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(v1.4.0) matching score update

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dr. M.S. (Matthijs) Berends
2020-10-08 11:16:03 +02:00
parent c04dc852cf
commit 28e77680c5
261 changed files with 1488 additions and 1171 deletions
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44
R/mo_matching_score.R
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@ -1,22 +1,26 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# Antimicrobial Resistance (AMR) Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.github.io/AMR. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Calculate the matching score for microorganisms
@ -27,7 +31,7 @@
#' @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:
#'
#' \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 )}
#' \deqn{m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{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:
#'
@ -49,29 +53,35 @@
#' mo_matching_score(x = "E. coli",
#' n = c("Escherichia coli", "Entamoeba coli"))
mo_matching_score <- function(x, n) {
x <- parse_and_convert(x)
# no dots and other non-whitespace characters
x <- gsub("[^a-zA-Z0-9 \\(\\)]+", "", x)
# only keep one space
x <- gsub(" +", " ", x)
# n is always a taxonomically valid full name
levenshtein <- double(length = length(x))
if (length(n) == 1) {
n <- rep(n, length(x))
}
if (length(x) == 1) {
x <- rep(x, length(n))
}
# length of fullname
l_n <- nchar(n)
lev <- double(length = length(x))
l_n.lev <- double(length = length(x))
for (i in seq_len(length(x))) {
# determine Levenshtein distance, but maximise to nchar of n
levenshtein[i] <- min(as.double(utils::adist(x[i], n[i], ignore.case = FALSE)),
nchar(n[i]))
lev[i] <- utils::adist(x[i], n[i], ignore.case = FALSE, fixed = TRUE)
# minimum of (l_n, Levenshtein distance)
l_n.lev[i] <- min(l_n[i], as.double(lev[i]))
}
# F = length of fullname
var_F <- nchar(n)
# L = modified Levenshtein distance
var_L <- levenshtein
# P = prevalence (1 to 3), see ?as.mo
var_P <- MO_lookup[match(n, MO_lookup$fullname), "prevalence", drop = TRUE]
# K = kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
var_K <- MO_lookup[match(n, MO_lookup$fullname), "kingdom_index", drop = TRUE]
# human pathogenic prevalence (1 to 3), see ?as.mo
p_n <- MO_lookup[match(n, MO_lookup$fullname), "prevalence", drop = TRUE]
# kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)
k_n <- MO_lookup[match(n, MO_lookup$fullname), "kingdom_index", drop = TRUE]
# matching score:
(var_F - 0.5 * var_L) / (var_F * var_P * var_K)
(l_n - 0.5 * l_n.lev) / (l_n * p_n * k_n)
}