P281424
/
AMR
mirror of https://github.com/msberends/AMR.git
1
0
Fork 0
Code Releases Activity
AMR/docs/articles/resistance_predict.html

409 lines
28 KiB
HTML
Raw Normal View History

resistance predict update
2019-02-11 10:27:10 +01:00
<!DOCTYPE html>
<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>How to predict antimicrobial resistance • AMR (for R)</title>
<!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png">
<link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png">
<link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png">
<link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png">
<link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png">
<link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png">
<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/flatly/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
<script src="../pkgdown.js"></script><!-- docsearch --><script src="../docsearch.js"></script><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/docsearch.js/2.6.1/docsearch.min.css" integrity="sha256-QOSRU/ra9ActyXkIBbiIB144aDBdtvXBcNc3OTNuX/Q=" crossorigin="anonymous">
<link href="../docsearch.css" rel="stylesheet">
<script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/jquery.mark.min.js" integrity="sha256-4HLtjeVgH0eIB3aZ9mLYF6E8oU5chNdjU6p6rrXpl9U=" crossorigin="anonymous"></script><link href="../extra.css" rel="stylesheet">
<script src="../extra.js"></script><meta property="og:title" content="How to predict antimicrobial resistance">
<meta property="og:description" content="">
<meta property="og:image" content="https://msberends.gitlab.io/AMR/logo.png">
<meta name="twitter:card" content="summary">
<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
<![endif]-->
</head>
<body>
<div class="container template-article">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
(v0.7.1.9067) honor info
2019-08-28 17:12:48 +02:00
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9067</span>
resistance predict update
2019-02-11 10:27:10 +01:00
</span>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="../index.html">
<span class="fa fa-home"></span>
Home
</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
<span class="fa fa-question-circle"></span>
How to
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/AMR.html">
<span class="fa fa-directions"></span>
Conduct AMR analysis
</a>
</li>
<li>
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
</a>
</li>
added mdr_tb()
2019-05-23 16:58:59 +02:00
<li>
<a href="../articles/MDR.html">
<span class="fa fa-skull-crossbones"></span>
Determine multi-drug resistance (MDR)
</a>
</li>
resistance predict update
2019-02-11 10:27:10 +01:00
<li>
<a href="../articles/WHONET.html">
<span class="fa fa-globe-americas"></span>
Work with WHONET data
</a>
</li>
SPSS tutorial
2019-02-14 15:18:17 +01:00
<li>
<a href="../articles/SPSS.html">
<span class="fa fa-file-upload"></span>
Import data from SPSS/SAS/Stata
</a>
</li>
resistance predict update
2019-02-11 10:27:10 +01:00
<li>
<a href="../articles/EUCAST.html">
<span class="fa fa-exchange-alt"></span>
Apply EUCAST rules
</a>
</li>
<li>
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
new antibiotics
2019-05-10 16:44:59 +02:00
<a href="../reference/ab_property.html">
resistance predict update
2019-02-11 10:27:10 +01:00
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
</a>
</li>
<li>
<a href="../articles/benchmarks.html">
<span class="fa fa-shipping-fast"></span>
Other: benchmarks
</a>
</li>
</ul>
</li>
<li>
<a href="../reference/">
<span class="fa fa-book-open"></span>
Manual
</a>
</li>
<li>
<a href="../authors.html">
<span class="fa fa-users"></span>
Authors
</a>
</li>
<li>
<a href="../news/">
<span class="far fa far fa-newspaper"></span>
Changelog
</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://gitlab.com/msberends/AMR">
<span class="fab fa fab fa-gitlab"></span>
Source Code
</a>
</li>
<li>
<a href="../LICENSE-text.html">
<span class="fa fa-book"></span>
Licence
</a>
</li>
</ul>
<form class="navbar-form navbar-right" role="search">
<div class="form-group">
<input type="search" class="form-control" name="search-input" id="search-input" placeholder="Search..." aria-label="Search for..." autocomplete="off">
</div>
</form>
</div>
<!--/.nav-collapse -->
</div>
<!--/.container -->
</div>
<!--/.navbar -->
</header><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
(v0.7.1.9067) honor info
2019-08-28 17:12:48 +02:00
<h4 class="date">28 August 2019</h4>
resistance predict update
2019-02-11 10:27:10 +01:00
<div class="hidden name"><code>resistance_predict.Rmd</code></div>
</div>
<div id="needed-r-packages" class="section level2">
<h2 class="hasAnchor">
<a href="#needed-r-packages" class="anchor"></a>Needed R packages</h2>
(v0.6.1.9053) prerelease fixes
2019-06-02 19:23:19 +02:00
<p>As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the <a href="https://www.tidyverse.org">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
resistance predict update
2019-02-11 10:27:10 +01:00
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(dplyr)</a>
<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(ggplot2)</a>
<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/library">library</a></span>(AMR)</a>
<a class="sourceLine" id="cb1-4" data-line-number="4"></a>
<a class="sourceLine" id="cb1-5" data-line-number="5"><span class="co"># (if not yet installed, install with:)</span></a>
<a class="sourceLine" id="cb1-6" data-line-number="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
</div>
<div id="prediction-analysis" class="section level2">
<h2 class="hasAnchor">
<a href="#prediction-analysis" class="anchor"></a>Prediction analysis</h2>
<p>Our package contains a function <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>, which takes the same input as functions for <a href="./AMR.html">other AMR analysis</a>. Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.</p>
<p>It is basically as easy as:</p>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1"><span class="co"># resistance prediction of piperacillin/tazobactam (TZP):</span></a>
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> example_isolates, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"TZP"</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>)</a>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<a class="sourceLine" id="cb2-3" data-line-number="3"></a>
<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co"># or:</span></a>
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<a class="sourceLine" id="cb2-5" data-line-number="5">example_isolates <span class="op">%&gt;%</span><span class="st"> </span></a>
(v0.7.1.9030) eucast_rules() fix
2019-08-08 15:52:07 +02:00
<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-7" data-line-number="7"> model <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb2-8" data-line-number="8"></a>
<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="co"># to bind it to object 'predict_TZP' for example:</span></a>
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<a class="sourceLine" id="cb2-10" data-line-number="10">predict_TZP &lt;-<span class="st"> </span>example_isolates <span class="op">%&gt;%</span><span class="st"> </span></a>
(v0.7.1.9030) eucast_rules() fix
2019-08-08 15:52:07 +02:00
<a class="sourceLine" id="cb2-11" data-line-number="11"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"TZP"</span>,</a>
<a class="sourceLine" id="cb2-12" data-line-number="12"> <span class="dt">model =</span> <span class="st">"binomial"</span>)</a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
<p>When running any of these commands, a summary of the regression model will be printed unless using <code><a href="../reference/resistance_predict.html">resistance_predict(..., info = FALSE)</a></code>.</p>
speed improvement eucast_rules(), support more old MO codes
2019-05-20 12:00:18 +02:00
<pre><code># NOTE: Using column `date` as input for `col_date`.
#
# Logistic regression model (logit) with binomial distribution
# ------------------------------------------------------------
#
# Call:
# glm(formula = df_matrix ~ year, family = binomial)
#
# Deviance Residuals:
# Min 1Q Median 3Q Max
# -2.6817 -1.4087 -0.5657 0.9672 3.5728
#
# Coefficients:
# Estimate Std. Error z value Pr(&gt;|z|)
# (Intercept) -224.39872 48.03354 -4.672 2.99e-06 ***
# year 0.11061 0.02388 4.633 3.61e-06 ***
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# (Dispersion parameter for binomial family taken to be 1)
#
# Null deviance: 61.512 on 14 degrees of freedom
# Residual deviance: 38.692 on 13 degrees of freedom
# AIC: 95.212
#
# Number of Fisher Scoring iterations: 4</code></pre>
resistance predict update
2019-02-11 10:27:10 +01:00
<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>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">predict_TZP</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co"># year value se_min se_max observations observed estimated</span></a>
<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span></a>
<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span></a>
<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co"># 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span></a>
<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span></a>
<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span></a>
<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span></a>
<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span></a>
<a class="sourceLine" id="cb4-10" data-line-number="10"><span class="co"># 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span></a>
<a class="sourceLine" id="cb4-11" data-line-number="11"><span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span></a>
<a class="sourceLine" id="cb4-12" data-line-number="12"><span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span></a>
<a class="sourceLine" id="cb4-13" data-line-number="13"><span class="co"># 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span></a>
<a class="sourceLine" id="cb4-14" data-line-number="14"><span class="co"># 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span></a>
<a class="sourceLine" id="cb4-15" data-line-number="15"><span class="co"># 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span></a>
<a class="sourceLine" id="cb4-16" data-line-number="16"><span class="co"># 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span></a>
<a class="sourceLine" id="cb4-17" data-line-number="17"><span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span></a>
<a class="sourceLine" id="cb4-18" data-line-number="18"><span class="co"># 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span></a>
<a class="sourceLine" id="cb4-19" data-line-number="19"><span class="co"># 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span></a>
<a class="sourceLine" id="cb4-20" data-line-number="20"><span class="co"># 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span></a>
<a class="sourceLine" id="cb4-21" data-line-number="21"><span class="co"># 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span></a>
<a class="sourceLine" id="cb4-22" data-line-number="22"><span class="co"># 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span></a>
<a class="sourceLine" id="cb4-23" data-line-number="23"><span class="co"># 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span></a>
<a class="sourceLine" id="cb4-24" data-line-number="24"><span class="co"># 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span></a>
<a class="sourceLine" id="cb4-25" data-line-number="25"><span class="co"># 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span></a>
<a class="sourceLine" id="cb4-26" data-line-number="26"><span class="co"># 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span></a>
<a class="sourceLine" id="cb4-27" data-line-number="27"><span class="co"># 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span></a>
<a class="sourceLine" id="cb4-28" data-line-number="28"><span class="co"># 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a>
<a class="sourceLine" id="cb4-29" data-line-number="29"><span class="co"># 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<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>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_TZP)</a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<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>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_TZP)</a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"></a>
<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co"># choose for error bars instead of a ribbon</span></a>
<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_TZP, <span class="dt">ribbon =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-2.png" width="720"></p>
<div id="choosing-the-right-model" class="section level3">
<h3 class="hasAnchor">
<a href="#choosing-the-right-model" class="anchor"></a>Choosing the right model</h3>
<p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram positives, the spread (i.e. standard error) is enormous:</p>
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">example_isolates <span class="op">%&gt;%</span></a>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo, <span class="dt">language =</span> <span class="ot">NULL</span>) <span class="op">==</span><span class="st"> "Gram-positive"</span>) <span class="op">%&gt;%</span></a>
(v0.7.1.9030) eucast_rules() fix
2019-08-08 15:52:07 +02:00
<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"binomial"</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a>
<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<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>
(v0.7.1.9030) eucast_rules() fix
2019-08-08 15:52:07 +02:00
<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>
resistance predict update
2019-02-11 10:27:10 +01:00
<p>Valid values are:</p>
<table class="table">
<colgroup>
<col width="32%">
<col width="25%">
<col width="42%">
</colgroup>
<thead><tr class="header">
<th>Input values</th>
<th>Function used by R</th>
<th>Type of model</th>
</tr></thead>
<tbody>
<tr class="odd">
<td>
<code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = binomial)</a></code></td>
<td>Generalised linear model with binomial distribution</td>
</tr>
<tr class="even">
<td>
<code>"loglin"</code> or <code>"poisson"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/glm">glm(..., family = poisson)</a></code></td>
<td>Generalised linear model with poisson distribution</td>
</tr>
<tr class="odd">
<td>
<code>"lin"</code> or <code>"linear"</code>
</td>
<td><code><a href="https://www.rdocumentation.org/packages/stats/topics/lm">lm()</a></code></td>
<td>Linear model</td>
</tr>
</tbody>
</table>
<p>For the vancomycin resistance in Gram positive bacteria, a linear model might be more appropriate since no (left half of a) binomial distribution is to be expected based on the observed years:</p>
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">example_isolates <span class="op">%&gt;%</span></a>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<a class="sourceLine" id="cb9-2" data-line-number="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo, <span class="dt">language =</span> <span class="ot">NULL</span>) <span class="op">==</span><span class="st"> "Gram-positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-3" data-line-number="3"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">col_ab =</span> <span class="st">"VAN"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>, <span class="dt">model =</span> <span class="st">"linear"</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb9-4" data-line-number="4"><span class="st"> </span><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>()</a>
<a class="sourceLine" id="cb9-5" data-line-number="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
<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>
(v0.7.1.90012) mo_shortname fix
2019-07-10 21:36:51 +02:00
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">model &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/attributes">attributes</a></span>(predict_TZP)<span class="op">$</span>model</a>
<a class="sourceLine" id="cb10-2" data-line-number="2"></a>
<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>family</a>
<a class="sourceLine" id="cb10-4" data-line-number="4"><span class="co"># </span></a>
<a class="sourceLine" id="cb10-5" data-line-number="5"><span class="co"># Family: binomial </span></a>
<a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co"># Link function: logit</span></a>
<a class="sourceLine" id="cb10-7" data-line-number="7"></a>
<a class="sourceLine" id="cb10-8" data-line-number="8"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>coefficients</a>
<a class="sourceLine" id="cb10-9" data-line-number="9"><span class="co"># Estimate Std. Error z value Pr(&gt;|z|)</span></a>
<a class="sourceLine" id="cb10-10" data-line-number="10"><span class="co"># (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a>
<a class="sourceLine" id="cb10-11" data-line-number="11"><span class="co"># year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div>
resistance predict update
2019-02-11 10:27:10 +01:00
</div>
</div>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<div id="tocnav">
<h2 class="hasAnchor">
<a href="#tocnav" class="anchor"></a>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#needed-r-packages">Needed R packages</a></li>
<li><a href="#prediction-analysis">Prediction analysis</a></li>
</ul>
</div>
</div>
</div>
<footer><div class="copyright">
(v0.7.1.9063) septic_patients -> example_isolates
2019-08-27 16:45:42 +02:00
<p>Developed by <a href="https://www.rug.nl/staff/m.s.berends/">Matthijs S. Berends</a>, <a href="https://www.rug.nl/staff/c.f.luz/">Christian F. Luz</a>, <a href="https://www.rug.nl/staff/a.w.friedrich/">Alex W. Friedrich</a>, <a href="https://www.rug.nl/staff/b.sinha/">Bhanu N. M. Sinha</a>, <a href="https://www.rug.nl/staff/c.j.albers/">Casper J. Albers</a>, <a href="https://www.rug.nl/staff/c.glasner/">Corinna Glasner</a>.</p>
resistance predict update
2019-02-11 10:27:10 +01:00
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.3.0.</p>
</div>
</footer>
</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/docsearch.js/2.6.1/docsearch.min.js" integrity="sha256-GKvGqXDznoRYHCwKXGnuchvKSwmx9SRMrZOTh2g4Sb0=" crossorigin="anonymous"></script><script>
docsearch({
apiKey: 'f737050abfd4d726c63938e18f8c496e',
indexName: 'amr',
inputSelector: 'input#search-input.form-control',
transformData: function(hits) {
return hits.map(function (hit) {
hit.url = updateHitURL(hit);
return hit;
});
}
});
</script>
</body>
</html>