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resistance predict update

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dr. M.S. (Matthijs) Berends 2019-02-11 10:27:10 +01:00
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commit 76ed26d27e
39 changed files with 1296 additions and 549 deletions
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2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.5.0.9017
Date: 2019-02-10
Date: 2019-02-11
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

1
NAMESPACE
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@ -253,6 +253,7 @@ importFrom(graphics,axis)
importFrom(graphics,barplot)
importFrom(graphics,hist)
importFrom(graphics,plot)
importFrom(graphics,points)
importFrom(graphics,text)
importFrom(hms,is.hms)
importFrom(knitr,kable)

64
R/resistance_predict.R
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@ -35,6 +35,7 @@
#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.
#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
#' @param main title of the plot
#' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars
#' @details Valid options for the statistical model are:
#' \itemize{
#' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
@ -51,6 +52,7 @@
#' \item{\code{observed}, the original observed resistant percentages}
#' \item{\code{estimated}, the estimated resistant percentages, calculated by the model}
#' }
#' Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples.
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
#' @rdname resistance_predict
#' @export
@ -71,7 +73,12 @@
#' plot(x)
#'
#'
#' # create nice plots with ggplot yourself
#' # get the model from the object
#' mymodel <- attributes(x)$model
#' summary(mymodel)
#'
#'
#' # create nice plots with ggplot2 yourself
#' if (!require(ggplot2)) {
#'
#' data <- septic_patients %>%
@ -295,8 +302,8 @@ rsi_predict <- resistance_predict
#' @exportMethod plot.mic
#' @export
#' @importFrom dplyr %>% group_by summarise
#' @importFrom graphics plot axis arrows
#' @importFrom dplyr filter
#' @importFrom graphics plot axis arrows points
#' @rdname resistance_predict
plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
if (attributes(x)$I_as_R == TRUE) {
@ -316,18 +323,30 @@ plot.resistance_predict <- function(x, main = paste("Resistance prediction of",
", model: ", attributes(x)$model_title, ")"),
cex.sub = 0.75)
axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
# arrows hack: https://stackoverflow.com/a/22037078/4575331
# hack for error bars: https://stackoverflow.com/a/22037078/4575331
arrows(x0 = x$year,
y0 = x$se_min,
x1 = x$year,
y1 = x$se_max, length = 0.05, angle = 90, code = 3)
y1 = x$se_max,
length = 0.05, angle = 90, code = 3, lwd = 1.5)
# overlay grey points for prediction
points(x = filter(x, is.na(observations))$year,
y = filter(x, is.na(observations))$value,
pch = 19,
col = "grey40")
}
#' @rdname resistance_predict
#' @importFrom dplyr filter
#' @export
ggplot_rsi_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
ggplot_rsi_predict <- function(x,
main = paste("Resistance prediction of", attributes(x)$ab),
ribbon = TRUE,
...) {
if (!"resistance_predict" %in% class(x)) {
stop("`x` must be a resistance prediction model created with resistance_predict().")
@ -338,15 +357,26 @@ ggplot_rsi_predict <- function(x, main = paste("Resistance prediction of", attri
} else {
ylab <- "%R"
}
suppressWarnings(
ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
ggplot2::geom_point(size = 2) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5) +
scale_y_percent(limits = c(0, 1)) +
ggplot2::labs(title = main,
y = paste0("Percentage (", ylab, ")"),
x = "Year",
caption = paste0("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"))
)
p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
ggplot2::geom_point(data = filter(x, !is.na(observations)),
size = 2) +
scale_y_percent(limits = c(0, 1)) +
ggplot2::labs(title = main,
y = paste0("Percentage (", ylab, ")"),
x = "Year",
caption = paste0("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"))
if (ribbon == TRUE) {
p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25)
} else {
p <- p + ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5)
}
p <- p +
# overlay grey points for prediction
ggplot2::geom_point(data = filter(x, is.na(observations)),
size = 2,
colour = "grey40")
p
}

10
README.md
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@ -23,11 +23,11 @@ Bhanu N.M. Sinha <a href="https://orcid.org/0000-0003-1634-0010"><img src="https
<sup>a</sup> Thesis dissertant<br>
<sup>b</sup> Thesis advisor
<a href="https://www.rug.nl"><img src="docs/logo_rug.png" height="60px"></a>
<a href="https://www.umcg.nl"><img src="docs/logo_umcg.png" height="60px"></a>
<a href="https://www.certe.nl"><img src="docs/logo_certe.png" height="60px"></a>
<a href="http://www.eurhealth-1health.eu"><img src="docs/logo_eh1h.png" height="60px"></a>
<a href="http://www.eurhealth-1health.eu"><img src="docs/logo_interreg.png" height="60px"></a>
<a href="https://www.rug.nl"><img src="pkgdown/logos/logo_rug.png" height="60px"></a>
<a href="https://www.umcg.nl"><img src="pkgdown/logos/logo_umcg.png" height="60px"></a>
<a href="https://www.certe.nl"><img src="pkgdown/logos/logo_certe.png" height="60px"></a>
<a href="http://www.eurhealth-1health.eu"><img src="pkgdown/logos/logo_eh1h.png" height="60px"></a>
<a href="http://www.eurhealth-1health.eu"><img src="pkgdown/logos/logo_interreg.png" height="60px"></a>
## How to get this package
All stable versions of this package [are published on CRAN](https://CRAN.R-project.org/package=AMR), the official R network with a peer-reviewed submission process.

8
_pkgdown.yml
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@ -37,7 +37,7 @@ navbar:
href: 'articles/AMR.html'
- text: 'Predict antimicrobial resistance'
icon: 'fa-dice'
href: 'articles/Predict.html'
href: 'articles/resistance_predict.html'
- text: 'Work with WHONET data'
icon: 'fa-globe-americas'
href: 'articles/WHONET.html'
@ -46,10 +46,12 @@ navbar:
href: 'articles/EUCAST.html'
- text: 'Get properties of a microorganism'
icon: 'fa-bug'
href: 'articles/mo_property.html'
# href: 'articles/mo_property.html'
href: 'reference/mo_property.html'
- text: 'Get properties of an antibiotic'
icon: 'fa-capsules'
href: 'articles/ab_property.html'
# href: 'articles/atc_property.html'
href: 'reference/atc_property.html'
- text: 'Create frequency tables'
icon: 'fa-sort-amount-down'
href: 'articles/freq.html'

6
docs/LICENSE-text.html
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@ -108,7 +108,7 @@
</a>
</li>
<li>
<a href="articles/Predict.html">
<a href="articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -129,14 +129,14 @@
</a>
</li>
<li>
<a href="articles/mo_property.html">
<a href="reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="articles/ab_property.html">
<a href="reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic

841
docs/articles/AMR.html
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8
docs/articles/EUCAST.html
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@ -70,7 +70,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -91,14 +91,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -185,7 +185,7 @@
<h1>How to apply EUCAST rules</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">09 February 2019</h4>
<h4 class="date">11 February 2019</h4>
<div class="hidden name"><code>EUCAST.Rmd</code></div>

233
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@ -0,0 +1,233 @@
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<h1>How to get properties of an antibiotic</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">11 February 2019</h4>
<div class="hidden name"><code>atc_property.Rmd</code></div>
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<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/c.glasner/">Corinna Glasner</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>.</p>
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@ -108,7 +108,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -129,14 +129,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
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<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -232,12 +232,12 @@
<li><a href="AMR.html">How to conduct AMR analysis</a></li>
<li><a href="EUCAST.html">How to apply EUCAST rules</a></li>
<li><a href="G_test.html">How to use the *G*-test</a></li>
<li><a href="Predict.html">How to predict antimicrobial resistance</a></li>
<li><a href="WHONET.html">How to work with WHONET data</a></li>
<li><a href="ab_property.html">How to get properties of an antibiotic</a></li>
<li><a href="atc_property.html">How to get properties of an antibiotic</a></li>
<li><a href="benchmarks.html">Benchmarks</a></li>
<li><a href="freq.html">How to create frequency tables</a></li>
<li><a href="mo_property.html">How to get properties of a microorganism</a></li>
<li><a href="resistance_predict.html">How to predict antimicrobial resistance</a></li>
</ul>
</div>
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8
docs/articles/mo_property.html
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@ -70,7 +70,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -91,14 +91,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -185,7 +185,7 @@
<h1>How to get properties of a microorganism</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">09 February 2019</h4>
<h4 class="date">11 February 2019</h4>
<div class="hidden name"><code>mo_property.Rmd</code></div>

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@ -0,0 +1,406 @@
<!DOCTYPE html>
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<h1>How to predict antimicrobial resistance</h1>
<h4 class="author">Matthijs S. Berends</h4>
<h4 class="date">11 February 2019</h4>
<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>
<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 <a href="https://www.linkedin.com/in/hadleywickham/">Dr Hadley Wickham</a>. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
<p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="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" title="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" title="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" title="4"></a>
<a class="sourceLine" id="cb1-5" title="5"><span class="co"># (if not yet installed, install with:)</span></a>
<a class="sourceLine" id="cb1-6" title="6"><span class="co"># install.packages(c("tidyverse", "AMR"))</span></a></code></pre></div>
</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>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="co"># resistance prediction of piperacillin/tazobactam (pita):</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw"><a href="../reference/resistance_predict.html">resistance_predict</a></span>(<span class="dt">tbl =</span> septic_patients, <span class="dt">col_date =</span> <span class="st">"date"</span>, <span class="dt">col_ab =</span> <span class="st">"pita"</span>)</a>
<a class="sourceLine" id="cb2-3" title="3"></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="co"># or:</span></a>
<a class="sourceLine" id="cb2-5" title="5">septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-6" title="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">"pita"</span>)</a>
<a class="sourceLine" id="cb2-7" title="7"></a>
<a class="sourceLine" id="cb2-8" title="8"><span class="co"># to bind it to object 'predict_pita' for example:</span></a>
<a class="sourceLine" id="cb2-9" title="9">predict_pita &lt;-<span class="st"> </span>septic_patients <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-10" title="10"><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">"pita"</span>)</a></code></pre></div>
<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>
<pre><code>#&gt; NOTE: Using column `date` as input for `col_date`.
#&gt;
#&gt; Logistic regression model (logit) with binomial distribution
#&gt; ------------------------------------------------------------
#&gt;
#&gt; Call:
#&gt; glm(formula = df_matrix ~ year, family = binomial)
#&gt;
#&gt; Deviance Residuals:
#&gt; Min 1Q Median 3Q Max
#&gt; -2.9224 -1.3120 0.0170 0.7586 3.1932
#&gt;
#&gt; Coefficients:
#&gt; Estimate Std. Error z value Pr(&gt;|z|)
#&gt; (Intercept) -222.92857 45.93922 -4.853 1.22e-06 ***
#&gt; year 0.10994 0.02284 4.814 1.48e-06 ***
#&gt; ---
#&gt; Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#&gt;
#&gt; (Dispersion parameter for binomial family taken to be 1)
#&gt;
#&gt; Null deviance: 59.794 on 14 degrees of freedom
#&gt; Residual deviance: 35.191 on 13 degrees of freedom
#&gt; AIC: 93.464
#&gt;
#&gt; Number of Fisher Scoring iterations: 4</code></pre>
<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"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">predict_pita</a>
<a class="sourceLine" id="cb4-2" title="2"><span class="co">#&gt; year value se_min se_max observations observed estimated</span></a>
<a class="sourceLine" id="cb4-3" title="3"><span class="co">#&gt; 1 2003 0.06250000 NA NA 32 0.06250000 0.06177594</span></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="co">#&gt; 2 2004 0.08536585 NA NA 82 0.08536585 0.06846343</span></a>
<a class="sourceLine" id="cb4-5" title="5"><span class="co">#&gt; 3 2005 0.10000000 NA NA 60 0.10000000 0.07581637</span></a>
<a class="sourceLine" id="cb4-6" title="6"><span class="co">#&gt; 4 2006 0.05084746 NA NA 59 0.05084746 0.08388789</span></a>
<a class="sourceLine" id="cb4-7" title="7"><span class="co">#&gt; 5 2007 0.12121212 NA NA 66 0.12121212 0.09273250</span></a>
<a class="sourceLine" id="cb4-8" title="8"><span class="co">#&gt; 6 2008 0.04166667 NA NA 72 0.04166667 0.10240539</span></a>
<a class="sourceLine" id="cb4-9" title="9"><span class="co">#&gt; 7 2009 0.01639344 NA NA 61 0.01639344 0.11296163</span></a>
<a class="sourceLine" id="cb4-10" title="10"><span class="co">#&gt; 8 2010 0.09433962 NA NA 53 0.09433962 0.12445516</span></a>
<a class="sourceLine" id="cb4-11" title="11"><span class="co">#&gt; 9 2011 0.18279570 NA NA 93 0.18279570 0.13693759</span></a>
<a class="sourceLine" id="cb4-12" title="12"><span class="co">#&gt; 10 2012 0.30769231 NA NA 65 0.30769231 0.15045682</span></a>
<a class="sourceLine" id="cb4-13" title="13"><span class="co">#&gt; 11 2013 0.08620690 NA NA 58 0.08620690 0.16505550</span></a>
<a class="sourceLine" id="cb4-14" title="14"><span class="co">#&gt; 12 2014 0.15254237 NA NA 59 0.15254237 0.18076926</span></a>
<a class="sourceLine" id="cb4-15" title="15"><span class="co">#&gt; 13 2015 0.27272727 NA NA 55 0.27272727 0.19762493</span></a>
<a class="sourceLine" id="cb4-16" title="16"><span class="co">#&gt; 14 2016 0.25000000 NA NA 84 0.25000000 0.21563859</span></a>
<a class="sourceLine" id="cb4-17" title="17"><span class="co">#&gt; 15 2017 0.16279070 NA NA 86 0.16279070 0.23481370</span></a>
<a class="sourceLine" id="cb4-18" title="18"><span class="co">#&gt; 16 2018 0.25513926 0.2228376 0.2874409 NA NA 0.25513926</span></a>
<a class="sourceLine" id="cb4-19" title="19"><span class="co">#&gt; 17 2019 0.27658825 0.2386811 0.3144954 NA NA 0.27658825</span></a>
<a class="sourceLine" id="cb4-20" title="20"><span class="co">#&gt; 18 2020 0.29911630 0.2551715 0.3430611 NA NA 0.29911630</span></a>
<a class="sourceLine" id="cb4-21" title="21"><span class="co">#&gt; 19 2021 0.32266085 0.2723340 0.3729877 NA NA 0.32266085</span></a>
<a class="sourceLine" id="cb4-22" title="22"><span class="co">#&gt; 20 2022 0.34714076 0.2901847 0.4040968 NA NA 0.34714076</span></a>
<a class="sourceLine" id="cb4-23" title="23"><span class="co">#&gt; 21 2023 0.37245666 0.3087318 0.4361815 NA NA 0.37245666</span></a>
<a class="sourceLine" id="cb4-24" title="24"><span class="co">#&gt; 22 2024 0.39849187 0.3279750 0.4690088 NA NA 0.39849187</span></a>
<a class="sourceLine" id="cb4-25" title="25"><span class="co">#&gt; 23 2025 0.42511415 0.3479042 0.5023241 NA NA 0.42511415</span></a>
<a class="sourceLine" id="cb4-26" title="26"><span class="co">#&gt; 24 2026 0.45217796 0.3684992 0.5358568 NA NA 0.45217796</span></a>
<a class="sourceLine" id="cb4-27" title="27"><span class="co">#&gt; 25 2027 0.47952757 0.3897276 0.5693275 NA NA 0.47952757</span></a>
<a class="sourceLine" id="cb4-28" title="28"><span class="co">#&gt; 26 2028 0.50700045 0.4115444 0.6024565 NA NA 0.50700045</span></a>
<a class="sourceLine" id="cb4-29" title="29"><span class="co">#&gt; 27 2029 0.53443111 0.4338908 0.6349714 NA NA 0.53443111</span></a></code></pre></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"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot</a></span>(predict_pita)</a></code></pre></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>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita)</a></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="co"># choose for error bars instead of a ribbon</span></a>
<a class="sourceLine" id="cb7-3" title="3"><span class="kw"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span>(predict_pita, <span class="dt">ribbon =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
<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>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-2" title="2"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/dplyr/topics/filter">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb8-3" title="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">"vanc"</span>, <span class="dt">year_min =</span> <span class="dv">2010</span>, <span class="dt">info =</span> <span class="ot">FALSE</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb8-4" title="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" title="5"><span class="co">#&gt; </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
<p>You can define the model with the <code>model</code> parameter. The default model 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>
<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>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1">septic_patients <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-2" title="2"><span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/dplyr/topics/filter">filter</a></span>(<span class="kw"><a href="../reference/mo_property.html">mo_gramstain</a></span>(mo) <span class="op">==</span><span class="st"> "Gram positive"</span>) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb9-3" title="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">"vanc"</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" title="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" title="5"><span class="co">#&gt; </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
<p>This seems more likely, doesn’t it?</p>
<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">model &lt;-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/attributes">attributes</a></span>(predict_pita)<span class="op">$</span>model</a>
<a class="sourceLine" id="cb10-2" title="2"></a>
<a class="sourceLine" id="cb10-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>family</a>
<a class="sourceLine" id="cb10-4" title="4"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb10-5" title="5"><span class="co">#&gt; Family: binomial </span></a>
<a class="sourceLine" id="cb10-6" title="6"><span class="co">#&gt; Link function: logit</span></a>
<a class="sourceLine" id="cb10-7" title="7"></a>
<a class="sourceLine" id="cb10-8" title="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" title="9"><span class="co">#&gt; Estimate Std. Error z value Pr(&gt;|z|)</span></a>
<a class="sourceLine" id="cb10-10" title="10"><span class="co">#&gt; (Intercept) -222.9285736 45.93922388 -4.852685 1.218012e-06</span></a>
<a class="sourceLine" id="cb10-11" title="11"><span class="co">#&gt; year 0.1099391 0.02283501 4.814500 1.475690e-06</span></a></code></pre></div>
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6
docs/authors.html
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@ -108,7 +108,7 @@
</a>
</li>
<li>
<a href="articles/Predict.html">
<a href="articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -129,14 +129,14 @@
</a>
</li>
<li>
<a href="articles/mo_property.html">
<a href="reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="articles/ab_property.html">
<a href="reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic

10
docs/extra.css
View File

@ -23,12 +23,12 @@
/* R for Data Science (r4ds) */
#r4ds a {
display: inline;
display: inline-flex;
align-items: center;
text-align: right;
}
#r4ds * {
text-align: center;
display: block;
margin: 0 auto;
#r4ds img {
margin-left: 10px;
}
/* class for footer */

22
docs/extra.js
View File

@ -34,15 +34,19 @@ $( document ).ready(function() {
window.location.replace(url_new);
}
$('#sidebar').prepend(
'<div id="r4ds">' +
' <a href="https://r4ds.had.co.nz/">' +
' <img src="../../AMR/cover_r4ds.png" width="25%">' +
' </a>' +
' <p>Learn R reading this great book!</p>' +
' <p>Or read it free online: <a href="https://r4ds.had.co.nz/" target="_blank">r4ds.co.nz</a>.</p>' +
' <hr>' +
'</div>');
// PR for 'R for Data Science' on How To pages
if ($(".template-article").length > 0) {
$('#sidebar').prepend(
'<div id="r4ds">' +
' <a href="https://r4ds.had.co.nz/">Learn R reading this great book: R for Data Science.' +
' <br>' +
' <br>' +
' Click to read it online - it was published for free.' +
' <img src="../../AMR/cover_r4ds.png" height="100px">' +
' </a> ' +
' <hr>' +
'</div>');
}
$('footer').html(
'<div>' +

8
docs/index.html
View File

@ -72,7 +72,7 @@
</a>
</li>
<li>
<a href="articles/Predict.html">
<a href="articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -93,14 +93,14 @@
</a>
</li>
<li>
<a href="articles/mo_property.html">
<a href="reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="articles/ab_property.html">
<a href="reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -195,7 +195,7 @@
<p>This package can be used for:</p>
<ul>
<li>Calculating antimicrobial resistance</li>
<li>Calculating empiric susceptibility of both mono therapy and combination therapy</li>
<li>Calculating empirical susceptibility of both mono therapy and combination therapy</li>
<li>Predicting future antimicrobial resistance using regression models</li>
<li>Getting properties for any microorganism (like Gram stain, species, genus or family)</li>
<li>Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)</li>

8
docs/news/index.html
View File

@ -108,7 +108,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -129,14 +129,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -798,7 +798,7 @@ These functions use <code><a href="../reference/as.atc.html">as.atc()</a></code>
<ul>
<li>Full support for Windows, Linux and macOS</li>
<li>Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)</li>
<li>Function <code>n_rsi</code> to count cases where antibiotic test results were available, to be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">dplyr::summarise</a></code>, see ?rsi</li>
<li>Function <code>n_rsi</code> to count cases where antibiotic test results were available, to be used in conjunction with <code><a href="https://www.rdocumentation.org/packages/dplyr/topics/summarise">dplyr::summarise</a></code>, see ?rsi</li>
<li>Function <code>guess_bactid</code> to <strong>determine the ID</strong> of a microorganism based on genus/species or known abbreviations like MRSA</li>
<li>Function <code>guess_atc</code> to <strong>determine the ATC</strong> of an antibiotic based on name, trade name, or known abbreviations</li>
<li>Function <code>freq</code> to create <strong>frequency tables</strong>, with additional info in a header</li>

4
docs/pkgdown.yml
View File

@ -5,12 +5,12 @@ articles:
AMR: AMR.html
EUCAST: EUCAST.html
G_test: G_test.html
Predict: Predict.html
WHONET: WHONET.html
ab_property: ab_property.html
atc_property: atc_property.html
benchmarks: benchmarks.html
freq: freq.html
mo_property: mo_property.html
resistance_predict: resistance_predict.html
urls:
reference: https://msberends.gitlab.io/AMR/reference
article: https://msberends.gitlab.io/AMR/articles

6
docs/reference/index.html
View File

@ -108,7 +108,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -129,14 +129,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic

50
docs/reference/resistance_predict.html
View File

@ -110,7 +110,7 @@
</a>
</li>
<li>
<a href="../articles/Predict.html">
<a href="../articles/resistance_predict.html">
<span class="fa fa-dice"></span>
Predict antimicrobial resistance
@ -131,14 +131,14 @@
</a>
</li>
<li>
<a href="../articles/mo_property.html">
<a href="../reference/mo_property.html">
<span class="fa fa-bug"></span>
Get properties of a microorganism
</a>
</li>
<li>
<a href="../articles/ab_property.html">
<a href="../reference/atc_property.html">
<span class="fa fa-capsules"></span>
Get properties of an antibiotic
@ -249,7 +249,7 @@
<span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)
<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>, <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)</pre>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='kw'>ribbon</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
@ -284,7 +284,7 @@
</tr>
<tr>
<th>model</th>
<td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> (or <code>"poisson"</code>) or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
<td><p>the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for valid options.</p></td>
</tr>
<tr>
<th>I_as_R</th>
@ -312,6 +312,10 @@
<th>...</th>
<td><p>parameters passed on to the <code>first_isolate</code> function</p></td>
</tr>
<tr>
<th>ribbon</th>
<td><p>a logical to indicate whether a ribbon should be shown (default) or error bars</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
@ -324,8 +328,15 @@
<li><p><code>observations</code>, the total number of available observations in that year, i.e. S + I + R</p></li>
<li><p><code>observed</code>, the original observed resistant percentages</p></li>
<li><p><code>estimated</code>, the estimated resistant percentages, calculated by the model</p></li>
</ul>
</ul><p>Furthermore, the model itself is available as an attribute: <code>attributes(x)$model</code>, see Examples.</p>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>Valid options for the statistical model are:</p><ul>
<li><p><code>"binomial"</code> or <code>"binom"</code> or <code>"logit"</code>: a generalised linear regression model with binomial distribution</p></li>
<li><p><code>"loglin"</code> or <code>"poisson"</code>: a generalised log-linear regression model with poisson distribution</p></li>
<li><p><code>"lin"</code> or <code>"linear"</code>: a linear regression model</p></li>
</ul>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>
@ -348,36 +359,41 @@ On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitla
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='first_isolate.html'>filter_first_isolate</a></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='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/dplyr/topics/filter'>filter</a></span>(<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%&gt;%</span>
<span class='fu'>resistance_predict</span>(<span class='st'>"peni"</span>)
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>)
<span class='co'># create nice plots with ggplot yourself</span>
<span class='co'># get the model from the object</span>
<span class='no'>mymodel</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>model</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></span>(<span class='no'>mymodel</span>)
<span class='co'># create nice plots with ggplot2 yourself</span>
<span class='kw'>if</span> (!<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>require</a></span>(<span class='no'>ggplot2</span>)) {
<span class='no'>data</span> <span class='kw'>&lt;-</span> <span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>mo</span> <span class='kw'>==</span> <span class='fu'><a href='as.mo.html'>as.mo</a></span>(<span class='st'>"E. coli"</span>)) <span class='kw'>%&gt;%</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/dplyr/topics/filter'>filter</a></span>(<span class='no'>mo</span> <span class='kw'>==</span> <span class='fu'><a href='as.mo.html'>as.mo</a></span>(<span class='st'>"E. coli"</span>)) <span class='kw'>%&gt;%</span>
<span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amox"</span>,
<span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>15</span>)
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span>(<span class='no'>data</span>,
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>x</span> <span class='kw'>=</span> <span class='no'>year</span>)) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/geom_bar.html'>geom_col</a></span>(<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>value</span>),
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/ggplot'>ggplot</a></span>(<span class='no'>data</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/aes'>aes</a></span>(<span class='kw'>x</span> <span class='kw'>=</span> <span class='no'>year</span>)) +
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/geom_bar'>geom_col</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/aes'>aes</a></span>(<span class='kw'>y</span> <span class='kw'>=</span> <span class='no'>value</span>),
<span class='kw'>fill</span> <span class='kw'>=</span> <span class='st'>"grey75"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/geom_linerange.html'>geom_errorbar</a></span>(<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>ymin</span> <span class='kw'>=</span> <span class='no'>se_min</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/geom_linerange'>geom_errorbar</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/aes'>aes</a></span>(<span class='kw'>ymin</span> <span class='kw'>=</span> <span class='no'>se_min</span>,
<span class='kw'>ymax</span> <span class='kw'>=</span> <span class='no'>se_max</span>),
<span class='kw'>colour</span> <span class='kw'>=</span> <span class='st'>"grey50"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/scale_continuous.html'>scale_y_continuous</a></span>(<span class='kw'>limits</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>),
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/scale_continuous'>scale_y_continuous</a></span>(<span class='kw'>limits</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/c'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>),
<span class='kw'>breaks</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0.1</span>),
<span class='kw'>labels</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste0</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq</a></span>(<span class='fl'>0</span>, <span class='fl'>100</span>, <span class='fl'>10</span>), <span class='st'>"%"</span>)) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/labs.html'>labs</a></span>(<span class='kw'>title</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/expression'>expression</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Forecast of amoxicillin resistance in "</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/labs'>labs</a></span>(<span class='kw'>title</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/expression'>expression</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Forecast of amoxicillin resistance in "</span>,
<span class='fu'><a href='https://www.rdocumentation.org/packages/grDevices/topics/plotmath'>italic</a></span>(<span class='st'>"E. coli"</span>))),
<span class='kw'>y</span> <span class='kw'>=</span> <span class='st'>"%IR"</span>,
<span class='kw'>x</span> <span class='kw'>=</span> <span class='st'>"Year"</span>) +
<span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggtheme.html'>theme_minimal</a></span>(<span class='kw'>base_size</span> <span class='kw'>=</span> <span class='fl'>13</span>)
<span class='fu'><a href='https://www.rdocumentation.org/packages/ggplot2/topics/ggtheme'>theme_minimal</a></span>(<span class='kw'>base_size</span> <span class='kw'>=</span> <span class='fl'>13</span>)
}
<span class='co'># }</span></pre>
</div>
@ -388,6 +404,8 @@ On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitla
<li><a href="#value">Value</a></li>
<li><a href="#details">Details</a></li>
<li><a href="#read-more-on-our-website-">Read more on our website!</a></li>
<li><a href="#see-also">See also</a></li>

8
docs/sitemap.xml
View File

@ -138,14 +138,11 @@
<url>
<loc>https://msberends.gitlab.io/AMR/articles/G_test.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/articles/Predict.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/articles/WHONET.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/articles/ab_property.html</loc>
<loc>https://msberends.gitlab.io/AMR/articles/atc_property.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/articles/benchmarks.html</loc>
@ -156,4 +153,7 @@
<url>
<loc>https://msberends.gitlab.io/AMR/articles/mo_property.html</loc>
</url>
<url>
<loc>https://msberends.gitlab.io/AMR/articles/resistance_predict.html</loc>
</url>
</urlset>

2
index.md
View File

@ -14,7 +14,7 @@ This R package is actively maintained and free software; you can freely use and
This package can be used for:
* Calculating antimicrobial resistance
* Calculating empiric susceptibility of both mono therapy and combination therapy
* Calculating empirical susceptibility of both mono therapy and combination therapy
* Predicting future antimicrobial resistance using regression models
* Getting properties for any microorganism (like Gram stain, species, genus or family)
* Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)

12
man/resistance_predict.Rd
View File

@ -21,7 +21,7 @@ rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
main = paste("Resistance prediction of", attributes(x)$ab), ...)
ggplot_rsi_predict(x, main = paste("Resistance prediction of",
attributes(x)$ab), ...)
attributes(x)$ab), ribbon = TRUE, ...)
}
\arguments{
\item{tbl}{a \code{data.frame} containing isolates.}
@ -53,6 +53,8 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\item{main}{title of the plot}
\item{...}{parameters passed on to the \code{first_isolate} function}
\item{ribbon}{a logical to indicate whether a ribbon should be shown (default) or error bars}
}
\value{
\code{data.frame} with extra class \code{"resistance_predict"} with columns:
@ -65,6 +67,7 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
\item{\code{observed}, the original observed resistant percentages}
\item{\code{estimated}, the estimated resistant percentages, calculated by the model}
}
Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples.
}
\description{
Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
@ -97,7 +100,12 @@ x <- septic_patients \%>\%
plot(x)
# create nice plots with ggplot yourself
# get the model from the object
mymodel <- attributes(x)$model
summary(mymodel)
# create nice plots with ggplot2 yourself
if (!require(ggplot2)) {
data <- septic_patients \%>\%

10
pkgdown/extra.css
View File

@ -23,12 +23,12 @@
/* R for Data Science (r4ds) */
#r4ds a {
display: inline;
display: inline-flex;
align-items: center;
text-align: right;
}
#r4ds * {
text-align: center;
display: block;
margin: 0 auto;
#r4ds img {
margin-left: 10px;
}
/* class for footer */

22
pkgdown/extra.js
View File

@ -34,15 +34,19 @@ $( document ).ready(function() {
window.location.replace(url_new);
}
$('#sidebar').prepend(
'<div id="r4ds">' +
' <a href="https://r4ds.had.co.nz/">' +
' <img src="../../AMR/cover_r4ds.png" width="25%">' +
' </a>' +
' <p>Learn R reading this great book!</p>' +
' <p>Or read it free online: <a href="https://r4ds.had.co.nz/" target="_blank">r4ds.co.nz</a>.</p>' +
' <hr>' +
'</div>');
// PR for 'R for Data Science' on How To pages
if ($(".template-article").length > 0) {
$('#sidebar').prepend(
'<div id="r4ds">' +
' <a href="https://r4ds.had.co.nz/">Learn R reading this great book: R for Data Science.' +
' <br>' +
' <br>' +
' Click to read it online - it was published for free.' +
' <img src="../../AMR/cover_r4ds.png" height="100px">' +
' </a> ' +
' <hr>' +
'</div>');
}
$('footer').html(
'<div>' +

34
vignettes/AMR.Rmd
View File

@ -17,9 +17,9 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
comment = "#>",
fig.width = 7.5,
fig.height = 4.5
fig.height = 5
)
```
@ -106,14 +106,21 @@ ab_interpretations <- c("S", "I", "R")
Using the `sample()` function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the `prob` parameter.
```{r merge data}
data <- data.frame(date = sample(dates, 5000, replace = TRUE),
patient_id = sample(patients, 5000, replace = TRUE),
hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
gent = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.92, 0.00, 0.08))
sample_size <- 20000
data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
patient_id = sample(patients, size = sample_size, replace = TRUE),
hospital = sample(hospitals, size = sample_size, replace = TRUE,
prob = c(0.30, 0.35, 0.15, 0.20)),
bacteria = sample(bacteria, size = sample_size, replace = TRUE,
prob = c(0.50, 0.25, 0.15, 0.10)),
amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.60, 0.05, 0.35)),
amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.75, 0.10, 0.15)),
cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.80, 0.00, 0.20)),
gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
prob = c(0.92, 0.00, 0.08))
)
```
@ -124,6 +131,7 @@ data <- data %>% left_join(patients_table)
```
The resulting data set contains 5,000 blood culture isolates. With the `head()` function we can preview the first 6 values of this data set:
```{r preview data set 1, eval = FALSE}
head(data)
```
@ -148,6 +156,7 @@ data %>% freq(gender, markdown = FALSE, header = TRUE)
So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values `M` and `F`. From a researcher perspective: there are slightly more men. Nothing we didn't already know.
The data is already quite clean, but we still need to transform some variables. The `bacteria` column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The `mutate()` function of the `dplyr` package makes this really easy:
```{r transform mo 1}
data <- data %>%
mutate(bacteria = as.mo(bacteria))
@ -202,6 +211,7 @@ data_1st <- data %>%
```
For future use, the above two syntaxes can be shortened with the `filter_first_isolate()` function:
```{r 1st isolate filter 2, eval = FALSE}
data_1st <- data %>%
filter_first_isolate()
@ -263,6 +273,7 @@ data_1st <- data %>%
So we end up with `r format(nrow(data_1st), big.mark = ",")` isolates for analysis.
We can remove unneeded columns:
```{r}
data_1st <- data_1st %>%
select(-c(first, keyab))
@ -359,6 +370,7 @@ data_1st %>%
```
To make a transition to the next part, let's see how this difference could be plotted:
```{r plot 1}
data_1st %>%
group_by(genus) %>%
@ -391,6 +403,7 @@ ggplot(a_data_set,
```
The `AMR` package contains functions to extend this `ggplot2` package, for example `geom_rsi()`. It automatically transforms data with `count_df()` or `portion_df()` and show results in stacked bars. Its simplest and shortest example:
```{r plot 3}
ggplot(data_1st) +
geom_rsi(translate_ab = FALSE)
@ -424,6 +437,7 @@ ggplot(data_1st %>% group_by(genus)) +
```
To simplify this, we also created the `ggplot_rsi()` function, which combines almost all above functions:
```{r plot 5}
data_1st %>%
group_by(genus) %>%

2
vignettes/EUCAST.Rmd
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@ -17,7 +17,7 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
comment = "#>",
fig.width = 7.5,
fig.height = 4.5
)

2
vignettes/ab_property.Rmd → vignettes/atc_property.Rmd
View File

@ -16,7 +16,7 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
comment = "#>"
)
# set to original language (English)
Sys.setlocale(locale = "C")

4
vignettes/mo_property.Rmd
View File

@ -16,8 +16,10 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
comment = "#>"
)
# set to original language (English)
Sys.setlocale(locale = "C")
```
*(will be available soon)*

38
vignettes/Predict.Rmd → vignettes/resistance_predict.Rmd
View File

@ -16,9 +16,9 @@ editor_options:
```{r setup, include = FALSE, results = 'markup'}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#",
comment = "#>",
fig.width = 7.5,
fig.height = 4.5
fig.height = 4.75
)
```
@ -37,7 +37,7 @@ library(AMR)
```
## Prediction analysis
Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./articles/AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
Our package contains a function `resistance_predict()`, which takes the same input as functions for [other AMR analysis](./AMR.html). Based on a date column, it calculates cases per year and uses a regression model to predict antimicrobial resistance.
It is basically as easy as:
```{r, eval = FALSE}
@ -53,27 +53,36 @@ predict_pita <- septic_patients %>%
resistance_predict(col_ab = "pita")
```
The function will look for a date column itself if `col_date` is not set.
When running any of these commands, a summary of the regression model will be printed unless using `resistance_predict(..., info = FALSE)`.
```{r, echo = FALSE}
predict_pita <- septic_patients %>%
resistance_predict(col_ab = "pita")
```
The function will look for a data column itself if `col_date` is not set. The result is nothing more than a `data.frame`, containing the years, number of observations, actual observed resistance, the estimated resistance and the standard error below and above the estimation:
This text is only a printed summary - the actual result (output) of the function is a `data.frame` containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:
```{r}
predict_pita
```
The function `plot` 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:
```{r}
```{r, fig.height = 5.5}
plot(predict_pita)
```
We also support the `ggplot2` package with the function `ggplot_rsi_predict()`:
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.
We also support the `ggplot2` package with our custom function `ggplot_rsi_predict()` to create more appealing plots:
```{r}
library(ggplot2)
ggplot_rsi_predict(predict_pita)
# choose for error bars instead of a ribbon
ggplot_rsi_predict(predict_pita, ribbon = FALSE)
```
### Choosing the right model
@ -84,7 +93,7 @@ Resistance is not easily predicted; if we look at vancomycin resistance in Gram
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE) %>%
plot()
ggplot_rsi_predict()
```
Vancomycin resistance could be 100% in ten years, but might also stay around 0%.
@ -99,13 +108,22 @@ Valid values are:
| `"loglin"` or `"poisson"` | `glm(..., family = poisson)` | Generalised linear model with poisson distribution |
| `"lin"` or `"linear"` | `lm()` | Linear model |
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 observed years:
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:
```{r}
septic_patients %>%
filter(mo_gramstain(mo) == "Gram positive") %>%
resistance_predict(col_ab = "vanc", year_min = 2010, info = FALSE, model = "linear") %>%
plot()
ggplot_rsi_predict()
```
This seems more likely, doesn't it?
The model itself is also available from the object, as an `attribute`:
```{r}
model <- attributes(predict_pita)$model
summary(model)$family
summary(model)$coefficients
```