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  1. 2
      DESCRIPTION
  2. 1
      NAMESPACE
  3. 64
      R/resistance_predict.R
  4. 10
      README.md
  5. 8
      _pkgdown.yml
  6. 6
      docs/LICENSE-text.html
  7. 779
      docs/articles/AMR.html
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  12. 8
      docs/articles/EUCAST.html
  13. 233
      docs/articles/atc_property.html
  14. 10
      docs/articles/index.html
  15. 8
      docs/articles/mo_property.html
  16. 406
      docs/articles/resistance_predict.html
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  22. 6
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  23. 10
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  24. 22
      docs/extra.js
  25. 8
      docs/index.html
  26. 8
      docs/news/index.html
  27. 4
      docs/pkgdown.yml
  28. 6
      docs/reference/index.html
  29. 50
      docs/reference/resistance_predict.html
  30. 8
      docs/sitemap.xml
  31. 2
      index.md
  32. 12
      man/resistance_predict.Rd
  33. 10
      pkgdown/extra.css
  34. 22
      pkgdown/extra.js
  35. 34
      vignettes/AMR.Rmd
  36. 2
      vignettes/EUCAST.Rmd
  37. 2
      vignettes/atc_property.Rmd
  38. 4
      vignettes/mo_property.Rmd
  39. 38
      vignettes/resistance_predict.Rmd

2
DESCRIPTION

@ -1,6 +1,6 @@ @@ -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

@ -253,6 +253,7 @@ importFrom(graphics,axis) @@ -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

@ -35,6 +35,7 @@ @@ -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 @@ @@ -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)$model}, see Examples.
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
#' @rdname resistance_predict
#' @export
@ -71,7 +73,12 @@ @@ -71,7 +73,12 @@
#' plot(x)
#'
#'
#' # create nice plots with ggplot yourself
#' # get the model from the object
#' mymodel <- attributes(x)$model
#' summary(mymodel)
#'
#'
#' # create nice plots with ggplot2 yourself
#' if (!require(ggplot2)) {
#'
#' data <- septic_patients %>%
@ -295,8 +302,8 @@ rsi_predict <- resistance_predict @@ -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", @@ -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 @@ -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

@ -23,11 +23,11 @@ Bhanu N.M. Sinha <a href="https://orcid.org/0000-0003-1634-0010"><img src="https @@ -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>
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<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>
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<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

@ -37,7 +37,7 @@ navbar: @@ -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: @@ -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

@ -108,7 +108,7 @@ @@ -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 @@ @@ -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

779
docs/articles/AMR.html

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8
docs/articles/EUCAST.html

@ -70,7 +70,7 @@ @@ -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 @@ @@ -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 @@ @@ -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
docs/articles/atc_property.html

@ -0,0 +1,233 @@ @@ -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 @@ @@ -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 @@ @@ -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
@ -232,12 +232,12 @@ @@ -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>
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<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>
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<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>
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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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Predict antimicrobial resistance
@ -129,14 +129,14 @@ @@ -129,14 +129,14 @@
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Predict antimicrobial resistance
@ -93,14 +93,14 @@ @@ -93,14 +93,14 @@
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@ -195,7 +195,7 @@ @@ -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>