<h3id="last-updated-june-1-8-1-9011"><small>Last updated: 3 June 2022</small><aclass="anchor"aria-label="anchor"href="#last-updated-june-1-8-1-9011"></a></h3>
<h3id="last-updated-june-1-8-1-9012"><small>Last updated: 3 June 2022</small><aclass="anchor"aria-label="anchor"href="#last-updated-june-1-8-1-9012"></a></h3>
<ul><li>EUCAST 2022 and CLSI 2022 guidelines have been added for <code><ahref="../reference/as.rsi.html">as.rsi()</a></code>. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.</li>
<ul><li>Fix for <code><ahref="../reference/as.rsi.html">as.rsi()</a></code> on certain EUCAST breakpoints for MIC values</li>
<li>Removed <code><ahref="https://rdrr.io/r/base/integer.html"class="external-link">as.integer()</a></code> for MIC values, since MIC are not integer values and running <code><ahref="https://rdrr.io/r/base/table.html"class="external-link">table()</a></code> on MIC values consequently failed for not being able to retrieve the level position (as that’s how normally <code><ahref="https://rdrr.io/r/base/integer.html"class="external-link">as.integer()</a></code> on <code>factor</code>s work)</li>
<h3id="using-taxonomic-properties-in-rules">Using taxonomic properties in rules<aclass="anchor"aria-label="anchor"href="#using-taxonomic-properties-in-rules"></a></h3>
<p>There is one exception in variables used for the rules: all column names of the <ahref="microorganisms.html">microorganisms</a> data set can also be used, but do not have to exist in the data set. These column names are: <code>r vector_and(colnames(microorganisms), quote = "``", sort = FALSE)</code>. Thus, this next example will work as well, despite the fact that the <code>df</code> data set does not contain a column <code>genus</code>:</p>
<p>There is one exception in variables used for the rules: all column names of the <ahref="microorganisms.html">microorganisms</a> data set can also be used, but do not have to exist in the data set. These column names are: mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence and snomed. Thus, this next example will work as well, despite the fact that the <code>df</code> data set does not contain a column <code>genus</code>:</p>
<h3id="usage-of-antibiotic-group-names">Usage of antibiotic group names<aclass="anchor"aria-label="anchor"href="#usage-of-antibiotic-group-names"></a></h3>
<p>It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part <em>after</em> the tilde. In above examples, the antibiotic group <code>aminopenicillins</code> is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the agents that will be matched when running the rule.</p>
<p>It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part <em>after</em> the tilde. In above examples, the antibiotic group <code>aminopenicillins</code> is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the agents that will be matched when running the rule.</p><ul><li><p>aminoglycosides<br>(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin and tobramycin-high)</p></li>
<li><p>aminopenicillins<br>(amoxicillin and ampicillin)</p></li>
main <spanclass="op">=</span><spanclass="fu"><ahref="https://rdrr.io/r/base/paste.html"class="external-link">paste</a></span><spanclass="op">(</span><spanclass="st">"Resistance Prediction of"</span>, <spanclass="va">x_name</span><spanclass="op">)</span>,
<dd><p>minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.</p></dd>
<dt>model</dt>
<dd><p>the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See <em>Details</em> for all valid options.</p></dd>
<dd><p>the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using <code>glm(..., family = binomial)</code>, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See <em>Details</em> for all valid options.</p></dd>
<dt>I_as_S</dt>
<dd><p>a <ahref="https://rdrr.io/r/base/logical.html"class="external-link">logical</a> to indicate whether values <code>"I"</code> should be treated as <code>"S"</code> (will otherwise be treated as <code>"R"</code>). The default, <code>TRUE</code>, follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section <em>Interpretation of S, I and R</em> below.</p></dd>
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