends
2020-12-24 23:29:10 +01:00
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<title>How to apply EUCAST rules • AMR (for R)</title>
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@ -56,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9042</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9044</span>
</span>
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@ -419,7 +402,7 @@
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<title>How to determine multi-drug resistance (MDR) • AMR (for R)</title>
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@ -56,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9042</span>
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@ -268,7 +251,7 @@
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span>
<span class="co"># Warning in warning_("NA introduced for isolates where the available percentage of antimicrobial classes was below ", : NA introduced for isolates where the available percentage of antimicrobial</span>
<span class="co"># Warning: NA introduced for isolates where the available percentage of antimicrobial</span>
<span class="co"># classes was below 50% (set with `pct_required_classes`)</span></code></pre></div>
<p><strong>Frequency table</strong></p>
<p>Class: factor &gt; ordered (numeric)<br>
@ -306,48 +289,41 @@ Unique: 2</p>
</table>
<p>For another example, I will create a data set to determine multi-drug resistant TB:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="co"># a helper function to get a random vector with values S, I and R</span>
<span class="co"># with the probabilities 50% - 10% - 40%</span>
<span class="va">sample_rsi</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="op">)</span> <span class="op">{</span>
<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"S"</span>, <span class="st">"I"</span>, <span class="st">"R"</span><span class="op">)</span>,
size <span class="op">=</span> <span class="fl">5000</span>,
prob <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.1</span>, <span class="fl">0.4</span><span class="op">)</span>,
replace <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>
<span class="op">}</span>
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
isoniazid <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
gatifloxacin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
ethambutol <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
pyrazinamide <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<code class="sourceCode R"><span class="co"># random_rsi() is a helper function to generate</span>
<span class="co"># a random vector with values S, I and R</span>
<span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:</p>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
GAT <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
ETH <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
PZA <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
MFX <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span>,
KAN <span class="op">=</span> <span class="fu">sample_rsi</span><span class="op">(</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<code class="sourceCode R"><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span>RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
PZA <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
MFX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,
KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_rsi</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span><span class="op">)</span></code></pre></div>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span>
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 S R S R R S</span>
<span class="co"># 2 S R S S S S</span>
<span class="co"># 3 S S S S R S</span>
<span class="co"># 4 S R S R S S</span>
<span class="co"># 5 R S R S R S</span>
<span class="co"># 6 S R R R S S</span>
<span class="co"># 1 S S R I R S</span>
<span class="co"># 2 I S R I I R</span>
<span class="co"># 3 R R R S I R</span>
<span class="co"># 4 I I R R S S</span>
<span class="co"># 5 I R I S S R</span>
<span class="co"># 6 I R I I S I</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 S</span>
<span class="co"># 1 R</span>
<span class="co"># 2 S</span>
<span class="co"># 3 S</span>
<span class="co"># 4 S</span>
<span class="co"># 5 R</span>
<span class="co"># 6 S</span></code></pre></div>
<span class="co"># 3 I</span>
<span class="co"># 4 I</span>
<span class="co"># 5 I</span>
<span class="co"># 6 R</span></code></pre></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></code></pre></div>
@ -378,40 +354,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3314</td>
<td align="right">66.28%</td>
<td align="right">3314</td>
<td align="right">66.28%</td>
<td align="right">3214</td>
<td align="right">64.28%</td>
<td align="right">3214</td>
<td align="right">64.28%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">637</td>
<td align="right">12.74%</td>
<td align="right">3951</td>
<td align="right">79.02%</td>
<td align="right">997</td>
<td align="right">19.94%</td>
<td align="right">4211</td>
<td align="right">84.22%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">565</td>
<td align="right">11.30%</td>
<td align="right">4516</td>
<td align="right">90.32%</td>
<td align="right">442</td>
<td align="right">8.84%</td>
<td align="right">4653</td>
<td align="right">93.06%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Poly-resistant</td>
<td align="right">298</td>
<td align="right">5.96%</td>
<td align="right">4814</td>
<td align="right">96.28%</td>
<td align="right">239</td>
<td align="right">4.78%</td>
<td align="right">4892</td>
<td align="right">97.84%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">186</td>
<td align="right">3.72%</td>
<td align="right">108</td>
<td align="right">2.16%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>
@ -433,7 +409,7 @@ Unique: 5</p>
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@ -210,7 +193,7 @@
<h1 data-toc-skip>How to import data from SPSS / SAS / Stata</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">21 December 2020</h4>
<h4 class="date">24 December 2020</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/SPSS.Rmd"><code>vignettes/SPSS.Rmd</code></a></small>
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@ -56,7 +39,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9042</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9044</span>
</span>
</div>
@ -244,20 +227,20 @@
times <span class="op">=</span> <span class="fl">10</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">S.aureus</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 10.0 11.0 20.0 13.0 38.0 41.0</span>
<span class="co"># as.mo("stau") 100.0 110.0 120.0 110.0 140.0 140.0</span>
<span class="co"># as.mo("STAU") 100.0 110.0 130.0 140.0 140.0 160.0</span>
<span class="co"># as.mo("staaur") 10.0 12.0 23.0 14.0 38.0 58.0</span>
<span class="co"># as.mo("STAAUR") 10.0 11.0 18.0 13.0 14.0 42.0</span>
<span class="co"># as.mo("S. aureus") 26.0 30.0 46.0 44.0 58.0 78.0</span>
<span class="co"># as.mo("S aureus") 26.0 27.0 32.0 31.0 31.0 56.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 2.0 2.4 2.6 2.7 2.8 3.1</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 890.0 910.0 920.0 920.0 930.0 960.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 380.0 390.0 400.0 390.0 400.0 430.0</span>
<span class="co"># as.mo("MRSA") 9.9 11.0 16.0 14.0 14.0 41.0</span>
<span class="co"># as.mo("VISA") 17.0 19.0 28.0 21.0 45.0 52.0</span>
<span class="co"># as.mo("VRSA") 17.0 19.0 26.0 21.0 22.0 53.0</span>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("sau") 9.9 12.0 15.0 13.0 13.0 42.0</span>
<span class="co"># as.mo("stau") 100.0 100.0 130.0 120.0 140.0 190.0</span>
<span class="co"># as.mo("STAU") 110.0 110.0 130.0 130.0 140.0 150.0</span>
<span class="co"># as.mo("staaur") 9.8 12.0 26.0 13.0 41.0 60.0</span>
<span class="co"># as.mo("STAAUR") 11.0 11.0 15.0 13.0 13.0 41.0</span>
<span class="co"># as.mo("S. aureus") 26.0 28.0 50.0 58.0 63.0 75.0</span>
<span class="co"># as.mo("S aureus") 26.0 27.0 46.0 51.0 60.0 67.0</span>
<span class="co"># as.mo("Staphylococcus aureus") 1.9 2.2 2.5 2.5 2.8 3.1</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 870.0 880.0 1100.0 910.0 1000.0 2400.0</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 360.0 370.0 400.0 380.0 420.0 540.0</span>
<span class="co"># as.mo("MRSA") 9.7 11.0 17.0 12.0 13.0 66.0</span>
<span class="co"># as.mo("VISA") 19.0 19.0 36.0 35.0 53.0 56.0</span>
<span class="co"># as.mo("VRSA") 17.0 20.0 20.0 21.0 21.0 23.0</span>
<span class="co"># neval</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
@ -301,8 +284,8 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># mo_name(x) 128 167 205 195 232 298 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.195 seconds. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 134 167 201 179 207 310 10</span></code></pre></div>
<p>So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.179 seconds. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
@ -316,10 +299,10 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 7.35 7.70 8.15 8.11 8.68 8.96 10</span>
<span class="co"># B 22.50 22.80 28.10 24.30 25.70 62.60 10</span>
<span class="co"># C 1.84 1.98 2.13 2.13 2.17 2.43 10</span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0021 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<span class="co"># A 7.59 7.93 8.24 8.09 8.66 9.06 10</span>
<span class="co"># B 23.60 23.70 29.20 24.30 27.10 66.10 10</span>
<span class="co"># C 1.84 2.13 2.22 2.19 2.35 2.62 10</span></code></pre></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0022 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span class="va">run_it</span> <span class="op">&lt;-</span> <span class="fu">microbenchmark</span><span class="op">(</span>A <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span><span class="op">(</span><span class="st">"aureus"</span><span class="op">)</span>,
B <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="st">"Staphylococcus"</span><span class="op">)</span>,
@ -333,14 +316,14 @@
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 1.60 1.63 1.74 1.70 1.83 2.02 10</span>
<span class="co"># B 1.37 1.40 1.62 1.54 1.74 2.07 10</span>
<span class="co"># C 1.39 1.45 1.63 1.65 1.77 1.86 10</span>
<span class="co"># D 1.37 1.40 1.58 1.51 1.62 2.27 10</span>
<span class="co"># E 1.44 1.64 1.70 1.66 1.81 2.13 10</span>
<span class="co"># F 1.38 1.42 1.57 1.58 1.67 1.81 10</span>
<span class="co"># G 1.38 1.62 1.64 1.63 1.70 1.82 10</span>
<span class="co"># H 1.44 1.65 1.84 1.82 1.99 2.60 10</span></code></pre></div>
<span class="co"># A 1.69 1.77 1.82 1.83 1.86 1.92 10</span>
<span class="co"># B 1.78 1.79 1.89 1.91 1.95 2.06 10</span>
<span class="co"># C 1.74 1.79 1.88 1.87 1.94 2.06 10</span>
<span class="co"># D 1.75 1.77 1.94 1.95 1.99 2.41 10</span>
<span class="co"># E 1.70 1.75 1.84 1.81 1.87 2.08 10</span>
<span class="co"># F 1.66 1.70 1.76 1.74 1.80 1.89 10</span>
<span class="co"># G 1.68 1.70 1.82 1.75 1.93 2.09 10</span>
<span class="co"># H 1.66 1.69 1.75 1.74 1.80 1.92 10</span></code></pre></div>
<p>Of course, when running <code><a href="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
</div>
<div id="results-in-other-languages" class="section level3">
@ -367,14 +350,14 @@
times <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>
<span class="fu"><a href="https://docs.ropensci.org/skimr/reference/print.html">print</a></span><span class="op">(</span><span class="va">run_it</span>, unit <span class="op">=</span> <span class="st">"ms"</span>, signif <span class="op">=</span> <span class="fl">4</span><span class="op">)</span>
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 15.77 16.36 18.40 16.85 17.50 53.58 100</span>
<span class="co"># de 19.03 19.70 24.60 20.14 21.06 59.83 100</span>
<span class="co"># nl 30.93 32.03 36.58 32.80 33.78 85.59 100</span>
<span class="co"># es 18.78 19.62 24.49 19.98 20.60 58.73 100</span>
<span class="co"># it 18.80 19.50 22.68 19.92 20.89 63.99 100</span>
<span class="co"># fr 18.78 19.59 21.99 19.90 20.75 58.87 100</span>
<span class="co"># pt 18.79 19.65 23.04 20.08 20.82 59.30 100</span></code></pre></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 15.91 16.98 21.87 17.54 18.34 168.50 100</span>
<span class="co"># de 18.98 20.14 26.30 20.74 22.64 66.68 100</span>
<span class="co"># nl 31.02 32.71 37.56 33.84 35.81 82.77 100</span>
<span class="co"># es 19.13 20.06 22.96 20.54 21.42 65.26 100</span>
<span class="co"># it 18.90 19.84 24.26 20.59 22.80 60.30 100</span>
<span class="co"># fr 18.89 19.92 23.46 20.49 21.72 62.85 100</span>
<span class="co"># pt 18.90 19.99 23.80 20.70 22.17 61.42 100</span></code></pre></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
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@ -392,7 +375,7 @@
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@ -56,7 +39,7 @@
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<span class="navbar-brand">
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9042</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9044</span>
</span>
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@ -1163,7 +1146,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
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@ -235,7 +205,6 @@
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<title>How to predict antimicrobial resistance • AMR (for R)</title>
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<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">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">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></code></pre></div>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></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 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>
@ -351,8 +333,7 @@
<code class="sourceCode R"><span class="va">example_isolates</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">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">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span>
<span class="co"># NOTE: Using column 'date' as input for `col_date`.</span></code></pre></div>
<span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="op">)</span></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>
@ -387,7 +368,7 @@
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