mirror of https://github.com/msberends/AMR.git
73 lines
4.1 KiB
R
Executable File
73 lines
4.1 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.github.io/AMR. #
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# ==================================================================== #
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#' Calculate the matching score for microorganisms
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#'
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#' This helper function is used by [as.mo()] to determine the most probable match of taxonomic records, based on user input.
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#' @param x Any user input value(s)
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#' @param fullname A full taxonomic name, that exists in [`microorganisms$fullname`][microorganisms]
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#' @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)
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#' @details The matching score is based on four parameters:
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#'
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#' 1. A human pathogenic prevalence \eqn{P}, that is categorised into group 1, 2 and 3 (see [as.mo()]);
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#' 2. A kingdom index \eqn{K} is set as follows: Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, and all others = 5;
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#' 3. The level of uncertainty \eqn{U} that is needed to get to a result (1 to 3, see [as.mo()]);
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#' 4. The [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) \eqn{L} is the distance between the user input and all taxonomic full names, with the text length of the user input being the maximum distance. A modified version of the Levenshtein distance \eqn{L'} based on the text length of the full name \eqn{F} is calculated as:
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#'
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#' \deqn{L' = 1 - \frac{0.5L}{F}}{L' = 1 - ((0.5 * L) / F)}
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#'
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#' The final matching score \eqn{M} is calculated as:
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#' \deqn{M = L' \times \frac{1}{P K U} = \frac{F - 0.5L}{F P K U}}{M = L' * (1 / (P * K * U)) = (F - 0.5L) / (F * P * K * U)}
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#'
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#' @export
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#' @examples
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#' as.mo("E. coli")
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#' mo_uncertainties()
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mo_matching_score <- function(x, fullname, uncertainty = 1) {
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# fullname is always a taxonomically valid full name
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levenshtein <- double(length = length(x))
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if (length(fullname) == 1) {
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fullname <- rep(fullname, length(x))
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}
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if (length(x) == 1) {
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x <- rep(x, length(fullname))
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}
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for (i in seq_len(length(x))) {
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# determine Levenshtein distance, but maximise to nchar of fullname
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levenshtein[i] <- min(as.double(utils::adist(x[i], fullname[i], ignore.case = FALSE)),
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nchar(fullname[i]))
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}
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# F = length of fullname
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var_F <- nchar(fullname)
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# L = modified Levenshtein distance
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var_L <- levenshtein
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# P = Prevalence (1 to 3)
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var_P <- MO_lookup[match(fullname, MO_lookup$fullname), "prevalence", drop = TRUE]
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# K = kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
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var_K <- MO_lookup[match(fullname, MO_lookup$fullname), "kingdom_index", drop = TRUE]
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# U = uncertainty level (1 to 3), as per as.mo()
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var_U <- uncertainty
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# matching score:
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(var_F - 0.5 * var_L) / (var_F * var_P * var_K * var_U)
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}
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