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(v1.3.0.9031) matching score update

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dr. M.S. (Matthijs) Berends 2020-09-26 16:51:17 +02:00
parent 050a9a04fb
commit 22f6ceb3e4
19 changed files with 85 additions and 81 deletions
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2
DESCRIPTION
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@ -1,5 +1,5 @@
Package: AMR Package: AMR
Version: 1.3.0.9030 Version: 1.3.0.9031
Date: 2020-09-26 Date: 2020-09-26
Title: Antimicrobial Resistance Analysis Title: Antimicrobial Resistance Analysis
Authors@R: c( Authors@R: c(

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NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.3.0.9030 # AMR 1.3.0.9031
## <small>Last updated: 26 September 2020</small> ## <small>Last updated: 26 September 2020</small>
Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt to. We are those reviewers very grateful for going through our code so thoroughly! Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt to. We are those reviewers very grateful for going through our code so thoroughly!

6
R/mo.R
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@ -318,7 +318,7 @@ exec_as.mo <- function(x,
if (NROW(res_df) > 1 & uncertainty != -1) { if (NROW(res_df) > 1 & uncertainty != -1) {
# sort the findings on matching score # sort the findings on matching score
scores <- mo_matching_score(x = input, scores <- mo_matching_score(x = input,
fullname = res_df[, "fullname", drop = TRUE]) n = res_df[, "fullname", drop = TRUE])
res_df <- res_df[order(scores, decreasing = TRUE), , drop = FALSE] res_df <- res_df[order(scores, decreasing = TRUE), , drop = FALSE]
} }
res <- as.character(res_df[, column, drop = TRUE]) res <- as.character(res_df[, column, drop = TRUE])
@ -1773,7 +1773,7 @@ print.mo_uncertainties <- function(x, ...) {
if (x[i, ]$candidates != "") { if (x[i, ]$candidates != "") {
candidates <- unlist(strsplit(x[i, ]$candidates, ", ", fixed = TRUE)) candidates <- unlist(strsplit(x[i, ]$candidates, ", ", fixed = TRUE))
scores <- mo_matching_score(x = x[i, ]$input, scores <- mo_matching_score(x = x[i, ]$input,
fullname = candidates) n = candidates)
# sort on descending scores # sort on descending scores
candidates <- candidates[order(1 - scores)] candidates <- candidates[order(1 - scores)]
n_candidates <- length(candidates) n_candidates <- length(candidates)
@ -1799,7 +1799,7 @@ print.mo_uncertainties <- function(x, ...) {
ifelse(!is.na(x[i, ]$renamed_to), paste(", renamed to", font_italic(x[i, ]$renamed_to)), ""), ifelse(!is.na(x[i, ]$renamed_to), paste(", renamed to", font_italic(x[i, ]$renamed_to)), ""),
" (", x[i, ]$mo, " (", x[i, ]$mo,
", matching score = ", trimws(percentage(mo_matching_score(x = x[i, ]$input, ", matching score = ", trimws(percentage(mo_matching_score(x = x[i, ]$input,
fullname = x[i, ]$fullname), n = x[i, ]$fullname),
digits = 1)), digits = 1)),
") "), ") "),
uncertainty_interpretation, uncertainty_interpretation,

17
R/mo_matching_score.R
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@ -24,20 +24,21 @@
#' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input. #' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input.
#' @param x Any user input value(s) #' @param x Any user input value(s)
#' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms] #' @param n A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
#' @param uncertainty The level of uncertainty set in [as.mo()], see `allow_uncertain` in that function (here, it defaults to 1, but is automatically determined in [as.mo()] based on the number of transformations needed to get to a result)
#' @section Matching score for microorganisms: #' @section Matching score for microorganisms:
#' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m} is calculated as: #' With ambiguous user input in [as.mo()] and all the [`mo_*`][mo_property()] functions, the returned results are chosen based on their matching score using [mo_matching_score()]. This matching score \eqn{m}, ranging from 0 to 100%, is calculated as:
#' #'
#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p * k )} #' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
#' #'
#' where: #' where:
#' #'
#' * \eqn{x} is the user input; #' * \eqn{x} is the user input;
#' * \eqn{n} is a taxonomic name (genus, species and subspecies); #' * \eqn{n} is a taxonomic name (genus, species and subspecies) as found in [`microorganisms$fullname`][microorganisms];
#' * \eqn{l_{n}}{l_n} is the length of the taxonomic name; #' * \eqn{l_{n}}{l_n} is the length of \eqn{n};
#' * \eqn{\operatorname{lev}}{lev} is the [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) function; #' * \eqn{\operatorname{lev}}{lev} is the [Levenshtein distance function](https://en.wikipedia.org/wiki/Levenshtein_distance);
#' * \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see *Details* in `?as.mo`), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}}; #' * \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see *Details* in `?as.mo`), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
#' * \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}. #' * \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
#'
#' This means that the user input `x = "E. coli"` gets for *Escherichia coli* a matching score of `r percentage(mo_matching_score("E. coli", "Escherichia coli"), 1)` and for *Entamoeba coli* a matching score of `r percentage(mo_matching_score("E. coli", "Entamoeba coli"), 1)`.
#' #'
#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. #' All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
#' @export #' @export

2
docs/404.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a> <a class="navbar-link" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

2
docs/LICENSE-text.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a> <a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

2
docs/articles/index.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

2
docs/authors.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a> <a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

2
docs/index.html
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@ -43,7 +43,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a> <a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

8
docs/news/index.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>
@ -236,9 +236,9 @@
<small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small> <small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div> </div>
<div id="amr-1309030" class="section level1"> <div id="amr-1309031" class="section level1">
<h1 class="page-header" data-toc-text="1.3.0.9030"> <h1 class="page-header" data-toc-text="1.3.0.9031">
<a href="#amr-1309030" class="anchor"></a>AMR 1.3.0.9030<small> Unreleased </small> <a href="#amr-1309031" class="anchor"></a>AMR 1.3.0.9031<small> Unreleased </small>
</h1> </h1>
<div id="last-updated-26-september-2020" class="section level2"> <div id="last-updated-26-september-2020" class="section level2">
<h2 class="hasAnchor"> <h2 class="hasAnchor">

2
docs/pkgdown.yml
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@ -2,7 +2,7 @@ pandoc: 2.7.3
pkgdown: 1.5.1.9000 pkgdown: 1.5.1.9000
pkgdown_sha: eae56f08694abebf93cdfc0dd8e9ede06d8c815f pkgdown_sha: eae56f08694abebf93cdfc0dd8e9ede06d8c815f
articles: [] articles: []
last_built: 2020-09-26T14:25Z last_built: 2020-09-26T14:51Z
urls: urls:
reference: https://msberends.github.io/AMR/reference reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles article: https://msberends.github.io/AMR/articles

17
docs/reference/as.mo.html
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@ -82,7 +82,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>
@ -388,17 +388,18 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>With ambiguous user input in <code>as.mo()</code> and all the <code><a href='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code><a href='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\) is calculated as:</p> <p>With ambiguous user input in <code>as.mo()</code> and all the <code><a href='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code><a href='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}$$</p> <p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$</p>
<p>where:</p><ul> <p>where:</p><ul>
<li><p>\(x\) is the user input;</p></li> <li><p>\(x\) is the user input;</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li> <li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><a href='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li> <li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li> <li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code>?as.mo</code>), meaning that \(p = \{1, 2 , 3\}\);</p></li> <li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code>?as.mo</code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li> <li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul> </ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p> <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2> <h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2>

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docs/reference/index.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

21
docs/reference/mo_matching_score.html
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@ -82,7 +82,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>
@ -255,27 +255,24 @@
<th>n</th> <th>n</th>
<td><p>A full taxonomic name, that exists in <code><a href='microorganisms.html'>microorganisms$fullname</a></code></p></td> <td><p>A full taxonomic name, that exists in <code><a href='microorganisms.html'>microorganisms$fullname</a></code></p></td>
</tr> </tr>
<tr>
<th>uncertainty</th>
<td><p>The level of uncertainty set in <code><a href='as.mo.html'>as.mo()</a></code>, see <code>allow_uncertain</code> in that function (here, it defaults to 1, but is automatically determined in <code><a href='as.mo.html'>as.mo()</a></code> based on the number of transformations needed to get to a result)</p></td>
</tr>
</table> </table>
<h2 class="hasAnchor" id="matching-score-for-microorganisms"><a class="anchor" href="#matching-score-for-microorganisms"></a>Matching score for microorganisms</h2> <h2 class="hasAnchor" id="matching-score-for-microorganisms"><a class="anchor" href="#matching-score-for-microorganisms"></a>Matching score for microorganisms</h2>
<p>With ambiguous user input in <code><a href='as.mo.html'>as.mo()</a></code> and all the <code><a href='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code>mo_matching_score()</code>. This matching score \(m\) is calculated as:</p> <p>With ambiguous user input in <code><a href='as.mo.html'>as.mo()</a></code> and all the <code><a href='mo_property.html'>mo_*</a></code> functions, the returned results are chosen based on their matching score using <code>mo_matching_score()</code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}$$</p> <p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$</p>
<p>where:</p><ul> <p>where:</p><ul>
<li><p>\(x\) is the user input;</p></li> <li><p>\(x\) is the user input;</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li> <li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><a href='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li> <li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li> <li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><a href='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li> <li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><a href='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li> <li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul> </ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p> <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>

17
docs/reference/mo_property.html
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@ -82,7 +82,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>
@ -350,17 +350,18 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
<p>With ambiguous user input in <code><a href='as.mo.html'>as.mo()</a></code> and all the <code>mo_*</code> functions, the returned results are chosen based on their matching score using <code><a href='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\) is calculated as:</p> <p>With ambiguous user input in <code><a href='as.mo.html'>as.mo()</a></code> and all the <code>mo_*</code> functions, the returned results are chosen based on their matching score using <code><a href='mo_matching_score.html'>mo_matching_score()</a></code>. This matching score \(m\), ranging from 0 to 100%, is calculated as:</p>
<p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}$$</p> <p>$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$</p>
<p>where:</p><ul> <p>where:</p><ul>
<li><p>\(x\) is the user input;</p></li> <li><p>\(x\) is the user input;</p></li>
<li><p>\(n\) is a taxonomic name (genus, species and subspecies);</p></li> <li><p>\(n\) is a taxonomic name (genus, species and subspecies) as found in <code><a href='microorganisms.html'>microorganisms$fullname</a></code>;</p></li>
<li><p>\(l_{n}\) is the length of the taxonomic name;</p></li> <li><p>\(l_{n}\) is the length of \(n\);</p></li>
<li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance</a> function;</p></li> <li><p>\(\operatorname{lev}\) is the <a href='https://en.wikipedia.org/wiki/Levenshtein_distance'>Levenshtein distance function</a>;</p></li>
<li><p>\(p\) is the human pathogenic prevalence, categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><a href='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li> <li><p>\(p_{n}\) is the human pathogenic prevalence of \(n\), categorised into group \(1\), \(2\) and \(3\) (see <em>Details</em> in <code><a href='as.mo.html'>?as.mo</a></code>), meaning that \(p = \{1, 2 , 3\}\);</p></li>
<li><p>\(k\) is the kingdom index, set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li> <li><p>\(k_{n}\) is the kingdom index of \(n\), set as follows: Bacteria = \(1\), Fungi = \(2\), Protozoa = \(3\), Archaea = \(4\), and all others = \(5\), meaning that \(k = \{1, 2 , 3, 4, 5\}\).</p></li>
</ul> </ul>
<p>This means that the user input <code>x = "E. coli"</code> gets for <em>Escherichia coli</em> a matching score of 68.8% and for <em>Entamoeba coli</em> a matching score of 7.9%.</p>
<p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p> <p>All matches are sorted descending on their matching score and for all user input values, the top match will be returned.</p>
<h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2> <h2 class="hasAnchor" id="catalogue-of-life"><a class="anchor" href="#catalogue-of-life"></a>Catalogue of Life</h2>

2
docs/survey.html
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@ -81,7 +81,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="index.html">AMR (for R)</a> <a class="navbar-link" href="index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9030</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9031</span>
</span> </span>
</div> </div>

16
man/as.mo.Rd
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@ -146,20 +146,22 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
\section{Matching score for microorganisms}{ \section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as: With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p * k )} \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
where: where:
\itemize{ \itemize{
\item \eqn{x} is the user input; \item \eqn{x} is the user input;
\item \eqn{n} is a taxonomic name (genus, species and subspecies); \item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name; \item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function; \item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}}; \item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}. \item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
} }
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
} }

18
man/mo_matching_score.Rd
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@ -10,28 +10,28 @@ mo_matching_score(x, n)
\item{x}{Any user input value(s)} \item{x}{Any user input value(s)}
\item{n}{A full taxonomic name, that exists in \code{\link[=microorganisms]{microorganisms$fullname}}} \item{n}{A full taxonomic name, that exists in \code{\link[=microorganisms]{microorganisms$fullname}}}
\item{uncertainty}{The level of uncertainty set in \code{\link[=as.mo]{as.mo()}}, see \code{allow_uncertain} in that function (here, it defaults to 1, but is automatically determined in \code{\link[=as.mo]{as.mo()}} based on the number of transformations needed to get to a result)}
} }
\description{ \description{
This helper function is used by \code{\link[=as.mo]{as.mo()}} to determine the most probable match of taxonomic records, based on user input. This helper function is used by \code{\link[=as.mo]{as.mo()}} to determine the most probable match of taxonomic records, based on user input.
} }
\section{Matching score for microorganisms}{ \section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as: With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p * k )} \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
where: where:
\itemize{ \itemize{
\item \eqn{x} is the user input; \item \eqn{x} is the user input;
\item \eqn{n} is a taxonomic name (genus, species and subspecies); \item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name; \item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function; \item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}}; \item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}. \item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
} }
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
} }

16
man/mo_property.Rd
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@ -126,20 +126,22 @@ If the unlying code needs breaking changes, they will occur gradually. For examp
\section{Matching score for microorganisms}{ \section{Matching score for microorganisms}{
With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m} is calculated as: With ambiguous user input in \code{\link[=as.mo]{as.mo()}} and all the \code{\link[=mo_property]{mo_*}} functions, the returned results are chosen based on their matching score using \code{\link[=mo_matching_score]{mo_matching_score()}}. This matching score \eqn{m}, ranging from 0 to 100\%, is calculated as:
\deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \times \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} p k}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p * k )} \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}}{m(x, n) = ( l_n * min(l_n, lev(x, n) ) ) / ( l_n * p_n * k_n )}
where: where:
\itemize{ \itemize{
\item \eqn{x} is the user input; \item \eqn{x} is the user input;
\item \eqn{n} is a taxonomic name (genus, species and subspecies); \item \eqn{n} is a taxonomic name (genus, species and subspecies) as found in \code{\link[=microorganisms]{microorganisms$fullname}};
\item \eqn{l_{n}}{l_n} is the length of the taxonomic name; \item \eqn{l_{n}}{l_n} is the length of \eqn{n};
\item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance} function; \item \eqn{\operatorname{lev}}{lev} is the \href{https://en.wikipedia.org/wiki/Levenshtein_distance}{Levenshtein distance function};
\item \eqn{p} is the human pathogenic prevalence, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}}; \item \eqn{p_{n}}{p_n} is the human pathogenic prevalence of \eqn{n}, categorised into group \eqn{1}, \eqn{2} and \eqn{3} (see \emph{Details} in \code{?as.mo}), meaning that \eqn{p = \{1, 2 , 3\}}{p = {1, 2, 3}};
\item \eqn{k} is the kingdom index, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}. \item \eqn{k_{n}}{k_n} is the kingdom index of \eqn{n}, set as follows: Bacteria = \eqn{1}, Fungi = \eqn{2}, Protozoa = \eqn{3}, Archaea = \eqn{4}, and all others = \eqn{5}, meaning that \eqn{k = \{1, 2 , 3, 4, 5\}}{k = {1, 2, 3, 4, 5}}.
} }
This means that the user input \code{x = "E. coli"} gets for \emph{Escherichia coli} a matching score of 68.8\% and for \emph{Entamoeba coli} a matching score of 7.9\%.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. All matches are sorted descending on their matching score and for all user input values, the top match will be returned.
} }