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@ -111,16 +111,16 @@
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.14521 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.90959 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.23836 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.95068 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.93699 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 37 37.04658 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.41918 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 47 47.00274 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.61370 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.46575 13</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.14795 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.91233 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.24110 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.95342 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.93973 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 37 37.04932 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.42192 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 47 47.00548 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.61644 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.46849 13</span>
</code></pre></div>
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<!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, shrink-to-fit=no"><title>Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram • 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"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.9/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.9/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram"><meta name="description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:description" content="Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.
Adhering to previously described approaches (see Source) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports."><meta property="og:image" content="https://msberends.github.io/AMR/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
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@ -52,7 +52,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
<div class="ref-description section level2">
<p>Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods.</p>
<p>Adhering to previously described approaches (see <em>Source</em>) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki <em>et al.</em>, these functions provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
<p>Adhering to previously described approaches (see <em>Source</em>) and especially the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki <em>et al.</em>, these functions provide flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.</p>
</div>
<div class="section level2">
@ -192,8 +192,8 @@ Adhering to previously described approaches (see Source) and especially the Baye
</dl></div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
<p>This function returns a table with values between 0 and 100 for <em>susceptibility</em>, not resistance.</p>
<p><strong>Remember that you should filter your data to let it contain only first isolates!</strong> This is needed to exclude duplicates and to reduce selection bias. Use <code><a href="first_isolate.html">first_isolate()</a></code> to determine them in your data set with one of the four available algorithms.</p>
<p>These functions return a table with values between 0 and 100 for <em>susceptibility</em>, not resistance.</p>
<p><strong>Remember that you should filter your data to let it contain only first isolates!</strong> This is needed to exclude duplicates and to reduce selection bias. Use <code><a href="first_isolate.html">first_isolate()</a></code> to determine them with one of the four available algorithms: isolate-based, patient-based, episode-based, or phenotype-based.</p>
<p>For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top <em>n</em> species encountered in the data. You can filter on this top <em>n</em> using <code><a href="top_n_microorganisms.html">top_n_microorganisms()</a></code>. For example, use <code>top_n_microorganisms(your_data, n = 10)</code> as a pre-processing step to only include the top 10 species in the data.</p>
<p>The numeric values of an antibiogram are stored in a long format as the <a href="https://rdrr.io/r/base/attributes.html" class="external-link">attribute</a> <code>long_numeric</code>. You can retrieve them using <code>attributes(x)$long_numeric</code>, where <code>x</code> is the outcome of <code>antibiogram()</code> or <code>wisca()</code>. This is ideal for e.g. advanced plotting.</p><div class="section">
<h3 id="formatting-type">Formatting Type<a class="anchor" aria-label="anchor" href="#formatting-type"></a></h3>
@ -321,7 +321,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
$$p_i = \frac{x_i}{\sum_{j=1}^K x_j}$$</p>
<p>where \(x_i\) represents unnormalised pathogen counts, and \(p_i\) is the normalised proportion for pathogen \(i\).</p>
<p>For hierarchical modelling, pathogen-level effects (e.g., differences in resistance patterns) and regimen-level effects are modelled using Gaussian priors on log-odds. This hierarchical structure ensures partial pooling of estimates across groups, improving stability in strata with small sample sizes. The model is implemented using Hamiltonian Monte Carlo (HMC) sampling.</p>
<p>Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using <code>dplyr</code>'s <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> as a pre-processing step before running <code>wisca()</code>. In this case, posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:</p>
<p>Stratified results can be provided based on covariates such as age, sex, and clinical complexity (e.g., prior antimicrobial treatments or renal/urological comorbidities) using <code>dplyr</code>'s <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> as a pre-processing step before running <code>wisca()</code>. Posterior odds ratios (ORs) are derived to quantify the effect of these covariates on coverage probabilities:</p>
<p>$$\text{OR}_{\text{covariate}} = \frac{\exp(\beta_{\text{covariate}})}{\exp(\beta_0)}$$</p>
<p>By combining empirical data with prior knowledge, WISCA overcomes the limitations of traditional combination antibiograms, offering disease-specific, patient-stratified estimates with robust uncertainty quantification. This tool is invaluable for antimicrobial stewardship programs and empirical treatment guideline refinement.</p>
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@ -17,7 +17,7 @@ my_data_with_all_these_columns %&amp;gt;%
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@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
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@ -757,16 +757,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 57 × 16</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index ab_given mo_given host_given ab mo </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 2 cipro Escheri… dogs CIP B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 2 cipro Escheri… dogs CIP B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-01-27 <span style="color: #949494;">22:16:01</span> 2 nitrofu… E. coli human NIT B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-01-27 <span style="color: #949494;">22:16:02</span> 2 nitrofu… E. coli human NIT B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 1 tobra bacteria human TOB B_[KNG]_BACTERIA</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-01-27 <span style="color: #949494;">22:16:00</span> 1 genta bacteria human GEN B_[KNG]_BACTERIA</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-01-28 <span style="color: #949494;">14:23:05</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-01-28 <span style="color: #949494;">14:23:12</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-01-28 <span style="color: #949494;">14:23:12</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-01-28 <span style="color: #949494;">14:23:13</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-01-28 <span style="color: #949494;">14:23:13</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-01-28 <span style="color: #949494;">14:23:05</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-01-28 <span style="color: #949494;">14:23:12</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-01-28 <span style="color: #949494;">14:23:12</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-01-28 <span style="color: #949494;">14:23:13</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-01-28 <span style="color: #949494;">14:23:13</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 47 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;dbl&gt;, outcome &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>

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reference/atc_online.html
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@ -7,7 +7,7 @@
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reference/av_from_text.html
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@ -7,7 +7,7 @@
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reference/av_property.html
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reference/availability.html
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@ -7,7 +7,7 @@
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reference/bug_drug_combinations.html
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@ -7,7 +7,7 @@
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reference/clinical_breakpoints.html
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@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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reference/count.html
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@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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reference/custom_eucast_rules.html
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@ -7,7 +7,7 @@
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reference/dosage.html
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reference/eucast_rules.html
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@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
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reference/example_isolates.html
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reference/example_isolates_unclean.html
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reference/export_ncbi_biosample.html
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reference/get_episode.html
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reference/microorganisms.groups.html
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@ -7,7 +7,7 @@
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reference/microorganisms.html
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@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9134</small>
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reference/mo_matching_score.html
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/mo_property.html
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@ -7,7 +7,7 @@
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reference/mo_source.html
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@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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reference/pca.html
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@ -7,7 +7,7 @@
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reference/plot.html
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@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
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reference/proportion.html
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@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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reference/random.html
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/resistance_predict.html
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@ -7,7 +7,7 @@
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reference/skewness.html
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@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
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reference/top_n_microorganisms.html
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@ -7,7 +7,7 @@
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reference/translate.html
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