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(v1.8.1.9012) update prevalence of some genera

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dr. M.S. (Matthijs) Berends
2022-06-03 13:28:55 +02:00
parent 1b84564d36
commit b84d647cac
37 changed files with 2909 additions and 2833 deletions
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2
R/atc_online.R
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@ -51,7 +51,7 @@
#'
#' - `"g"` = gram
#' - `"mg"` = milligram
#' - `"mcg"`` = microgram
#' - `"mcg"` = microgram
#' - `"U"` = unit
#' - `"TU"` = thousand units
#' - `"MU"` = million units

6
R/custom_eucast_rules.R
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@ -27,7 +27,7 @@
#'
#' Define custom EUCAST rules for your organisation or specific analysis and use the output of this function in [eucast_rules()].
#' @inheritSection lifecycle Stable Lifecycle
#' @param ... rules in formula notation, see *Examples*
#' @param ... rules in [formula][`~`()] notation, see *Examples*
#' @details
#' Some organisations have their own adoption of EUCAST rules. This function can be used to define custom EUCAST rules to be used in the [eucast_rules()] function.
#'
@ -75,7 +75,7 @@
#'
#' ### Using taxonomic properties in rules
#'
#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), quote = "``", sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), quote = "\u0096", sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
#'
#' ```
#' y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
@ -91,7 +91,7 @@
#'
#' It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part *after* the tilde. In above examples, the antibiotic group `aminopenicillins` is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the agents that will be matched when running the rule.
#'
#' `r paste0(" * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("``", tolower(gsub("^AB_", "", x)), "``\\cr(", vector_and(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), quotes = FALSE), ")"), USE.NAMES = FALSE), "\n", collapse = "")`
#' `r paste0(" * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("\u0096", tolower(gsub("^AB_", "", x)), "\u0096\\cr(", vector_and(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), quotes = FALSE), ")"), USE.NAMES = FALSE), "\n", collapse = "")`
#' @returns A [list] containing the custom rules
#' @inheritSection AMR Read more on Our Website!
#' @export

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R/resistance_predict.R
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@ -34,7 +34,7 @@
#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
#' @param year_every unit of sequence between lowest year found in the data and `year_max`
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
#' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)``, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See *Details* for all valid options.
#' @param model the statistical model of choice. This could be a generalised linear regression model with binomial distribution (i.e. using `glm(..., family = binomial)`, assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See *Details* for all valid options.
#' @param I_as_S a [logical] to indicate whether values `"I"` should be treated as `"S"` (will otherwise be treated as `"R"`). The default, `TRUE`, follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section *Interpretation of S, I and R* below.
#' @param preserve_measurements a [logical] to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be `NA`.
#' @param info a [logical] to indicate whether textual analysis should be printed with the name and [summary()] of the statistical model.