(v0.8.0.9037) complete documentation rewrite

R/amr.R

@@ -46,7 +46,7 @@
 #' For suggestions, comments or questions, please contact us at:
 #'
 #' Matthijs S. Berends \cr
-#' m.s.berends [at] umcg [dot] nl \cr
+#' m.s.berends at umcg dot nl \cr
 #' Department of Medical Microbiology, University of Groningen \cr
 #' University Medical Center Groningen \cr
 #' Post Office Box 30001 \cr

R/data.R

@@ -80,7 +80,7 @@
 #' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
 #' @details Manually added were:
 #' - 11 entries of *Streptococcus* (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
-#' - 2 entries of *Staphylococcus* (coagulase-negative [CoNS] and coagulase-positive [CoPS])
+#' - 2 entries of *Staphylococcus* (coagulase-negative (CoNS) and coagulase-positive (CoPS))
 #' - 3 entries of *Trichomonas* (*Trichomonas vaginalis*, and its family and genus)
 #' - 1 entry of *Blastocystis* (*Blastocystis hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993)
 #' - 5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)

R/first_isolate.R

@@ -41,7 +41,7 @@
 #' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
 #' @param ... parameters passed on to the [first_isolate()] function
 #' @details **WHY THIS IS SO IMPORTANT** \cr
-#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [[1]](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias).
+#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [(ref)](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias).
 #'
 #' All isolates with a microbial ID of `NA` will be excluded as first isolate.
 #'

R/g.test.R

@@ -21,7 +21,7 @@
 
 #' *G*-test for Count Data
 #'
-#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable [1]. A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**).
+#' [g.test()] performs chi-squared contingency table tests and goodness-of-fit tests, just like [chisq.test()] but is more reliable (1). A *G*-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a ***G*-test of goodness-of-fit**), or to see whether the proportions of one variable are different for different values of the other variable (called a ***G*-test of independence**).
 #' @inherit stats::chisq.test params return
 #' @details If `x` is a matrix with one row or column, or if `x` is a vector and `y` is not given, then a *goodness-of-fit test* is performed (`x` is treated as a one-dimensional contingency table). The entries of `x` must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in `p`, or are all equal if `p` is not given.
 #'
@@ -64,7 +64,7 @@
 #'
 #' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use *G*-tests for each category, of course.
 #' @seealso [chisq.test()]
-#' @references [1] McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. <http://www.biostathandbook.com/gtestgof.html>.
+#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. <http://www.biostathandbook.com/gtestgof.html>.
 #' @source The code for this function is identical to that of [chisq.test()], except that:
 #' - The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))}
 #' - Yates' continuity correction was removed as it does not apply to a *G*-test

R/join_microorganisms.R

@@ -29,7 +29,7 @@
 #' @param by a variable to join by - if left empty will search for a column with class [`mo`] (created with [as.mo()]) or will be `"mo"` if that column name exists in `x`, could otherwise be a column name of `x` with values that exist in `microorganisms$mo` (like `by = "bacteria_id"`), or another column in [microorganisms] (but then it should be named, like `by = c("my_genus_species" = "fullname")`)
 #' @param suffix if there are non-joined duplicate variables in `x` and `y`, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
 #' @param ... other parameters to pass on to [dplyr::join()]
-#' @details **Note:** As opposed to the [dplyr::join()] functions of `dplyr`, [`characters`] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. See [dplyr::join()] for more information.
+#' @details **Note:** As opposed to the [dplyr::join()] functions of `dplyr`, [`character`] vectors are supported and at default existing columns will get a suffix `"2"` and the newly joined columns will not get a suffix. See [dplyr::join()] for more information.
 #' @inheritSection AMR Read more on our website!
 #' @export
 #' @examples

R/kurtosis.R

@@ -22,7 +22,7 @@
 #' Kurtosis of the sample
 #'
 #' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
-#' @param x a vector of values, a [`matrix`] or a [`data frame`]
+#' @param x a vector of values, a [`matrix`] or a [`data.frame`]
 #' @param na.rm a logical value indicating whether `NA` values should be stripped before the computation proceeds.
 #' @exportMethod kurtosis
 #' @seealso [skewness()]

R/mdro.R

@@ -43,7 +43,7 @@
 #' - `guideline = "MRGN"`\cr
 #'   The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
 #' - `guideline = "BRMO"`\cr
-#'   The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) [ZKH]" ([link](https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH))
+#'   The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" ([link](https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH))
 #'
 #' Please suggest your own (country-specific) guidelines by letting us know: <https://gitlab.com/msberends/AMR/issues/new>.
 #' 

R/mic.R

@@ -27,7 +27,7 @@
 #' @param na.rm a logical indicating whether missing values should be removed
 #' @details To interpret MIC values as RSI values, use [as.rsi()] on MIC values. It supports guidelines from EUCAST and CLSI.
 #' @return Ordered [`factor`] with new class [`mic`]
-#' @aliases MIC
+#' @aliases mic
 #' @export
 #' @importFrom dplyr %>%
 #' @seealso [as.rsi()]

R/rsi.R

@@ -46,7 +46,7 @@
 #'
 #' This AMR package honours this new insight. Use [susceptibility()] (equal to [proportion_SI()]) to determine antimicrobial susceptibility and [count_susceptible()] (equal to [count_SI()]) to count susceptible isolates.
 #' @return Ordered factor with new class [`rsi`]
-#' @aliases RSI
+#' @aliases rsi
 #' @export
 #' @importFrom dplyr %>% desc arrange filter 
 #' @seealso [as.mic()]