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dr. M.S. (Matthijs) Berends
2023-01-05 14:46:44 +01:00
parent a0ee86536a
commit a98d0d75ea
8 changed files with 37 additions and 25 deletions
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34
man/mean_amr_distance.Rd
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@ -16,7 +16,7 @@ mean_amr_distance(x, ...)
amr_distance_from_row(amr_distance, row)
}
\arguments{
\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.rsi]{rsi} or \link[=as.rsi]{rsi}, or a \link{data.frame} containing columns of any of these classes}
\item{x}{a vector of class \link[=as.rsi]{rsi}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes}
\item{...}{variables to select (supports \link[tidyselect:language]{tidyselect language} such as \code{column1:column4} and \code{where(is.mic)}, and can thus also be \link[=ab_selector]{antibiotic selectors}}
@ -30,13 +30,13 @@ amr_distance_from_row(amr_distance, row)
Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
}
\details{
The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to \href{https://en.wikipedia.org/wiki/Standard_score}{Z scores} (the number of standard deviations from the mean).
The mean AMR distance is effectively \href{https://en.wikipedia.org/wiki/Standard_score}{the Z-score}; a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
MIC values (see \code{\link[=as.mic]{as.mic()}}) are transformed with \code{\link[=log2]{log2()}} first; their distance is thus calculated as \code{(log2(x) - mean(log2(x))) / sd(log2(x))}.
R/SI values (see \code{\link[=as.rsi]{as.rsi()}}) are transformed using \code{"S"} = 1, \code{"I"} = 2, and \code{"R"} = 3. If \code{combine_SI} is \code{TRUE} (default), the \code{"I"} will be considered to be 1.
For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using \code{\link[=rowMeans]{rowMeans()}}), see \emph{Examples}.
For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see \emph{Examples}.
Use \code{\link[=amr_distance_from_row]{amr_distance_from_row()}} to subtract distances from the distance of one row, see \emph{Examples}.
}
@ -46,14 +46,24 @@ Isolates with distances less than 0.01 difference from each other should be cons
}
\examples{
x <- random_mic(10)
x
mean_amr_distance(x)
rsi <- random_rsi(10)
rsi
mean_amr_distance(rsi)
mic <- random_mic(10)
mic
mean_amr_distance(mic)
# equal to the Z-score of their log2:
(log2(mic) - mean(log2(mic))) / sd(log2(mic))
disk <- random_disk(10)
disk
mean_amr_distance(disk)
y <- data.frame(
id = LETTERS[1:10],
amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
)
@ -65,7 +75,7 @@ y[order(y$amr_distance), ]
if (require("dplyr")) {
y \%>\%
mutate(
amr_distance = mean_amr_distance(., where(is.mic)),
amr_distance = mean_amr_distance(y),
check_id_C = amr_distance_from_row(amr_distance, id == "C")
) \%>\%
arrange(check_id_C)
@ -76,7 +86,7 @@ if (require("dplyr")) {
filter(mo_genus() == "Enterococcus" & mo_species() != "") \%>\%
select(mo, TCY, carbapenems()) \%>\%
group_by(mo) \%>\%
mutate(d = mean_amr_distance(., where(is.rsi))) \%>\%
arrange(mo, d)
mutate(dist = mean_amr_distance(.)) \%>\%
arrange(mo, dist)
}
}