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(v0.7.1.9006) new rsi calculations, atc class removal
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@ -80,7 +80,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9006</span>
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</span>
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</div>
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@ -406,7 +406,6 @@ The <code><a href='mo_property.html'>mo_property</a></code> functions (like <cod
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<span class='co'># All mo_* functions use as.mo() internally too (see ?mo_property):</span>
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<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='st'>"E. coli"</span>) <span class='co'># returns "Escherichia"</span>
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<span class='fu'><a href='mo_property.html'>mo_gramstain</a></span>(<span class='st'>"E. coli"</span>) <span class='co'># returns "Gram negative"#'</span>
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<span class='co'># }</span><span class='co'># NOT RUN {</span>
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<span class='no'>df</span>$<span class='no'>mo</span> <span class='kw'><-</span> <span class='fu'>as.mo</span>(<span class='no'>df</span>$<span class='no'>microorganism_name</span>)
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@ -81,7 +81,7 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9006</span>
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</span>
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</div>
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@ -349,10 +349,10 @@ not tested R - - - -
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not tested not tested - - - -
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-------------------------------------------------------------------------
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</pre>
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<p>Please note that for <code>only_all_tested = TRUE</code> applies that:</p><pre>
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<p>Please note that, in combination therapies, for <code>only_all_tested = TRUE</code> applies that:</p><pre>
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count_S() + count_I() + count_R() == count_all()
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portion_S() + portion_I() + portion_R() == 1
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</pre><p>and that for <code>only_all_tested = FALSE</code> applies that:</p><pre>
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</pre><p>and that, in combination therapies, for <code>only_all_tested = FALSE</code> applies that:</p><pre>
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count_S() + count_I() + count_R() >= count_all()
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portion_S() + portion_I() + portion_R() >= 1
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</pre>
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@ -78,7 +78,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9006</span>
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</span>
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</div>
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@ -81,7 +81,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9005</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">0.7.1.9006</span>
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</span>
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</div>
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@ -319,7 +319,7 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port
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<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
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<p><strong>Remember that you should filter your table 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.</p>
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<p>These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the <code><a href='count.html'>count</a></code> functions to count isolates. <em>Low counts can infuence the outcome - these <code>portion</code> functions may camouflage this, since they only return the portion albeit being dependent on the <code>minimum</code> parameter.</em></p>
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<p>These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the <code><a href='count.html'>count</a></code> functions to count isolates. The function <code>portion_SI()</code> is essentially equal to <code>count_SI() / count_all()</code>. <em>Low counts can infuence the outcome - the <code>portion</code> functions may camouflage this, since they only return the portion (albeit being dependent on the <code>minimum</code> parameter).</em></p>
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<p>The function <code>portion_df</code> takes any variable from <code>data</code> that has an <code>"rsi"</code> class (created with <code><a href='as.rsi.html'>as.rsi</a></code>) and calculates the portions R, I and S. The resulting <em>tidy data</em> (see Source) <code>data.frame</code> will have three rows (S/I/R) and a column for each group and each variable with class <code>"rsi"</code>.</p>
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<p>The function <code>rsi_df</code> works exactly like <code>portion_df</code>, but adds the number of isolates.</p>
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@ -352,10 +352,10 @@ not tested R - - - -
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not tested not tested - - - -
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-------------------------------------------------------------------------
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</pre>
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<p>Please note that for <code>only_all_tested = TRUE</code> applies that:</p><pre>
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<p>Please note that, in combination therapies, for <code>only_all_tested = TRUE</code> applies that:</p><pre>
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count_S() + count_I() + count_R() == count_all()
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portion_S() + portion_I() + portion_R() == 1
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</pre><p>and that for <code>only_all_tested = FALSE</code> applies that:</p><pre>
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</pre><p>and that, in combination therapies, for <code>only_all_tested = FALSE</code> applies that:</p><pre>
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count_S() + count_I() + count_R() >= count_all()
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portion_S() + portion_I() + portion_R() >= 1
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</pre>
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