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speed improvement eucast_rules(), support more old MO codes
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@ -192,7 +192,7 @@
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<h1>How to predict antimicrobial resistance</h1>
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<h4 class="author">Matthijs S. Berends</h4>
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<h4 class="date">17 May 2019</h4>
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<h4 class="date">20 May 2019</h4>
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<div class="hidden name"><code>resistance_predict.Rmd</code></div>
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@ -230,62 +230,62 @@
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<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">"TZP"</span>)</a></code></pre></div>
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<p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
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<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>
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<pre><code>#> NOTE: Using column `date` as input for `col_date`.
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#>
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#> Logistic regression model (logit) with binomial distribution
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#> ------------------------------------------------------------
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#>
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#> Call:
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#> glm(formula = df_matrix ~ year, family = binomial)
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#>
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#> Deviance Residuals:
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#> Min 1Q Median 3Q Max
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#> -2.6817 -1.4087 -0.5657 0.9672 3.5728
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#>
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#> Coefficients:
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#> Estimate Std. Error z value Pr(>|z|)
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#> (Intercept) -224.39872 48.03354 -4.672 2.99e-06 ***
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#> year 0.11061 0.02388 4.633 3.61e-06 ***
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#> ---
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#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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#>
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#> (Dispersion parameter for binomial family taken to be 1)
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#>
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#> Null deviance: 61.512 on 14 degrees of freedom
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#> Residual deviance: 38.692 on 13 degrees of freedom
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#> AIC: 95.212
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#>
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#> Number of Fisher Scoring iterations: 4</code></pre>
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<pre><code># NOTE: Using column `date` as input for `col_date`.
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#
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# Logistic regression model (logit) with binomial distribution
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# ------------------------------------------------------------
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#
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# Call:
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# glm(formula = df_matrix ~ year, family = binomial)
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#
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# Deviance Residuals:
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# Min 1Q Median 3Q Max
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# -2.6817 -1.4087 -0.5657 0.9672 3.5728
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#
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# Coefficients:
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# Estimate Std. Error z value Pr(>|z|)
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# (Intercept) -224.39872 48.03354 -4.672 2.99e-06 ***
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# year 0.11061 0.02388 4.633 3.61e-06 ***
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# ---
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# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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#
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# (Dispersion parameter for binomial family taken to be 1)
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#
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# Null deviance: 61.512 on 14 degrees of freedom
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# Residual deviance: 38.692 on 13 degrees of freedom
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# AIC: 95.212
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#
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# Number of Fisher Scoring iterations: 4</code></pre>
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<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>
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<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">predict_TZP</a>
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<a class="sourceLine" id="cb4-2" title="2"><span class="co">#> year value se_min se_max observations observed estimated</span></a>
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<a class="sourceLine" id="cb4-3" title="3"><span class="co">#> 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span></a>
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<a class="sourceLine" id="cb4-4" title="4"><span class="co">#> 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span></a>
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<a class="sourceLine" id="cb4-5" title="5"><span class="co">#> 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span></a>
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<a class="sourceLine" id="cb4-6" title="6"><span class="co">#> 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span></a>
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<a class="sourceLine" id="cb4-7" title="7"><span class="co">#> 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span></a>
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<a class="sourceLine" id="cb4-8" title="8"><span class="co">#> 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span></a>
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<a class="sourceLine" id="cb4-9" title="9"><span class="co">#> 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span></a>
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<a class="sourceLine" id="cb4-10" title="10"><span class="co">#> 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span></a>
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<a class="sourceLine" id="cb4-11" title="11"><span class="co">#> 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span></a>
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<a class="sourceLine" id="cb4-12" title="12"><span class="co">#> 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span></a>
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<a class="sourceLine" id="cb4-13" title="13"><span class="co">#> 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span></a>
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<a class="sourceLine" id="cb4-14" title="14"><span class="co">#> 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span></a>
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<a class="sourceLine" id="cb4-15" title="15"><span class="co">#> 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span></a>
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<a class="sourceLine" id="cb4-16" title="16"><span class="co">#> 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span></a>
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<a class="sourceLine" id="cb4-17" title="17"><span class="co">#> 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span></a>
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<a class="sourceLine" id="cb4-18" title="18"><span class="co">#> 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span></a>
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<a class="sourceLine" id="cb4-19" title="19"><span class="co">#> 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span></a>
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<a class="sourceLine" id="cb4-20" title="20"><span class="co">#> 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span></a>
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<a class="sourceLine" id="cb4-21" title="21"><span class="co">#> 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span></a>
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<a class="sourceLine" id="cb4-22" title="22"><span class="co">#> 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span></a>
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<a class="sourceLine" id="cb4-23" title="23"><span class="co">#> 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span></a>
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<a class="sourceLine" id="cb4-24" title="24"><span class="co">#> 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span></a>
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<a class="sourceLine" id="cb4-25" title="25"><span class="co">#> 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span></a>
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<a class="sourceLine" id="cb4-26" title="26"><span class="co">#> 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span></a>
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<a class="sourceLine" id="cb4-27" title="27"><span class="co">#> 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span></a>
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<a class="sourceLine" id="cb4-28" title="28"><span class="co">#> 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a>
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<a class="sourceLine" id="cb4-29" title="29"><span class="co">#> 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></code></pre></div>
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<a class="sourceLine" id="cb4-2" title="2"><span class="co"># year value se_min se_max observations observed estimated</span></a>
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<a class="sourceLine" id="cb4-3" title="3"><span class="co"># 1 2003 0.06250000 NA NA 32 0.06250000 0.05486389</span></a>
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<a class="sourceLine" id="cb4-4" title="4"><span class="co"># 2 2004 0.08536585 NA NA 82 0.08536585 0.06089002</span></a>
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<a class="sourceLine" id="cb4-5" title="5"><span class="co"># 3 2005 0.05000000 NA NA 60 0.05000000 0.06753075</span></a>
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<a class="sourceLine" id="cb4-6" title="6"><span class="co"># 4 2006 0.05084746 NA NA 59 0.05084746 0.07483801</span></a>
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<a class="sourceLine" id="cb4-7" title="7"><span class="co"># 5 2007 0.12121212 NA NA 66 0.12121212 0.08286570</span></a>
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<a class="sourceLine" id="cb4-8" title="8"><span class="co"># 6 2008 0.04166667 NA NA 72 0.04166667 0.09166918</span></a>
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<a class="sourceLine" id="cb4-9" title="9"><span class="co"># 7 2009 0.01639344 NA NA 61 0.01639344 0.10130461</span></a>
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<a class="sourceLine" id="cb4-10" title="10"><span class="co"># 8 2010 0.05660377 NA NA 53 0.05660377 0.11182814</span></a>
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<a class="sourceLine" id="cb4-11" title="11"><span class="co"># 9 2011 0.18279570 NA NA 93 0.18279570 0.12329488</span></a>
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<a class="sourceLine" id="cb4-12" title="12"><span class="co"># 10 2012 0.30769231 NA NA 65 0.30769231 0.13575768</span></a>
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<a class="sourceLine" id="cb4-13" title="13"><span class="co"># 11 2013 0.06896552 NA NA 58 0.06896552 0.14926576</span></a>
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<a class="sourceLine" id="cb4-14" title="14"><span class="co"># 12 2014 0.10000000 NA NA 60 0.10000000 0.16386307</span></a>
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<a class="sourceLine" id="cb4-15" title="15"><span class="co"># 13 2015 0.23636364 NA NA 55 0.23636364 0.17958657</span></a>
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<a class="sourceLine" id="cb4-16" title="16"><span class="co"># 14 2016 0.22619048 NA NA 84 0.22619048 0.19646431</span></a>
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<a class="sourceLine" id="cb4-17" title="17"><span class="co"># 15 2017 0.16279070 NA NA 86 0.16279070 0.21451350</span></a>
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<a class="sourceLine" id="cb4-18" title="18"><span class="co"># 16 2018 0.23373852 0.2021578 0.2653193 NA NA 0.23373852</span></a>
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<a class="sourceLine" id="cb4-19" title="19"><span class="co"># 17 2019 0.25412909 0.2168525 0.2914057 NA NA 0.25412909</span></a>
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<a class="sourceLine" id="cb4-20" title="20"><span class="co"># 18 2020 0.27565854 0.2321869 0.3191302 NA NA 0.27565854</span></a>
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<a class="sourceLine" id="cb4-21" title="21"><span class="co"># 19 2021 0.29828252 0.2481942 0.3483709 NA NA 0.29828252</span></a>
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<a class="sourceLine" id="cb4-22" title="22"><span class="co"># 20 2022 0.32193804 0.2649008 0.3789753 NA NA 0.32193804</span></a>
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<a class="sourceLine" id="cb4-23" title="23"><span class="co"># 21 2023 0.34654311 0.2823269 0.4107593 NA NA 0.34654311</span></a>
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<a class="sourceLine" id="cb4-24" title="24"><span class="co"># 22 2024 0.37199700 0.3004860 0.4435080 NA NA 0.37199700</span></a>
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<a class="sourceLine" id="cb4-25" title="25"><span class="co"># 23 2025 0.39818127 0.3193839 0.4769787 NA NA 0.39818127</span></a>
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<a class="sourceLine" id="cb4-26" title="26"><span class="co"># 24 2026 0.42496142 0.3390173 0.5109056 NA NA 0.42496142</span></a>
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<a class="sourceLine" id="cb4-27" title="27"><span class="co"># 25 2027 0.45218939 0.3593720 0.5450068 NA NA 0.45218939</span></a>
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<a class="sourceLine" id="cb4-28" title="28"><span class="co"># 26 2028 0.47970658 0.3804212 0.5789920 NA NA 0.47970658</span></a>
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<a class="sourceLine" id="cb4-29" title="29"><span class="co"># 27 2029 0.50734745 0.4021241 0.6125708 NA NA 0.50734745</span></a></code></pre></div>
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<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>
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<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_TZP)</a></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
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@ -305,7 +305,7 @@
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<a class="sourceLine" id="cb8-2" title="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">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">%>%</span></a>
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<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">"VAN"</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">%>%</span><span class="st"> </span></a>
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<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>
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<a class="sourceLine" id="cb8-5" title="5"><span class="co">#> </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
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<a class="sourceLine" id="cb8-5" title="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-6-1.png" width="720"></p>
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<p>Vancomycin resistance could be 100% in ten years, but might also stay around 0%.</p>
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<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>
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@ -350,21 +350,21 @@
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<a class="sourceLine" id="cb9-2" title="2"><span class="st"> </span><span class="kw"><a href="https://dplyr.tidyverse.org/reference/filter.html">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">%>%</span></a>
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<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">"VAN"</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">%>%</span><span class="st"> </span></a>
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<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>
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<a class="sourceLine" id="cb9-5" title="5"><span class="co">#> </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
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<a class="sourceLine" id="cb9-5" title="5"><span class="co"># </span><span class="al">NOTE</span><span class="co">: Using column `date` as input for `col_date`.</span></a></code></pre></div>
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<p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
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<p>This seems more likely, doesn’t it?</p>
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<p>The model itself is also available from the object, as an <code>attribute</code>:</p>
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<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1">model <-<span class="st"> </span><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/attributes">attributes</a></span>(predict_TZP)<span class="op">$</span>model</a>
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<a class="sourceLine" id="cb10-2" title="2"></a>
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<a class="sourceLine" id="cb10-3" title="3"><span class="kw"><a href="https://www.rdocumentation.org/packages/base/topics/summary">summary</a></span>(model)<span class="op">$</span>family</a>
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<a class="sourceLine" id="cb10-4" title="4"><span class="co">#> </span></a>
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<a class="sourceLine" id="cb10-5" title="5"><span class="co">#> Family: binomial </span></a>
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<a class="sourceLine" id="cb10-6" title="6"><span class="co">#> Link function: logit</span></a>
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<a class="sourceLine" id="cb10-4" title="4"><span class="co"># </span></a>
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<a class="sourceLine" id="cb10-5" title="5"><span class="co"># Family: binomial </span></a>
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<a class="sourceLine" id="cb10-6" title="6"><span class="co"># Link function: logit</span></a>
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<a class="sourceLine" id="cb10-7" title="7"></a>
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<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>
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<a class="sourceLine" id="cb10-9" title="9"><span class="co">#> Estimate Std. Error z value Pr(>|z|)</span></a>
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<a class="sourceLine" id="cb10-10" title="10"><span class="co">#> (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a>
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<a class="sourceLine" id="cb10-11" title="11"><span class="co">#> year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div>
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<a class="sourceLine" id="cb10-9" title="9"><span class="co"># Estimate Std. Error z value Pr(>|z|)</span></a>
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<a class="sourceLine" id="cb10-10" title="10"><span class="co"># (Intercept) -224.3987194 48.0335384 -4.671709 2.987038e-06</span></a>
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<a class="sourceLine" id="cb10-11" title="11"><span class="co"># year 0.1106102 0.0238753 4.632831 3.606990e-06</span></a></code></pre></div>
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</div>
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</div>
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</div>
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