P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2025-07-25 03:35:38 +02:00
Code Releases Activity

(v1.1.0.9019) mo_source fix

Browse Source
This commit is contained in:
dr. M.S. (Matthijs) Berends
2020-05-25 01:01:14 +02:00
parent f5ff2e6634
commit ae1969b941
73 changed files with 619 additions and 571 deletions
Download Patch File Download Diff File Expand all files Collapse all files

467
docs/articles/AMR.html
View File

@ -39,7 +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">1.1.0.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>How to conduct AMR analysis</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">20 May 2020</h4>
<h4 class="date">25 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/AMR.Rmd"><code>vignettes/AMR.Rmd</code></a></small>
<div class="hidden name"><code>AMR.Rmd</code></div>
@ -195,7 +195,7 @@
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 20 May 2020.</p>
<p><strong>Note:</strong> values on this page will change with every website update since they are based on randomly created values and the page was written in <a href="https://rmarkdown.rstudio.com/">R Markdown</a>. However, the methodology remains unchanged. This page was generated on 25 May 2020.</p>
<div id="introduction" class="section level1">
<h1 class="hasAnchor">
<a href="#introduction" class="anchor"></a>Introduction</h1>
@ -226,21 +226,21 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2020-05-20</td>
<td align="center">2020-05-25</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2020-05-20</td>
<td align="center">2020-05-25</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2020-05-20</td>
<td align="center">2020-05-25</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -336,41 +336,63 @@
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2013-03-27</td>
<td align="center">L8</td>
<td align="center">2015-03-17</td>
<td align="center">U1</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2017-08-02</td>
<td align="center">P6</td>
<td align="center">Hospital B</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="odd">
<td align="center">2017-06-24</td>
<td align="center">E4</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2015-12-30</td>
<td align="center">K1</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">2011-02-12</td>
<td align="center">I10</td>
<td align="center">Hospital D</td>
<td align="center">Streptococcus pneumoniae</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2016-06-06</td>
<td align="center">D3</td>
<td align="center">Hospital B</td>
<td align="center">2010-03-17</td>
<td align="center">Q3</td>
<td align="center">Hospital C</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
<tr class="even">
<td align="center">2011-07-04</td>
<td align="center">H10</td>
<td align="center">2010-08-19</td>
<td align="center">A7</td>
<td align="center">Hospital D</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
@ -379,28 +401,6 @@
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="odd">
<td align="center">2012-04-12</td>
<td align="center">M3</td>
<td align="center">Hospital B</td>
<td align="center">Staphylococcus aureus</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">M</td>
</tr>
<tr class="even">
<td align="center">2017-03-06</td>
<td align="center">S4</td>
<td align="center">Hospital C</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
</tr>
</tbody>
</table>
<p>Now, let’s start the cleaning and the analysis!</p>
@ -432,16 +432,16 @@ Longest: 1</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">M</td>
<td align="right">10,319</td>
<td align="right">51.60%</td>
<td align="right">10,319</td>
<td align="right">51.60%</td>
<td align="right">10,403</td>
<td align="right">52.02%</td>
<td align="right">10,403</td>
<td align="right">52.02%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">F</td>
<td align="right">9,681</td>
<td align="right">48.41%</td>
<td align="right">9,597</td>
<td align="right">47.99%</td>
<td align="right">20,000</td>
<td align="right">100.00%</td>
</tr>
@ -456,11 +456,7 @@ Longest: 1</p>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html">mutate_at</a></span>(<span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html">vars</a></span>(<span class="no">AMX</span>:<span class="no">GEN</span>), <span class="no">as.rsi</span>)</pre></body></html></div>
<p>Finally, we will apply <a href="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">EUCAST rules</a> on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function can also apply additional rules, like forcing <help title="ATC: J01CA01">ampicillin</help> = R when <help title="ATC: J01CR02">amoxicillin/clavulanic acid</help> = R.</p>
<p>Because the amoxicillin (column <code>AMX</code>) and amoxicillin/clavulanic acid (column <code>AMC</code>) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> fixes this:</p>
<div class="sourceCode" id="cb13"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(<span class="no">data</span>, <span class="kw">col_mo</span> <span class="kw">=</span> <span class="st">"bacteria"</span>)
<span class="co"># [31m</span>
<span class="co"># Skipping inheritance rules defined by this package, such as setting trimethoprim (TMP) = R where trimethoprim/sulfamethoxazole (SXT) = R.</span>
<span class="co"># Use eucast_rules(..., rules = "all") to also apply those rules.</span>
<span class="co"># [39m</span></pre></body></html></div>
<div class="sourceCode" id="cb13"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span>(<span class="no">data</span>, <span class="kw">col_mo</span> <span class="kw">=</span> <span class="st">"bacteria"</span>)</pre></body></html></div>
</div>
<div id="adding-new-variables" class="section level1">
<h1 class="hasAnchor">
@ -482,10 +478,10 @@ Longest: 1</p>
<p>This <code>AMR</code> package includes this methodology with the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:</p>
<div class="sourceCode" id="cb15"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">first</span> <span class="kw">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span>(<span class="no">.</span>))
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mdate[22m` as input for `col_date`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mpatient_id[22m` as input for `col_patient_id`.[39m</span></pre></body></html></div>
<p>So only 28.5% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span></pre></body></html></div>
<p>So only 28.2% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb16"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="no">first</span> <span class="kw">==</span> <span class="fl">TRUE</span>)</pre></body></html></div>
<p>For future use, the above two syntaxes can be shortened with the <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> function:</p>
@ -495,7 +491,7 @@ Longest: 1</p>
<div id="first-weighted-isolates" class="section level2">
<h2 class="hasAnchor">
<a href="#first-weighted-isolates" class="anchor"></a>First <em>weighted</em> isolates</h2>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient S1, sorted on date:</p>
<p>We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient K4, sorted on date:</p>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -511,32 +507,32 @@ Longest: 1</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-10</td>
<td align="center">S1</td>
<td align="center">2010-01-01</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-02-27</td>
<td align="center">S1</td>
<td align="center">2010-02-09</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-03-05</td>
<td align="center">S1</td>
<td align="center">2010-03-03</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -544,8 +540,8 @@ Longest: 1</p>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-04-03</td>
<td align="center">S1</td>
<td align="center">2010-04-25</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -555,19 +551,19 @@ Longest: 1</p>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-05-22</td>
<td align="center">S1</td>
<td align="center">2010-07-04</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-07-19</td>
<td align="center">S1</td>
<td align="center">2010-09-04</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
@ -577,10 +573,10 @@ Longest: 1</p>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-07-19</td>
<td align="center">S1</td>
<td align="center">2010-10-01</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -588,30 +584,30 @@ Longest: 1</p>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2010-08-28</td>
<td align="center">S1</td>
<td align="center">2011-03-20</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">S</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2010-09-09</td>
<td align="center">S1</td>
<td align="center">2011-06-26</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2010-09-20</td>
<td align="center">S1</td>
<td align="center">2011-10-22</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -621,16 +617,16 @@ Longest: 1</p>
</tr>
</tbody>
</table>
<p>Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The <code><a href="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The <code><a href="../reference/key_antibiotics.html">key_antibiotics()</a></code> function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.</p>
<p>If a column exists with a name like ‘key(…)ab’ the <code><a href="../reference/first_isolate.html">first_isolate()</a></code> function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:</p>
<div class="sourceCode" id="cb18"><html><body><pre class="r"><span class="no">data</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">keyab</span> <span class="kw">=</span> <span class="fu"><a href="../reference/key_antibiotics.html">key_antibiotics</a></span>(<span class="no">.</span>)) <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span>(<span class="kw">first_weighted</span> <span class="kw">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span>(<span class="no">.</span>))
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mbacteria[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mdate[22m` as input for `col_date`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mpatient_id[22m` as input for `col_patient_id`.[39m</span>
<span class="co"># [34mNOTE: Using column `[1mkeyab[22m` as input for `col_keyantibiotics`. Use [1mcol_keyantibiotics = FALSE[22m to prevent this.[39m</span></pre></body></html></div>
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `bacteria` as input for `col_mo`.</span>
<span class="co"># NOTE: Using column `date` as input for `col_date`.</span>
<span class="co"># NOTE: Using column `patient_id` as input for `col_patient_id`.</span>
<span class="co"># NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.</span></pre></body></html></div>
<table class="table">
<thead><tr class="header">
<th align="center">isolate</th>
@ -647,44 +643,44 @@ Longest: 1</p>
<tbody>
<tr class="odd">
<td align="center">1</td>
<td align="center">2010-02-10</td>
<td align="center">S1</td>
<td align="center">2010-01-01</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">2</td>
<td align="center">2010-02-27</td>
<td align="center">S1</td>
<td align="center">2010-02-09</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">3</td>
<td align="center">2010-03-05</td>
<td align="center">S1</td>
<td align="center">2010-03-03</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">4</td>
<td align="center">2010-04-03</td>
<td align="center">S1</td>
<td align="center">2010-04-25</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -695,34 +691,34 @@ Longest: 1</p>
</tr>
<tr class="odd">
<td align="center">5</td>
<td align="center">2010-05-22</td>
<td align="center">S1</td>
<td align="center">2010-07-04</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="even">
<td align="center">6</td>
<td align="center">2010-07-19</td>
<td align="center">S1</td>
<td align="center">2010-09-04</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
<td align="center">FALSE</td>
</tr>
<tr class="odd">
<td align="center">7</td>
<td align="center">2010-07-19</td>
<td align="center">S1</td>
<td align="center">2010-10-01</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
@ -731,32 +727,32 @@ Longest: 1</p>
</tr>
<tr class="even">
<td align="center">8</td>
<td align="center">2010-08-28</td>
<td align="center">S1</td>
<td align="center">2011-03-20</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">S</td>
<td align="center">TRUE</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td align="center">9</td>
<td align="center">2010-09-09</td>
<td align="center">S1</td>
<td align="center">2011-06-26</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">FALSE</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td align="center">10</td>
<td align="center">2010-09-20</td>
<td align="center">S1</td>
<td align="center">2011-10-22</td>
<td align="center">K4</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
@ -767,11 +763,11 @@ Longest: 1</p>
</tr>
</tbody>
</table>
<p>Instead of 1, now 10 isolates are flagged. In total, 78.4% of all isolates are marked ‘first weighted’ - 49.9% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>Instead of 2, now 7 isolates are flagged. In total, 78.4% of all isolates are marked ‘first weighted’ - 50.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.</p>
<p>As with <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code>, there’s a shortcut for this new algorithm too:</p>
<div class="sourceCode" id="cb19"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="../reference/first_isolate.html">filter_first_weighted_isolate</a></span>()</pre></body></html></div>
<p>So we end up with 15,684 isolates for analysis.</p>
<p>So we end up with 15,673 isolates for analysis.</p>
<p>We can remove unneeded columns:</p>
<div class="sourceCode" id="cb20"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">&lt;-</span> <span class="no">data_1st</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(-<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">first</span>, <span class="no">keyab</span>))</pre></body></html></div>
@ -797,14 +793,46 @@ Longest: 1</p>
<tbody>
<tr class="odd">
<td>1</td>
<td align="center">2013-03-27</td>
<td align="center">L8</td>
<td align="center">2015-03-17</td>
<td align="center">U1</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>2</td>
<td align="center">2017-08-02</td>
<td align="center">P6</td>
<td align="center">Hospital B</td>
<td align="center">B_STRPT_PNMN</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">F</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>4</td>
<td align="center">2011-02-12</td>
<td align="center">I10</td>
<td align="center">Hospital D</td>
<td align="center">B_STRPT_PNMN</td>
<td align="center">R</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Streptococcus</td>
@ -812,41 +840,9 @@ Longest: 1</p>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>2</td>
<td align="center">2015-12-30</td>
<td align="center">K1</td>
<td align="center">Hospital B</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>3</td>
<td align="center">2016-06-06</td>
<td align="center">D3</td>
<td align="center">Hospital B</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>4</td>
<td align="center">2011-07-04</td>
<td align="center">H10</td>
<td>6</td>
<td align="center">2010-08-19</td>
<td align="center">A7</td>
<td align="center">Hospital D</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">S</td>
@ -860,35 +856,35 @@ Longest: 1</p>
<td align="center">TRUE</td>
</tr>
<tr class="odd">
<td>6</td>
<td align="center">2017-03-06</td>
<td align="center">S4</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COLI</td>
<td>7</td>
<td align="center">2013-04-06</td>
<td align="center">H5</td>
<td align="center">Hospital A</td>
<td align="center">B_STPHY_AURS</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">S</td>
<td align="center">F</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">M</td>
<td align="center">Gram-positive</td>
<td align="center">Staphylococcus</td>
<td align="center">aureus</td>
<td align="center">TRUE</td>
</tr>
<tr class="even">
<td>7</td>
<td align="center">2014-08-08</td>
<td align="center">C5</td>
<td>8</td>
<td align="center">2013-12-11</td>
<td align="center">J8</td>
<td align="center">Hospital C</td>
<td align="center">B_ESCHR_COLI</td>
<td align="center">B_KLBSL_PNMN</td>
<td align="center">R</td>
<td align="center">I</td>
<td align="center">S</td>
<td align="center">R</td>
<td align="center">S</td>
<td align="center">M</td>
<td align="center">Gram-negative</td>
<td align="center">Escherichia</td>
<td align="center">coli</td>
<td align="center">Klebsiella</td>
<td align="center">pneumoniae</td>
<td align="center">TRUE</td>
</tr>
</tbody>
@ -910,8 +906,8 @@ Longest: 1</p>
<div class="sourceCode" id="cb23"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="no">genus</span>, <span class="no">species</span>)</pre></body></html></div>
<p><strong>Frequency table</strong></p>
<p>Class: character<br>
Length: 15,684<br>
Available: 15,684 (100%, NA: 0 = 0%)<br>
Length: 15,673<br>
Available: 15,673 (100%, NA: 0 = 0%)<br>
Unique: 4</p>
<p>Shortest: 16<br>
Longest: 24</p>
@ -928,33 +924,33 @@ Longest: 24</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Escherichia coli</td>
<td align="right">7,819</td>
<td align="right">49.85%</td>
<td align="right">7,819</td>
<td align="right">49.85%</td>
<td align="right">7,843</td>
<td align="right">50.04%</td>
<td align="right">7,843</td>
<td align="right">50.04%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Staphylococcus aureus</td>
<td align="right">3,992</td>
<td align="right">25.45%</td>
<td align="right">11,811</td>
<td align="right">75.31%</td>
<td align="right">3,949</td>
<td align="right">25.20%</td>
<td align="right">11,792</td>
<td align="right">75.24%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Streptococcus pneumoniae</td>
<td align="right">2,332</td>
<td align="right">14.87%</td>
<td align="right">14,143</td>
<td align="right">90.17%</td>
<td align="right">2,320</td>
<td align="right">14.80%</td>
<td align="right">14,112</td>
<td align="right">90.04%</td>
</tr>
<tr class="even">
<td align="left">4</td>
<td align="left">Klebsiella pneumoniae</td>
<td align="right">1,541</td>
<td align="right">9.83%</td>
<td align="right">15,684</td>
<td align="right">1,561</td>
<td align="right">9.96%</td>
<td align="right">15,673</td>
<td align="right">100.00%</td>
</tr>
</tbody>
@ -966,7 +962,7 @@ Longest: 24</p>
<p>The functions <code><a href="../reference/proportion.html">resistance()</a></code> and <code><a href="../reference/proportion.html">susceptibility()</a></code> can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions <code><a href="../reference/proportion.html">proportion_S()</a></code>, <code><a href="../reference/proportion.html">proportion_SI()</a></code>, <code><a href="../reference/proportion.html">proportion_I()</a></code>, <code><a href="../reference/proportion.html">proportion_IR()</a></code> and <code><a href="../reference/proportion.html">proportion_R()</a></code> can be used to determine the proportion of a specific antimicrobial outcome.</p>
<p>As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a></code>, equal to <code><a href="../reference/proportion.html">resistance()</a></code>) and susceptibility as the proportion of S and I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their own:</p>
<div class="sourceCode" id="cb24"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span>(<span class="no">AMX</span>)
<span class="co"># [1] 0.4410227</span></pre></body></html></div>
<span class="co"># [1] 0.441396</span></pre></body></html></div>
<p>Or can be used in conjuction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
<div class="sourceCode" id="cb25"><html><body><pre class="r"><span class="no">data_1st</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="no">hospital</span>) <span class="kw">%&gt;%</span>
@ -979,19 +975,19 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4448217</td>
<td align="center">0.4339461</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4476345</td>
<td align="center">0.4463033</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4299828</td>
<td align="center">0.4511013</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4317450</td>
<td align="center">0.4368288</td>
</tr>
</tbody>
</table>
@ -1009,23 +1005,23 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Hospital A</td>
<td align="center">0.4448217</td>
<td align="center">4712</td>
<td align="center">0.4339461</td>
<td align="center">4678</td>
</tr>
<tr class="even">
<td align="center">Hospital B</td>
<td align="center">0.4476345</td>
<td align="center">5538</td>
<td align="center">0.4463033</td>
<td align="center">5559</td>
</tr>
<tr class="odd">
<td align="center">Hospital C</td>
<td align="center">0.4299828</td>
<td align="center">2328</td>
<td align="center">0.4511013</td>
<td align="center">2270</td>
</tr>
<tr class="even">
<td align="center">Hospital D</td>
<td align="center">0.4317450</td>
<td align="center">3106</td>
<td align="center">0.4368288</td>
<td align="center">3166</td>
</tr>
</tbody>
</table>
@ -1045,27 +1041,27 @@ Longest: 24</p>
<tbody>
<tr class="odd">
<td align="center">Escherichia</td>
<td align="center">0.8199258</td>
<td align="center">0.8988362</td>
<td align="center">0.9842691</td>
<td align="center">0.8236644</td>
<td align="center">0.8999107</td>
<td align="center">0.9832972</td>
</tr>
<tr class="even">
<td align="center">Klebsiella</td>
<td align="center">0.8189487</td>
<td align="center">0.8916288</td>
<td align="center">0.9857236</td>
<td align="center">0.8315183</td>
<td align="center">0.8923767</td>
<td align="center">0.9820628</td>
</tr>
<tr class="odd">
<td align="center">Staphylococcus</td>
<td align="center">0.8166333</td>
<td align="center">0.9170842</td>
<td align="center">0.9857214</td>
<td align="center">0.8219802</td>
<td align="center">0.9161813</td>
<td align="center">0.9853127</td>
</tr>
<tr class="even">
<td align="center">Streptococcus</td>
<td align="center">0.6170669</td>
<td align="center">0.6224138</td>
<td align="center">0.0000000</td>
<td align="center">0.6170669</td>
<td align="center">0.6224138</td>
</tr>
</tbody>
</table>
@ -1141,8 +1137,7 @@ Longest: 24</p>
<a href="#independence-test" class="anchor"></a>Independence test</h2>
<p>The next example uses the <code>example_isolates</code> data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.</p>
<p>We will compare the resistance to fosfomycin (column <code>FOS</code>) in hospital A and D. The input for the <code><a href="https://rdrr.io/r/stats/fisher.test.html">fisher.test()</a></code> can be retrieved with a transformation like this:</p>
<div class="sourceCode" id="cb33"><html><body><pre class="r"><span class="co"># use package 'tidyr' to pivot data; </span>
<span class="co"># it gets installed with this 'AMR' package</span>
<div class="sourceCode" id="cb33"><html><body><pre class="r"><span class="co"># use package 'tidyr' to pivot data:</span>
<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">tidyr</span>)
<span class="no">check_FOS</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
@ -1173,7 +1168,7 @@ Longest: 24</p>
<span class="co"># sample estimates:</span>
<span class="co"># odds ratio </span>
<span class="co"># 0.4488318</span></pre></body></html></div>
<p>As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in hospital A and D are really different.</p>
<p>As can be seen, the p value is 0.031, which means that the fosfomycin resistance found in isolates from patients in hospital A and D are really different.</p>
</div>
</div>
</div>

BIN
docs/articles/AMR_files/figure-html/plot 1-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 64 KiB

After

Width:  |  Height:  |  Size: 64 KiB

BIN
docs/articles/AMR_files/figure-html/plot 3-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 51 KiB

After

Width:  |  Height:  |  Size: 51 KiB

BIN
docs/articles/AMR_files/figure-html/plot 4-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 102 KiB

After

Width:  |  Height:  |  Size: 102 KiB

BIN
docs/articles/AMR_files/figure-html/plot 5-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 83 KiB

After

Width:  |  Height:  |  Size: 83 KiB

74
docs/articles/MDR.html
View File

@ -39,7 +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">1.1.0.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>How to determine multi-drug resistance (MDR)</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">20 May 2020</h4>
<h4 class="date">25 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/MDR.Rmd"><code>vignettes/MDR.Rmd</code></a></small>
<div class="hidden name"><code>MDR.Rmd</code></div>
@ -235,9 +235,9 @@ The German national guideline - Mueller et al. (2015) Antimicrobial Resistance a
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="../reference/mdro.html">mdro</a></span>() <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>() <span class="co"># show frequency table of the result</span>
<span class="co"># [34mNOTE: Using column `[1mmo[22m` as input for `col_mo`.[39m</span>
<span class="co"># [34mNOTE: Auto-guessing columns suitable for analysis...[39m[34mOK.[39m</span>
<span class="co"># [34mNOTE: Reliability would be improved if these antimicrobial results would be available too: ceftaroline ([1mCPT[22m), fusidic acid ([1mFUS[22m), telavancin ([1mTLV[22m), daptomycin ([1mDAP[22m), quinupristin/dalfopristin ([1mQDA[22m), minocycline ([1mMNO[22m), gentamicin-high ([1mGEH[22m), streptomycin-high ([1mSTH[22m), doripenem ([1mDOR[22m), levofloxacin ([1mLVX[22m), netilmicin ([1mNET[22m), ticarcillin/clavulanic acid ([1mTCC[22m), ertapenem ([1mETP[22m), cefotetan ([1mCTT[22m), aztreonam ([1mATM[22m), ampicillin/sulbactam ([1mSAM[22m), polymyxin B ([1mPLB[22m)[39m</span>
<span class="co"># NOTE: Using column `mo` as input for `col_mo`.</span>
<span class="co"># NOTE: Auto-guessing columns suitable for analysis...OK.</span>
<span class="co"># NOTE: Reliability would be improved if these antimicrobial results would be available too: ceftaroline (CPT), fusidic acid (FUS), telavancin (TLV), daptomycin (DAP), quinupristin/dalfopristin (QDA), minocycline (MNO), gentamicin-high (GEH), streptomycin-high (STH), doripenem (DOR), levofloxacin (LVX), netilmicin (NET), ticarcillin/clavulanic acid (TCC), ertapenem (ETP), cefotetan (CTT), aztreonam (ATM), ampicillin/sulbactam (SAM), polymyxin B (PLB)</span>
<span class="co"># Warning in mdro(.): NA introduced for isolates where the available percentage of</span>
<span class="co"># antimicrobial classes was below 50% (set with `pct_required_classes`)</span></pre></body></html></div>
<p><strong>Frequency table</strong></p>
@ -302,26 +302,26 @@ Unique: 2</p>
<p>The data set now looks like this:</p>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">my_TB_data</span>)
<span class="co"># rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span>
<span class="co"># 1 R R R R S S</span>
<span class="co"># 2 S S S I R S</span>
<span class="co"># 3 S R S I S I</span>
<span class="co"># 4 S R S S S R</span>
<span class="co"># 5 R S R S S R</span>
<span class="co"># 6 I S R R R R</span>
<span class="co"># 1 S S R R S S</span>
<span class="co"># 2 R R S R S R</span>
<span class="co"># 3 R S S R S S</span>
<span class="co"># 4 R S S S S R</span>
<span class="co"># 5 S R R S R R</span>
<span class="co"># 6 R R R R R R</span>
<span class="co"># kanamycin</span>
<span class="co"># 1 I</span>
<span class="co"># 1 R</span>
<span class="co"># 2 S</span>
<span class="co"># 3 R</span>
<span class="co"># 4 I</span>
<span class="co"># 5 I</span>
<span class="co"># 6 S</span></pre></body></html></div>
<span class="co"># 3 S</span>
<span class="co"># 4 S</span>
<span class="co"># 5 R</span>
<span class="co"># 6 R</span></pre></body></html></div>
<p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="fu"><a href="../reference/mdro.html">mdro</a></span>(<span class="no">my_TB_data</span>, <span class="kw">guideline</span> <span class="kw">=</span> <span class="st">"TB"</span>)</pre></body></html></div>
<p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">my_TB_data</span>$<span class="no">mdr</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span>(<span class="no">my_TB_data</span>)
<span class="co"># [34mNOTE: No column found as input for `col_mo`, [1massuming all records contain [3mMycobacterium tuberculosis.[23m[22m[39m</span>
<span class="co"># [34mNOTE: Auto-guessing columns suitable for analysis...[39m[34mOK.[39m</span>
<span class="co"># [34mNOTE: Reliability would be improved if these antimicrobial results would be available too: capreomycin ([1mCAP[22m), rifabutin ([1mRIB[22m), rifapentine ([1mRFP[22m)[39m</span></pre></body></html></div>
<span class="co"># NOTE: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.</span>
<span class="co"># NOTE: Auto-guessing columns suitable for analysis...OK.</span>
<span class="co"># NOTE: Reliability would be improved if these antimicrobial results would be available too: capreomycin (CAP), rifabutin (RIB), rifapentine (RFP)</span></pre></body></html></div>
<p>Create a frequency table of the results:</p>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/pkg/cleaner/man/freq.html">freq</a></span>(<span class="no">my_TB_data</span>$<span class="no">mdr</span>)</pre></body></html></div>
<p><strong>Frequency table</strong></p>
@ -343,40 +343,40 @@ Unique: 5</p>
<tr class="odd">
<td align="left">1</td>
<td align="left">Mono-resistant</td>
<td align="right">3288</td>
<td align="right">65.76%</td>
<td align="right">3288</td>
<td align="right">65.76%</td>
<td align="right">3239</td>
<td align="right">64.78%</td>
<td align="right">3239</td>
<td align="right">64.78%</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">Negative</td>
<td align="right">631</td>
<td align="right">12.62%</td>
<td align="right">3919</td>
<td align="right">78.38%</td>
<td align="right">655</td>
<td align="right">13.10%</td>
<td align="right">3894</td>
<td align="right">77.88%</td>
</tr>
<tr class="odd">
<td align="left">3</td>
<td align="left">Multi-drug-resistant</td>
<td align="right">582</td>
<td align="right">11.64%</td>
<td align="right">4501</td>
<td align="right">90.02%</td>
<td align="right">593</td>
<td align="right">11.86%</td>
<td align="right">4487</td>
<td align="right">89.74%</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">4799</td>
<td align="right">95.98%</td>
<td align="right">304</td>
<td align="right">6.08%</td>
<td align="right">4791</td>
<td align="right">95.82%</td>
</tr>
<tr class="odd">
<td align="left">5</td>
<td align="left">Extensively drug-resistant</td>
<td align="right">201</td>
<td align="right">4.02%</td>
<td align="right">209</td>
<td align="right">4.18%</td>
<td align="right">5000</td>
<td align="right">100.00%</td>
</tr>

93
docs/articles/PCA.html
View File

@ -39,7 +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">1.1.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 April 2020</h4>
<h4 class="date">25 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
<div class="hidden name"><code>PCA.Rmd</code></div>
@ -217,47 +217,47 @@
<span class="co"># $ age &lt;dbl&gt; 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 7…</span>
<span class="co"># $ gender &lt;chr&gt; "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M"…</span>
<span class="co"># $ patient_id &lt;chr&gt; "A77334", "A77334", "067927", "067927", "067927", "06…</span>
<span class="co"># $ mo &lt;mo&gt; B_ESCHR_COLI, B_ESCHR_COLI, B_STPHY_EPDR, B_STPHY_EPDR…</span>
<span class="co"># $ PEN &lt;rsi&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span>
<span class="co"># $ OXA &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FLC &lt;rsi&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ AMX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ AMC &lt;rsi&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span>
<span class="co"># $ AMP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TZP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CZO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FEP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CXM &lt;rsi&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span>
<span class="co"># $ FOX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CTX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ CAZ &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span>
<span class="co"># $ CRO &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ GEN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TOB &lt;rsi&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span>
<span class="co"># $ AMK &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ KAN &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TMP &lt;rsi&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span>
<span class="co"># $ SXT &lt;rsi&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span>
<span class="co"># $ NIT &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FOS &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ LNZ &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ CIP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ MFX &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ VAN &lt;rsi&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span>
<span class="co"># $ TEC &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ TCY &lt;rsi&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span>
<span class="co"># $ TGC &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ DOX &lt;rsi&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span>
<span class="co"># $ ERY &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ CLI &lt;rsi&gt; NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ AZM &lt;rsi&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ IPM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ MEM &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ MTR &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CHL &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ COL &lt;rsi&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
<span class="co"># $ MUP &lt;rsi&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ RIF &lt;rsi&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></body></html></div>
<span class="co"># $ mo &lt;mo&gt; "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STP…</span>
<span class="co"># $ PEN &lt;ord&gt; R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R,…</span>
<span class="co"># $ OXA &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FLC &lt;ord&gt; NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ AMX &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ AMC &lt;ord&gt; I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I,…</span>
<span class="co"># $ AMP &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TZP &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CZO &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FEP &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CXM &lt;ord&gt; I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R…</span>
<span class="co"># $ FOX &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CTX &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ CAZ &lt;ord&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, …</span>
<span class="co"># $ CRO &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ GEN &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TOB &lt;ord&gt; NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, N…</span>
<span class="co"># $ AMK &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ KAN &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ TMP &lt;ord&gt; R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, …</span>
<span class="co"># $ SXT &lt;ord&gt; R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S,…</span>
<span class="co"># $ NIT &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ FOS &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ LNZ &lt;ord&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ CIP &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA,…</span>
<span class="co"># $ MFX &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ VAN &lt;ord&gt; R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, …</span>
<span class="co"># $ TEC &lt;ord&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
<span class="co"># $ TCY &lt;ord&gt; R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, …</span>
<span class="co"># $ TGC &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ DOX &lt;ord&gt; NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R,…</span>
<span class="co"># $ ERY &lt;ord&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ CLI &lt;ord&gt; NA, NA, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, NA, NA…</span>
<span class="co"># $ AZM &lt;ord&gt; R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R,…</span>
<span class="co"># $ IPM &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S,…</span>
<span class="co"># $ MEM &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ MTR &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ CHL &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ COL &lt;ord&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
<span class="co"># $ MUP &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
<span class="co"># $ RIF &lt;ord&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></body></html></div>
<p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">resistance_data</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="kw">order</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="no">mo</span>), <span class="co"># group on anything, like order</span>
@ -283,7 +283,7 @@
<a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">pca_result</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="no">resistance_data</span>)
<span class="co"># NOTE: Columns selected for PCA: AMC/CXM/CTX/CAZ/GEN/TOB/TMP/SXT.</span>
<span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT.</span>
<span class="co"># Total observations available: 7.</span></pre></body></html></div>
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">pca_result</span>)
@ -303,8 +303,7 @@
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>)</pre></body></html></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
<p>You can also print an ellipse per group, and edit the appearance:</p>
<div class="sourceCode" id="cb7"><html><body><pre class="r">
<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>, <span class="kw">ellipse</span> <span class="kw">=</span> <span class="fl">TRUE</span>) +
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>, <span class="kw">ellipse</span> <span class="kw">=</span> <span class="fl">TRUE</span>) +
<span class="kw pkg">ggplot2</span><span class="kw ns">::</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="st">"An AMR/PCA biplot!"</span>)</pre></body></html></div>
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
</div>
@ -325,7 +324,7 @@
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
</div>
</footer>

BIN
docs/articles/PCA_files/figure-html/unnamed-chunk-5-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 82 KiB

After

Width:  |  Height:  |  Size: 87 KiB

109
docs/articles/benchmarks.html
View File

@ -39,7 +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">1.1.0.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>Benchmarks</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">20 May 2020</h4>
<h4 class="date">25 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/benchmarks.Rmd"><code>vignettes/benchmarks.Rmd</code></a></small>
<div class="hidden name"><code>benchmarks.Rmd</code></div>
@ -221,21 +221,21 @@
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">S.aureus</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">2</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 8.7 9.4 13.0 9.5 11 39.0 10</span>
<span class="co"># as.mo("stau") 140.0 150.0 160.0 170.0 180 180.0 10</span>
<span class="co"># as.mo("STAU") 140.0 140.0 160.0 150.0 170 180.0 10</span>
<span class="co"># as.mo("staaur") 8.7 9.6 16.0 11.0 11 41.0 10</span>
<span class="co"># as.mo("STAAUR") 8.7 9.2 13.0 10.0 11 37.0 10</span>
<span class="co"># as.mo("S. aureus") 10.0 12.0 23.0 13.0 39 41.0 10</span>
<span class="co"># as.mo("S aureus") 9.4 10.0 11.0 11.0 12 12.0 10</span>
<span class="co"># as.mo("Staphylococcus aureus") 7.5 7.5 8.3 8.4 9 9.3 10</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 890.0 930.0 940.0 940.0 950 980.0 10</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 370.0 390.0 420.0 400.0 440 510.0 10</span>
<span class="co"># as.mo("MRSA") 8.0 8.9 15.0 10.0 11 39.0 10</span>
<span class="co"># as.mo("VISA") 20.0 21.0 36.0 25.0 53 60.0 10</span>
<span class="co"># as.mo("VRSA") 19.0 22.0 29.0 24.0 26 53.0 10</span>
<span class="co"># as.mo(22242419) 150.0 150.0 170.0 150.0 160 290.0 10</span></pre></body></html></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># as.mo("sau") 9.7 11.0 15 11.0 12.0 49 10</span>
<span class="co"># as.mo("stau") 130.0 150.0 180 170.0 190.0 270 10</span>
<span class="co"># as.mo("STAU") 130.0 130.0 140 140.0 150.0 170 10</span>
<span class="co"># as.mo("staaur") 7.9 9.6 15 11.0 12.0 40 10</span>
<span class="co"># as.mo("STAAUR") 9.1 9.4 16 11.0 12.0 41 10</span>
<span class="co"># as.mo("S. aureus") 8.5 11.0 12 12.0 13.0 16 10</span>
<span class="co"># as.mo("S aureus") 8.4 11.0 17 12.0 14.0 47 10</span>
<span class="co"># as.mo("Staphylococcus aureus") 6.4 8.6 12 8.8 9.8 40 10</span>
<span class="co"># as.mo("Staphylococcus aureus (MRSA)") 830.0 860.0 910 900.0 930.0 1100 10</span>
<span class="co"># as.mo("Sthafilokkockus aaureuz") 370.0 390.0 410 400.0 430.0 440 10</span>
<span class="co"># as.mo("MRSA") 9.2 9.5 16 11.0 11.0 63 10</span>
<span class="co"># as.mo("VISA") 22.0 22.0 36 26.0 54.0 59 10</span>
<span class="co"># as.mo("VRSA") 21.0 23.0 48 27.0 55.0 180 10</span>
<span class="co"># as.mo(22242419) 150.0 160.0 160 160.0 170.0 190 10</span></pre></body></html></div>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-4-1.png" width="562.5"></p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
@ -247,18 +247,18 @@
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">M.semesiae</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">4</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq</span>
<span class="co"># as.mo("metsem") 163.900 165.300 180.600 176.600 192.600</span>
<span class="co"># as.mo("METSEM") 153.500 156.100 177.300 176.000 200.400</span>
<span class="co"># as.mo("M. semesiae") 9.155 10.250 15.980 10.800 12.790</span>
<span class="co"># as.mo("M. semesiae") 9.498 10.160 16.700 10.560 11.050</span>
<span class="co"># as.mo("Methanosarcina semesiae") 7.006 7.345 7.993 7.814 8.413</span>
<span class="co"># max neval</span>
<span class="co"># 206.600 10</span>
<span class="co"># 206.800 10</span>
<span class="co"># 38.630 10</span>
<span class="co"># 46.810 10</span>
<span class="co"># 9.659 10</span></pre></body></html></div>
<span class="co"># expr min lq mean median uq max</span>
<span class="co"># as.mo("metsem") 152.700 163.100 172.10 171.40 176.20 213.80</span>
<span class="co"># as.mo("METSEM") 148.300 167.900 181.50 185.30 196.70 204.60</span>
<span class="co"># as.mo("M. semesiae") 8.610 9.468 13.88 10.05 15.28 35.20</span>
<span class="co"># as.mo("M. semesiae") 9.164 9.530 21.29 11.58 42.98 50.57</span>
<span class="co"># as.mo("Methanosarcina semesiae") 6.625 7.229 14.25 7.98 11.32 42.18</span>
<span class="co"># neval</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span>
<span class="co"># 10</span></pre></body></html></div>
<p>Looking up arbitrary codes of less prevalent microorganisms costs the most time. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p><img src="benchmarks_files/figure-html/unnamed-chunk-6-1.png" width="900"></p>
@ -267,8 +267,7 @@
<h3 class="hasAnchor">
<a href="#repetitive-results" class="anchor"></a>Repetitive results</h3>
<p>Repetitive results are unique values that are present more than once. Unique values will only be calculated once by <code><a href="../reference/as.mo.html">as.mo()</a></code>. We will use <code><a href="../reference/mo_property.html">mo_name()</a></code> for this test - a helper function that returns the full microbial name (genus, species and possibly subspecies) which uses <code><a href="../reference/as.mo.html">as.mo()</a></code> internally.</p>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)</pre></body></html></div>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="co"># take all MO codes from the example_isolates data set</span>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="co"># take all MO codes from the example_isolates data set</span>
<span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span>$<span class="no">mo</span> <span class="kw">%&gt;%</span>
<span class="co"># keep only the unique ones</span>
<span class="fu"><a href="https://rdrr.io/r/base/unique.html">unique</a></span>() <span class="kw">%&gt;%</span>
@ -293,25 +292,25 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</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) 1690 1710 1770 1760 1810 1870 10</span></pre></body></html></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 1.76 seconds. You only lose time on your unique input values.</p>
<span class="co"># mo_name(x) 1700 1760 1780 1770 1800 1880 10</span></pre></body></html></div>
<p>So transforming 500,000 values (!!) of 50 unique values only takes 1.77 seconds. You only lose time on your unique input values.</p>
</div>
<div id="precalculated-results" class="section level3">
<h3 class="hasAnchor">
<a href="#precalculated-results" class="anchor"></a>Precalculated results</h3>
<p>What about precalculated results? If the input is an already precalculated result of a helper function like <code><a href="../reference/mo_property.html">mo_name()</a></code>, it almost doesn’t take any time at all (see ‘C’ below):</p>
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"B_STPHY_AURS"</span>),
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"B_STPHY_AURS"</span>),
<span class="kw">B</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"S. aureus"</span>),
<span class="kw">C</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),
<span class="kw">times</span> <span class="kw">=</span> <span class="fl">10</span>)
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 6.240 6.490 6.860 6.910 7.15 7.510 10</span>
<span class="co"># B 10.500 10.600 15.300 12.000 12.10 49.300 10</span>
<span class="co"># C 0.198 0.243 0.266 0.278 0.29 0.322 10</span></pre></body></html></div>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 5.640 5.890 10.300 6.580 6.850 44.400 10</span>
<span class="co"># B 11.000 11.300 11.500 11.400 11.500 12.400 10</span>
<span class="co"># C 0.217 0.238 0.271 0.267 0.298 0.383 10</span></pre></body></html></div>
<p>So going from <code><a href="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0003 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="cb7"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),
<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">run_it</span> <span class="kw">&lt;-</span> <span class="fu">microbenchmark</span>(<span class="kw">A</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_species</a></span>(<span class="st">"aureus"</span>),
<span class="kw">B</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="st">"Staphylococcus"</span>),
<span class="kw">C</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"Staphylococcus aureus"</span>),
<span class="kw">D</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_family</a></span>(<span class="st">"Staphylococcaceae"</span>),
@ -323,21 +322,21 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">3</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># A 0.248 0.253 0.266 0.256 0.271 0.323 10</span>
<span class="co"># B 0.248 0.254 0.272 0.255 0.257 0.420 10</span>
<span class="co"># C 0.245 0.259 0.265 0.268 0.271 0.286 10</span>
<span class="co"># D 0.248 0.252 0.268 0.261 0.278 0.323 10</span>
<span class="co"># E 0.250 0.256 0.265 0.261 0.268 0.312 10</span>
<span class="co"># F 0.237 0.238 0.249 0.243 0.246 0.317 10</span>
<span class="co"># G 0.239 0.242 0.252 0.245 0.245 0.319 10</span>
<span class="co"># H 0.233 0.241 0.262 0.256 0.272 0.347 10</span></pre></body></html></div>
<span class="co"># A 0.205 0.208 0.230 0.219 0.240 0.307 10</span>
<span class="co"># B 0.201 0.216 0.227 0.223 0.229 0.293 10</span>
<span class="co"># C 0.209 0.210 0.224 0.218 0.241 0.255 10</span>
<span class="co"># D 0.200 0.211 0.222 0.217 0.224 0.279 10</span>
<span class="co"># E 0.199 0.209 0.220 0.211 0.219 0.280 10</span>
<span class="co"># F 0.201 0.205 0.228 0.212 0.217 0.346 10</span>
<span class="co"># G 0.193 0.208 0.218 0.217 0.220 0.272 10</span>
<span class="co"># H 0.190 0.195 0.206 0.200 0.203 0.263 10</span></pre></body></html></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">
<h3 class="hasAnchor">
<a href="#results-in-other-languages" class="anchor"></a>Results in other languages</h3>
<p>When the system language is non-English and supported by this <code>AMR</code> package, some functions will have a translated result. This almost does’t take extra time:</p>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>) <span class="co"># or just mo_name("CoNS") on an English system</span>
<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"en"</span>) <span class="co"># or just mo_name("CoNS") on an English system</span>
<span class="co"># [1] "Coagulase-negative Staphylococcus (CoNS)"</span>
<span class="fu"><a href="../reference/mo_property.html">mo_name</a></span>(<span class="st">"CoNS"</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="st">"es"</span>) <span class="co"># or just mo_name("CoNS") on a Spanish system</span>
@ -357,13 +356,13 @@
<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">run_it</span>, <span class="kw">unit</span> <span class="kw">=</span> <span class="st">"ms"</span>, <span class="kw">signif</span> <span class="kw">=</span> <span class="fl">4</span>)
<span class="co"># Unit: milliseconds</span>
<span class="co"># expr min lq mean median uq max neval</span>
<span class="co"># en 20.99 21.59 26.64 22.35 22.94 69.49 100</span>
<span class="co"># de 22.02 22.54 26.83 22.98 23.87 63.13 100</span>
<span class="co"># nl 25.78 26.55 34.69 27.25 28.22 182.90 100</span>
<span class="co"># es 21.92 22.67 26.97 23.26 24.01 62.68 100</span>
<span class="co"># it 21.96 22.58 25.11 22.90 23.50 66.81 100</span>
<span class="co"># fr 21.82 22.76 27.53 23.26 23.84 66.75 100</span>
<span class="co"># pt 21.76 22.56 25.84 23.13 23.90 63.63 100</span></pre></body></html></div>
<span class="co"># en 20.45 21.03 25.78 21.63 27.29 71.19 100</span>
<span class="co"># de 21.48 22.06 28.50 22.81 28.56 73.54 100</span>
<span class="co"># nl 25.11 26.21 33.48 27.31 35.01 81.28 100</span>
<span class="co"># es 21.59 22.20 29.02 23.05 30.33 73.83 100</span>
<span class="co"># it 21.46 22.05 28.45 22.79 29.45 64.36 100</span>
<span class="co"># fr 21.47 22.12 31.87 22.83 31.24 174.10 100</span>
<span class="co"># pt 21.53 22.22 27.19 22.91 25.67 71.87 100</span></pre></body></html></div>
<p>Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.</p>
</div>
</div>

BIN
docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 91 KiB

After

Width:  |  Height:  |  Size: 91 KiB

BIN
docs/articles/benchmarks_files/figure-html/unnamed-chunk-6-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 60 KiB

After

Width:  |  Height:  |  Size: 56 KiB

2
docs/articles/index.html
View File

@ -81,7 +81,7 @@
</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">1.1.0.9018</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>

77
docs/articles/resistance_predict.html
View File

@ -39,7 +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">1.1.0</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.1.0.9019</span>
</span>
</div>
@ -186,7 +186,7 @@
<h1 data-toc-skip>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">15 April 2020</h4>
<h4 class="date">25 May 2020</h4>
<small class="dont-index">Source: <a href="https://gitlab.com/msberends/AMR/blob/master/vignettes/resistance_predict.Rmd"><code>vignettes/resistance_predict.Rmd</code></a></small>
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
@ -221,7 +221,7 @@ example_isolates %&gt;%
model "binomial")
# to bind it to object 'predict_TZP' for example:
predict_TZP %
predict_TZP &lt;- example_isolates %&gt;%
resistance_predict(col_ab = "TZP",
model = "binomial")</pre></body></html></div>
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
@ -230,36 +230,37 @@ predict_TZP %
<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>
<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">predict_TZP</span>
<span class="co"># year value se_min se_max observations observed estimated</span>
<span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span>
<span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span>
<span class="co"># 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span>
<span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span>
<span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span>
<span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span>
<span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span>
<span class="co"># 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span>
<span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span>
<span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span>
<span class="co"># 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span>
<span class="co"># 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span>
<span class="co"># 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span>
<span class="co"># 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span>
<span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span>
<span class="co"># 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span>
<span class="co"># 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span>
<span class="co"># 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span>
<span class="co"># 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span>
<span class="co"># 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span>
<span class="co"># 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span>
<span class="co"># 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span>
<span class="co"># 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span>
<span class="co"># 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span>
<span class="co"># 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span>
<span class="co"># 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span>
<span class="co"># 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span>
<span class="co"># 28 2030 0.53494347 0.4244247 0.6454622 NA NA 0.53494347</span></pre></body></html></div>
<span class="co"># 1 2002 0.20000000 NA NA 15 0.20000000 0.05616378</span>
<span class="co"># 2 2003 0.06250000 NA NA 32 0.06250000 0.06163839</span>
<span class="co"># 3 2004 0.08536585 NA NA 82 0.08536585 0.06760841</span>
<span class="co"># 4 2005 0.05000000 NA NA 60 0.05000000 0.07411100</span>
<span class="co"># 5 2006 0.05084746 NA NA 59 0.05084746 0.08118454</span>
<span class="co"># 6 2007 0.12121212 NA NA 66 0.12121212 0.08886843</span>
<span class="co"># 7 2008 0.04166667 NA NA 72 0.04166667 0.09720264</span>
<span class="co"># 8 2009 0.01639344 NA NA 61 0.01639344 0.10622731</span>
<span class="co"># 9 2010 0.05660377 NA NA 53 0.05660377 0.11598223</span>
<span class="co"># 10 2011 0.18279570 NA NA 93 0.18279570 0.12650615</span>
<span class="co"># 11 2012 0.30769231 NA NA 65 0.30769231 0.13783610</span>
<span class="co"># 12 2013 0.06896552 NA NA 58 0.06896552 0.15000651</span>
<span class="co"># 13 2014 0.10000000 NA NA 60 0.10000000 0.16304829</span>
<span class="co"># 14 2015 0.23636364 NA NA 55 0.23636364 0.17698785</span>
<span class="co"># 15 2016 0.22619048 NA NA 84 0.22619048 0.19184597</span>
<span class="co"># 16 2017 0.16279070 NA NA 86 0.16279070 0.20763675</span>
<span class="co"># 17 2018 0.22436641 0.1938710 0.2548618 NA NA 0.22436641</span>
<span class="co"># 18 2019 0.24203228 0.2062911 0.2777735 NA NA 0.24203228</span>
<span class="co"># 19 2020 0.26062172 0.2191758 0.3020676 NA NA 0.26062172</span>
<span class="co"># 20 2021 0.28011130 0.2325557 0.3276669 NA NA 0.28011130</span>
<span class="co"># 21 2022 0.30046606 0.2464567 0.3544755 NA NA 0.30046606</span>
<span class="co"># 22 2023 0.32163907 0.2609011 0.3823771 NA NA 0.32163907</span>
<span class="co"># 23 2024 0.34357130 0.2759081 0.4112345 NA NA 0.34357130</span>
<span class="co"># 24 2025 0.36619175 0.2914934 0.4408901 NA NA 0.36619175</span>
<span class="co"># 25 2026 0.38941799 0.3076686 0.4711674 NA NA 0.38941799</span>
<span class="co"># 26 2027 0.41315710 0.3244399 0.5018743 NA NA 0.41315710</span>
<span class="co"># 27 2028 0.43730688 0.3418075 0.5328063 NA NA 0.43730688</span>
<span class="co"># 28 2029 0.46175755 0.3597639 0.5637512 NA NA 0.46175755</span>
<span class="co"># 29 2030 0.48639359 0.3782932 0.5944939 NA NA 0.48639359</span></pre></body></html></div>
<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>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.html">plot</a></span>(<span class="no">predict_TZP</span>)</pre></body></html></div>
<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">predict_TZP</span>)</pre></body></html></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
<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>
@ -272,7 +273,7 @@ predict_TZP %
<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>
<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>
<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="no">mo</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="kw">NULL</span>) <span class="kw">==</span> <span class="st">"Gram-positive"</span>) <span class="kw">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="kw">col_ab</span> <span class="kw">=</span> <span class="st">"VAN"</span>, <span class="kw">year_min</span> <span class="kw">=</span> <span class="fl">2010</span>, <span class="kw">info</span> <span class="kw">=</span> <span class="fl">FALSE</span>, <span class="kw">model</span> <span class="kw">=</span> <span class="st">"binomial"</span>) <span class="kw">%&gt;%</span>
@ -317,7 +318,7 @@ predict_TZP %
</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>
<p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate since no binomial distribution is to be expected based on the observed years:</p>
<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span>(<span class="no">mo</span>, <span class="kw">language</span> <span class="kw">=</span> <span class="kw">NULL</span>) <span class="kw">==</span> <span class="st">"Gram-positive"</span>) <span class="kw">%&gt;%</span>
<span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="kw">col_ab</span> <span class="kw">=</span> <span class="st">"VAN"</span>, <span class="kw">year_min</span> <span class="kw">=</span> <span class="fl">2010</span>, <span class="kw">info</span> <span class="kw">=</span> <span class="fl">FALSE</span>, <span class="kw">model</span> <span class="kw">=</span> <span class="st">"linear"</span>) <span class="kw">%&gt;%</span>
@ -334,9 +335,9 @@ predict_TZP %
<span class="co"># Link function: logit</span>
<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">model</span>)$<span class="no">coefficients</span>
<span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span>
<span class="co"># (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span>
<span class="co"># year 0.1106102 0.0238753 4.632831 3.606990e-06</span></pre></body></html></div>
<span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span>
<span class="co"># (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05</span>
<span class="co"># year 0.09883005 0.02295317 4.305725 1.664395e-05</span></pre></body></html></div>
</div>
</div>
</div>
@ -356,7 +357,7 @@ predict_TZP %
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.0.</p>
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
</div>
</footer>

BIN
docs/articles/resistance_predict_files/figure-html/unnamed-chunk-4-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 96 KiB

After

Width:  |  Height:  |  Size: 96 KiB

BIN
docs/articles/resistance_predict_files/figure-html/unnamed-chunk-5-1.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 94 KiB

After

Width:  |  Height:  |  Size: 94 KiB

BIN
docs/articles/resistance_predict_files/figure-html/unnamed-chunk-5-2.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 92 KiB

After

Width:  |  Height:  |  Size: 92 KiB