<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second.</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
<p>To achieve this speed, the <code>as.mo</code> function also takes into account the prevalence of human pathogenic microorganisms. The downside of this is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of <em>Methanosarcina semesiae</em> (<code>B_MTHNSR_SEMS</code>), a bug probably never found before in humans:</p>
<p>That takes 5.6 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>That takes 5.6 times as much time on average. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like <em>Methanosarcina semesiae</em>) are always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<p>In the figure below, we compare <em>Escherichia coli</em> (which is very common) with <em>Prevotella brevis</em> (which is moderately common) and with <em>Methanosarcina semesiae</em> (which is uncommon):</p>
<aclass="sourceLine"id="cb5-7"data-line-number="7"><spanclass="co"># expr min lq mean median uq max neval</span></a>
<aclass="sourceLine"id="cb5-7"data-line-number="7"><spanclass="co"># expr min lq mean median uq max neval</span></a>
<aclass="sourceLine"id="cb5-8"data-line-number="8"><spanclass="co"># A 6.370 6.460 9.890 6.53 6.900 39.400 10</span></a>
<aclass="sourceLine"id="cb5-8"data-line-number="8"><spanclass="co"># A 6.120 6.540 7.44 6.900 8.830 9.12 10</span></a>
<aclass="sourceLine"id="cb5-9"data-line-number="9"><spanclass="co"># B 13.400 13.500 13.800 13.60 14.100 14.500 10</span></a>
<aclass="sourceLine"id="cb5-9"data-line-number="9"><spanclass="co"># B 13.600 13.800 14.50 14.200 15.000 16.30 10</span></a>
<aclass="sourceLine"id="cb5-10"data-line-number="10"><spanclass="co"># C 0.795 0.825 0.851 0.84 0.849 0.973 10</span></a></code></pre></div>
<aclass="sourceLine"id="cb5-10"data-line-number="10"><spanclass="co"># C 0.841 0.8593.82 0.876 0.917 30.30 10</span></a></code></pre></div>
<p>So going from <code><ahref="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0008 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<p>So going from <code><ahref="../reference/mo_property.html">mo_name("Staphylococcus aureus")</a></code> to <code>"Staphylococcus aureus"</code> takes 0.0009 seconds - it doesn’t even start calculating <em>if the result would be the same as the expected resulting value</em>. That goes for all helper functions:</p>
<aclass="sourceLine"id="cb6-12"data-line-number="12"><spanclass="co"># expr min lq mean median uq max neval</span></a>
<aclass="sourceLine"id="cb6-12"data-line-number="12"><spanclass="co"># expr min lq mean median uq max neval</span></a>
<aclass="sourceLine"id="cb6-13"data-line-number="13"><spanclass="co"># A 0.451 0.485 0.488 0.489 0.495 0.518 10</span></a>
<aclass="sourceLine"id="cb6-13"data-line-number="13"><spanclass="co"># A 0.517 0.522 0.570 0.540 0.575 0.756 10</span></a>
<aclass="sourceLine"id="cb6-14"data-line-number="14"><spanclass="co"># B 0.507 0.510 0.526 0.522 0.538 0.554 10</span></a>
<aclass="sourceLine"id="cb6-14"data-line-number="14"><spanclass="co"># B 0.536 0.550 0.584 0.559 0.579 0.801 10</span></a>
<aclass="sourceLine"id="cb6-15"data-line-number="15"><spanclass="co"># C 0.732 0.742 0.769 0.781 0.786 0.807 10</span></a>
<aclass="sourceLine"id="cb6-15"data-line-number="15"><spanclass="co"># C 0.696 0.776 0.804 0.828 0.841 0.908 10</span></a>
<aclass="sourceLine"id="cb6-16"data-line-number="16"><spanclass="co"># D 0.507 0.514 0.534 0.531 0.549 0.585 10</span></a>
<aclass="sourceLine"id="cb6-16"data-line-number="16"><spanclass="co"># D 0.531 0.552 0.597 0.576 0.616 0.784 10</span></a>
<aclass="sourceLine"id="cb6-17"data-line-number="17"><spanclass="co"># E 0.469 0.486 0.492 0.489 0.499 0.532 10</span></a>
<aclass="sourceLine"id="cb6-17"data-line-number="17"><spanclass="co"># E 0.520 0.529 0.549 0.534 0.569 0.626 10</span></a>
<aclass="sourceLine"id="cb6-18"data-line-number="18"><spanclass="co"># F 0.473 0.479 0.483 0.481 0.482 0.513 10</span></a>
<aclass="sourceLine"id="cb6-18"data-line-number="18"><spanclass="co"># F 0.483 0.491 0.519 0.517 0.521 0.580 10</span></a>
<aclass="sourceLine"id="cb6-19"data-line-number="19"><spanclass="co"># G 0.466 0.469 0.481 0.480 0.486 0.517 10</span></a>
<aclass="sourceLine"id="cb6-19"data-line-number="19"><spanclass="co"># G 0.509 0.514 0.545 0.520 0.537 0.763 10</span></a>
<aclass="sourceLine"id="cb6-20"data-line-number="20"><spanclass="co"># H 0.468 0.476 0.502 0.483 0.494 0.665 10</span></a></code></pre></div>
<aclass="sourceLine"id="cb6-20"data-line-number="20"><spanclass="co"># H 0.464 0.514 0.555 0.542 0.593 0.698 10</span></a></code></pre></div>
<p>Of course, when running <code><ahref="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
<p>Of course, when running <code><ahref="../reference/mo_property.html">mo_phylum("Firmicutes")</a></code> the function has zero knowledge about the actual microorganism, namely <em>S. aureus</em>. But since the result would be <code>"Firmicutes"</code> anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.</p>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.9.0.9020</span>
<spanclass="version label label-default"data-toggle="tooltip"data-placement="bottom"title="Latest development version">0.9.0.9021</span>
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