mirror of
https://github.com/msberends/AMR.git
synced 2024-12-25 20:06:12 +01:00
resistance predict
This commit is contained in:
parent
cda7087722
commit
046d195064
@ -1,6 +1,6 @@
|
||||
Package: AMR
|
||||
Version: 0.5.0.9009
|
||||
Date: 2019-01-12
|
||||
Date: 2019-01-15
|
||||
Title: Antimicrobial Resistance Analysis
|
||||
Authors@R: c(
|
||||
person(
|
||||
|
@ -20,6 +20,7 @@ S3method(kurtosis,default)
|
||||
S3method(kurtosis,matrix)
|
||||
S3method(plot,frequency_tbl)
|
||||
S3method(plot,mic)
|
||||
S3method(plot,resistance_predict)
|
||||
S3method(plot,rsi)
|
||||
S3method(print,atc)
|
||||
S3method(print,frequency_tbl)
|
||||
@ -76,6 +77,7 @@ export(g.test)
|
||||
export(geom_rsi)
|
||||
export(get_locale)
|
||||
export(ggplot_rsi)
|
||||
export(ggplot_rsi_predict)
|
||||
export(guess_ab_col)
|
||||
export(guess_atc)
|
||||
export(guess_mo)
|
||||
@ -215,6 +217,7 @@ importFrom(dplyr,mutate_all)
|
||||
importFrom(dplyr,mutate_at)
|
||||
importFrom(dplyr,n)
|
||||
importFrom(dplyr,n_distinct)
|
||||
importFrom(dplyr,n_groups)
|
||||
importFrom(dplyr,progress_estimated)
|
||||
importFrom(dplyr,pull)
|
||||
importFrom(dplyr,row_number)
|
||||
@ -228,6 +231,7 @@ importFrom(dplyr,top_n)
|
||||
importFrom(dplyr,ungroup)
|
||||
importFrom(dplyr,vars)
|
||||
importFrom(grDevices,boxplot.stats)
|
||||
importFrom(graphics,arrows)
|
||||
importFrom(graphics,axis)
|
||||
importFrom(graphics,barplot)
|
||||
importFrom(graphics,hist)
|
||||
|
6
NEWS.md
6
NEWS.md
@ -12,6 +12,12 @@
|
||||
* Function `mo_renamed()` to get a list of all returned values from `as.mo()` that have had taxonomic renaming
|
||||
* Function `age()` to calculate the (patients) age in years
|
||||
* Function `age_groups()` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
|
||||
* Function `ggplot_rsi_predict()` as well as the base R `plot()` function can now be used for resistance prediction calculated with `resistance_predict()`:
|
||||
```r
|
||||
x <- resistance_predict(septic_patients, col_ab = "amox")
|
||||
plot(x)
|
||||
ggplot_rsi_predict(x)
|
||||
```
|
||||
* Functions `filter_first_isolate()` and `filter_first_weighted_isolate()` to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
|
||||
```r
|
||||
septic_patients %>% filter_first_isolate(...)
|
||||
|
@ -231,9 +231,8 @@ eucast_rules <- function(tbl,
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
|
||||
col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
|
@ -23,7 +23,7 @@
|
||||
#'
|
||||
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
|
||||
#' @param tbl a \code{data.frame} containing isolates.
|
||||
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}
|
||||
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class
|
||||
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
|
||||
#' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.
|
||||
#' @param col_testcode column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
|
||||
@ -187,9 +187,8 @@ first_isolate <- function(tbl,
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
|
||||
col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
@ -197,33 +196,25 @@ first_isolate <- function(tbl,
|
||||
|
||||
# -- date
|
||||
if (is.null(col_date)) {
|
||||
for (i in 1:ncol(tbl)) {
|
||||
if ("Date" %in% class(tbl %>% pull(i)) | "POSIXct" %in% class(tbl %>% pull(i))) {
|
||||
col_date <- colnames(tbl)[i]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_date), "` as input for `col_date`.")))
|
||||
break
|
||||
}
|
||||
}
|
||||
col_date <- search_type_in_df(tbl = tbl, type = "date")
|
||||
}
|
||||
if (is.null(col_date)) {
|
||||
stop("`col_date` must be set.", call. = FALSE)
|
||||
}
|
||||
# convert to Date (pipes for supporting tibbles too)
|
||||
# convert to Date (pipes/pull for supporting tibbles too)
|
||||
tbl[, col_date] <- tbl %>% pull(col_date) %>% as.Date()
|
||||
|
||||
# -- patient id
|
||||
if (is.null(col_patient_id) & any(colnames(tbl) %like% "^(patient|patid)")) {
|
||||
col_patient_id <- colnames(tbl)[colnames(tbl) %like% "^(patient|patid)"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_patient_id), "` as input for `col_patient_id`.")))
|
||||
if (is.null(col_patient_id)) {
|
||||
col_patient_id <- search_type_in_df(tbl = tbl, type = "patient_id")
|
||||
}
|
||||
if (is.null(col_patient_id)) {
|
||||
stop("`col_patient_id` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
# -- key antibiotics
|
||||
if (is.null(col_keyantibiotics) & any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
|
||||
col_keyantibiotics <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_keyantibiotics), "` as input for `col_keyantibiotics`. Use ", bold("col_keyantibiotics = FALSE"), " to prevent this.")))
|
||||
if (is.null(col_keyantibiotics)) {
|
||||
col_keyantibiotics <- search_type_in_df(tbl = tbl, type = "keyantibiotics")
|
||||
}
|
||||
if (isFALSE(col_keyantibiotics)) {
|
||||
col_keyantibiotics <- NULL
|
||||
|
@ -101,9 +101,8 @@ key_antibiotics <- function(tbl,
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
|
||||
col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
@ -114,7 +113,6 @@ key_antibiotics <- function(tbl,
|
||||
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
|
||||
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = warnings)
|
||||
print(col.list)
|
||||
universal_1 <- col.list[universal_1]
|
||||
universal_2 <- col.list[universal_2]
|
||||
universal_3 <- col.list[universal_3]
|
||||
|
5
R/mdro.R
5
R/mdro.R
@ -113,9 +113,8 @@ mdro <- function(tbl,
|
||||
|
||||
# try to find columns based on type
|
||||
# -- mo
|
||||
if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
|
||||
col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
|
||||
if (is.null(col_mo)) {
|
||||
col_mo <- search_type_in_df(tbl = tbl, type = "mo")
|
||||
}
|
||||
if (is.null(col_mo)) {
|
||||
stop("`col_mo` must be set.", call. = FALSE)
|
||||
|
45
R/misc.R
45
R/misc.R
@ -123,3 +123,48 @@ size_humanreadable <- function(bytes, decimals = 1) {
|
||||
out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
|
||||
out
|
||||
}
|
||||
|
||||
#' @importFrom crayon blue bold
|
||||
#' @importFrom dplyr %>% pull
|
||||
search_type_in_df <- function(tbl, type) {
|
||||
# try to find columns based on type
|
||||
found <- NULL
|
||||
|
||||
# -- mo
|
||||
if (type == "mo") {
|
||||
if ("mo" %in% lapply(tbl, class)) {
|
||||
found <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
|
||||
}
|
||||
}
|
||||
# -- key antibiotics
|
||||
if (type == "keyantibiotics") {
|
||||
if (any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
|
||||
}
|
||||
}
|
||||
# -- date
|
||||
if (type == "date") {
|
||||
for (i in 1:ncol(tbl)) {
|
||||
if ("Date" %in% class(tbl %>% pull(i)) | "POSIXct" %in% class(tbl %>% pull(i))) {
|
||||
found <- colnames(tbl)[i]
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
# -- patient id
|
||||
if (type == "patient_id") {
|
||||
if (any(colnames(tbl) %like% "^(patient|patid)")) {
|
||||
found <- colnames(tbl)[colnames(tbl) %like% "^(patient|patid)"][1]
|
||||
}
|
||||
}
|
||||
|
||||
if (!is.null(found)) {
|
||||
msg <- paste0("NOTE: Using column `", bold(found), "` as input for `col_", type, "`.")
|
||||
if (type == "keyantibiotics") {
|
||||
msg <- paste(msg, "Use", bold("col_keyantibiotics = FALSE"), "to prevent this.")
|
||||
}
|
||||
message(blue(msg))
|
||||
}
|
||||
found
|
||||
}
|
||||
|
@ -23,17 +23,18 @@
|
||||
#'
|
||||
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
#' @inheritParams first_isolate
|
||||
#' @inheritParams graphics::plot
|
||||
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
|
||||
#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}
|
||||
#' @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 \code{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. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
|
||||
#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) or \code{"linear"} (or \code{"lin"}).
|
||||
#' @param I_as_R a logical to indicate whether values \code{I} should be treated as \code{R}
|
||||
#' @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 \code{NA}.
|
||||
#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
|
||||
#' @return \code{data.frame} with columns:
|
||||
#' @return \code{data.frame} with extra class \code{"resistance_predict"} with columns:
|
||||
#' \itemize{
|
||||
#' \item{\code{year}}
|
||||
#' \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
@ -47,42 +48,23 @@
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
#' @importFrom stats predict glm lm
|
||||
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when
|
||||
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when n_groups
|
||||
#' @inheritSection AMR Read more on our website!
|
||||
#' @examples
|
||||
#' \dontrun{
|
||||
#' # use it with base R:
|
||||
#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
#' col_ab = "amcl", col_date = "date")
|
||||
#' x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
|
||||
#' plot(x)
|
||||
#' ggplot_rsi_predict(x)
|
||||
#'
|
||||
#' # or use dplyr so you can actually read it:
|
||||
#' # use dplyr so you can actually read it:
|
||||
#' library(dplyr)
|
||||
#' tbl %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Haemophilus") %>%
|
||||
#' resistance_predict(amcl, date)
|
||||
#' }
|
||||
#' x <- septic_patients %>%
|
||||
#' filter_first_isolate() %>%
|
||||
#' filter(mo_genus(mo) == "Staphylococcus") %>%
|
||||
#' resistance_predict("peni")
|
||||
#' plot(x)
|
||||
#'
|
||||
#'
|
||||
#' # real live example:
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' # get bacteria properties like genus and species
|
||||
#' left_join_microorganisms("mo") %>%
|
||||
#' # calculate first isolates
|
||||
#' mutate(first_isolate = first_isolate(.)) %>%
|
||||
#' # filter on first E. coli isolates
|
||||
#' filter(genus == "Escherichia",
|
||||
#' species == "coli",
|
||||
#' first_isolate == TRUE) %>%
|
||||
#' # predict resistance of cefotaxime for next years
|
||||
#' resistance_predict(col_ab = "cfot",
|
||||
#' col_date = "date",
|
||||
#' year_max = 2025,
|
||||
#' preserve_measurements = TRUE,
|
||||
#' minimum = 0)
|
||||
#'
|
||||
#' # create nice plots with ggplot
|
||||
#' # create nice plots with ggplot yourself
|
||||
#' if (!require(ggplot2)) {
|
||||
#'
|
||||
#' data <- septic_patients %>%
|
||||
@ -110,7 +92,7 @@
|
||||
#' }
|
||||
resistance_predict <- function(tbl,
|
||||
col_ab,
|
||||
col_date,
|
||||
col_date = NULL,
|
||||
year_min = NULL,
|
||||
year_max = NULL,
|
||||
year_every = 1,
|
||||
@ -128,23 +110,23 @@ resistance_predict <- function(tbl,
|
||||
stop('Column ', col_ab, ' not found.')
|
||||
}
|
||||
|
||||
# -- date
|
||||
if (is.null(col_date)) {
|
||||
col_date <- search_type_in_df(tbl = tbl, type = "date")
|
||||
}
|
||||
if (is.null(col_date)) {
|
||||
stop("`col_date` must be set.", call. = FALSE)
|
||||
}
|
||||
|
||||
if (!col_date %in% colnames(tbl)) {
|
||||
stop('Column ', col_date, ' not found.')
|
||||
}
|
||||
if ('grouped_df' %in% class(tbl)) {
|
||||
|
||||
if (n_groups(tbl) > 1) {
|
||||
# no grouped tibbles please, mutate will throw errors
|
||||
tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
|
||||
}
|
||||
|
||||
if (I_as_R == TRUE) {
|
||||
tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
|
||||
}
|
||||
|
||||
tbl <- tbl %>%
|
||||
mutate_at(col_ab, as.rsi) %>%
|
||||
filter_at(col_ab, all_vars(!is.na(.)))
|
||||
tbl[, col_ab] <- droplevels(tbl[, col_ab])
|
||||
|
||||
year <- function(x) {
|
||||
if (all(grepl('^[0-9]{4}$', x))) {
|
||||
x
|
||||
@ -154,13 +136,23 @@ resistance_predict <- function(tbl,
|
||||
}
|
||||
|
||||
df <- tbl %>%
|
||||
mutate(year = tbl %>% pull(col_date) %>% year()) %>%
|
||||
mutate_at(col_ab, as.rsi) %>%
|
||||
mutate_at(col_ab, droplevels) %>%
|
||||
mutate_at(col_ab, funs(
|
||||
if (I_as_R == TRUE) {
|
||||
gsub("I", "R", .)
|
||||
} else {
|
||||
gsub("I", "S", .)
|
||||
}
|
||||
)) %>%
|
||||
filter_at(col_ab, all_vars(!is.na(.))) %>%
|
||||
mutate(year = pull(., col_date) %>% year()) %>%
|
||||
group_by_at(c('year', col_ab)) %>%
|
||||
summarise(n())
|
||||
|
||||
if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
|
||||
stop("No variety in antimicrobial interpretations - all isolates are '",
|
||||
df %>% pull(col_ab) %>% unique() %>% .[!is.na(.)], "'.",
|
||||
df %>% pull(col_ab) %>% unique(), "'.",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
@ -168,8 +160,11 @@ resistance_predict <- function(tbl,
|
||||
df <- df %>%
|
||||
filter(!is.na(antibiotic)) %>%
|
||||
tidyr::spread(antibiotic, observations, fill = 0) %>%
|
||||
mutate(total = R + S) %>%
|
||||
filter(total >= minimum)
|
||||
filter((R + S) >= minimum)
|
||||
df_matrix <- df %>%
|
||||
ungroup() %>%
|
||||
select(R, S) %>%
|
||||
as.matrix()
|
||||
|
||||
if (NROW(df) == 0) {
|
||||
stop('There are no observations.')
|
||||
@ -185,41 +180,44 @@ resistance_predict <- function(tbl,
|
||||
year_max <- year(Sys.Date()) + 10
|
||||
}
|
||||
|
||||
years_predict <- seq(from = year_min, to = year_max, by = year_every)
|
||||
years <- list(year = seq(from = year_min, to = year_max, by = year_every))
|
||||
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
|
||||
model <- "binomial"
|
||||
model_lm <- with(df, glm(df_matrix ~ year, family = binomial))
|
||||
if (info == TRUE) {
|
||||
cat('\nLogistic regression model (logit) with binomial distribution')
|
||||
cat('\n------------------------------------------------------------\n')
|
||||
print(summary(logitmodel))
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model == 'loglin') {
|
||||
loglinmodel <- with(df, glm(R ~ year, family = poisson))
|
||||
} else if (model %in% c('loglin', 'poisson')) {
|
||||
model <- "poisson"
|
||||
model_lm <- with(df, glm(R ~ year, family = poisson))
|
||||
if (info == TRUE) {
|
||||
cat('\nLog-linear regression model (loglin) with poisson distribution')
|
||||
cat('\n--------------------------------------------------------------\n')
|
||||
print(summary(loglinmodel))
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model %in% c('lin', 'linear')) {
|
||||
linmodel <- with(df, lm((R / (R + S)) ~ year))
|
||||
model <- "linear"
|
||||
model_lm <- with(df, lm((R / (R + S)) ~ year))
|
||||
if (info == TRUE) {
|
||||
cat('\nLinear regression model')
|
||||
cat('\n-----------------------\n')
|
||||
print(summary(linmodel))
|
||||
print(summary(model_lm))
|
||||
}
|
||||
|
||||
predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
|
||||
predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
@ -228,64 +226,117 @@ resistance_predict <- function(tbl,
|
||||
}
|
||||
|
||||
# prepare the output dataframe
|
||||
prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)
|
||||
df_prediction <- data.frame(year = unlist(years),
|
||||
value = prediction,
|
||||
stringsAsFactors = FALSE) %>%
|
||||
|
||||
prediction$se_min <- prediction$value - se
|
||||
prediction$se_max <- prediction$value + se
|
||||
mutate(se_min = value - se,
|
||||
se_max = value + se)
|
||||
|
||||
if (model == 'loglin') {
|
||||
prediction$value <- prediction$value %>%
|
||||
if (model == 'poisson') {
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = value %>%
|
||||
format(scientific = FALSE) %>%
|
||||
as.integer()
|
||||
prediction$se_min <- prediction$se_min %>% as.integer()
|
||||
prediction$se_max <- prediction$se_max %>% as.integer()
|
||||
|
||||
colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
|
||||
as.integer(),
|
||||
se_min = as.integer(se_min),
|
||||
se_max = as.integer(se_max))
|
||||
} else {
|
||||
prediction$se_max[which(prediction$se_max > 1)] <- 1
|
||||
df_prediction <- df_prediction %>%
|
||||
# se_max not above 1
|
||||
mutate(se_max = ifelse(se_max > 1, 1, se_max))
|
||||
}
|
||||
prediction$se_min[which(prediction$se_min < 0)] <- 0
|
||||
prediction$observations = NA
|
||||
df_prediction <- df_prediction %>%
|
||||
# se_min not below 0
|
||||
mutate(se_min = ifelse(se_min < 0, 0, se_min))
|
||||
|
||||
total <- prediction
|
||||
df_observations <- df %>%
|
||||
ungroup() %>%
|
||||
transmute(year,
|
||||
observations = R + S,
|
||||
observed = R / (R + S))
|
||||
df_prediction <- df_prediction %>%
|
||||
left_join(df_observations, by = "year") %>%
|
||||
mutate(estimated = value)
|
||||
|
||||
if (preserve_measurements == TRUE) {
|
||||
# replace estimated data by observed data
|
||||
if (I_as_R == TRUE) {
|
||||
if (!'I' %in% colnames(df)) {
|
||||
df$I <- 0
|
||||
}
|
||||
df$value <- df$R / rowSums(df[, c('R', 'S', 'I')])
|
||||
} else {
|
||||
df$value <- df$R / rowSums(df[, c('R', 'S')])
|
||||
}
|
||||
measurements <- data.frame(year = df$year,
|
||||
value = df$value,
|
||||
se_min = NA,
|
||||
se_max = NA,
|
||||
observations = df$total,
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(measurements) <- colnames(prediction)
|
||||
|
||||
total <- rbind(measurements,
|
||||
prediction %>% filter(!year %in% df$year))
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
total <- total %>% mutate(observed = ifelse(is.na(observations), NA, value),
|
||||
estimated = prediction$value)
|
||||
}
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = ifelse(!is.na(observed), observed, value),
|
||||
se_min = ifelse(!is.na(observed), NA, se_min),
|
||||
se_max = ifelse(!is.na(observed), NA, se_max))
|
||||
}
|
||||
|
||||
if ("value" %in% colnames(total)) {
|
||||
total <- total %>%
|
||||
df_prediction <- df_prediction %>%
|
||||
mutate(value = case_when(value > 1 ~ 1,
|
||||
value < 0 ~ 0,
|
||||
TRUE ~ value))
|
||||
}
|
||||
|
||||
total %>% arrange(year)
|
||||
TRUE ~ value)) %>%
|
||||
arrange(year)
|
||||
|
||||
structure(
|
||||
.Data = df_prediction,
|
||||
class = c("resistance_predict", "data.frame"),
|
||||
I_as_R = I_as_R,
|
||||
model_title = model,
|
||||
model = model_lm,
|
||||
ab = col_ab
|
||||
)
|
||||
}
|
||||
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
rsi_predict <- resistance_predict
|
||||
|
||||
#' @exportMethod plot.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise
|
||||
#' @importFrom graphics plot axis arrows
|
||||
#' @rdname resistance_predict
|
||||
plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
|
||||
if (attributes(x)$I_as_R == TRUE) {
|
||||
ylab <- "%IR"
|
||||
} else {
|
||||
ylab <- "%R"
|
||||
}
|
||||
plot(x = x$year,
|
||||
y = x$value,
|
||||
ylim = c(0, 1),
|
||||
yaxt = "n", # no y labels
|
||||
pch = 19, # closed dots
|
||||
ylab = paste0("Percentage (", ylab, ")"),
|
||||
xlab = "Year",
|
||||
main = main,
|
||||
sub = paste0("(model: ", attributes(x)$model_title, ")"))
|
||||
|
||||
axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
|
||||
|
||||
# arrows hack: https://stackoverflow.com/a/22037078/4575331
|
||||
arrows(x0 = x$year,
|
||||
y0 = x$se_min,
|
||||
x1 = x$year,
|
||||
y1 = x$se_max, length = 0.05, angle = 90, code = 3)
|
||||
}
|
||||
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
ggplot_rsi_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
|
||||
|
||||
if (!"resistance_predict" %in% class(x)) {
|
||||
stop("`x` must be a resistance prediction model created with resistance_predict().")
|
||||
}
|
||||
|
||||
if (attributes(x)$I_as_R == TRUE) {
|
||||
ylab <- "%IR"
|
||||
} else {
|
||||
ylab <- "%R"
|
||||
}
|
||||
suppressWarnings(
|
||||
ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
|
||||
ggplot2::geom_col() +
|
||||
ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max)) +
|
||||
scale_y_percent() +
|
||||
labs(title = main,
|
||||
y = paste0("Percentage (", ylab, ")"),
|
||||
x = "Year",
|
||||
caption = paste0("(model: ", attributes(x)$model_title, ")"))
|
||||
)
|
||||
}
|
||||
|
@ -238,6 +238,8 @@
|
||||
<li>Function <code><a href="../reference/mo_renamed.html">mo_renamed()</a></code> to get a list of all returned values from <code><a href="../reference/as.mo.html">as.mo()</a></code> that have had taxonomic renaming</li>
|
||||
<li>Function <code><a href="../reference/age.html">age()</a></code> to calculate the (patients) age in years</li>
|
||||
<li>Function <code><a href="../reference/age_groups.html">age_groups()</a></code> to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.</li>
|
||||
<li>Function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> as well as the base R <code><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot()</a></code> function can now be used for resistance prediction calculated with <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>: <code>r x <- resistance_predict(septic_patients, col_ab = "amox") plot(x) ggplot_rsi_predict(x)</code>
|
||||
</li>
|
||||
<li>Functions <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> and <code><a href="../reference/first_isolate.html">filter_first_weighted_isolate()</a></code> to shorten and fasten filtering on data sets with antimicrobial results, e.g.: <code>r septic_patients %>% filter_first_isolate(...) # or filter_first_isolate(septic_patients, ...)</code> is equal to: <code>r septic_patients %>% mutate(only_firsts = first_isolate(septic_patients, ...)) %>% filter(only_firsts == TRUE) %>% select(-only_firsts)</code>
|
||||
</li>
|
||||
<li>New vignettes about how to conduct AMR analysis, predict antimicrobial resistance, use the <em>G</em>-test and more. These are also available (and even easier readable) on our website: <a href="https://msberends.gitlab.io/AMR" class="uri">https://msberends.gitlab.io/AMR</a>.</li>
|
||||
|
@ -250,7 +250,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<th>col_date</th>
|
||||
<td><p>column name of the result date (or date that is was received on the lab), defaults to the first column of class <code>Date</code></p></td>
|
||||
<td><p>column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>col_patient_id</th>
|
||||
|
@ -415,7 +415,7 @@
|
||||
</tr><tr>
|
||||
|
||||
<td>
|
||||
<p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> </p>
|
||||
<p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> <code><a href="resistance_predict.html">plot(<i><resistance_predict></i>)</a></code> <code><a href="resistance_predict.html">ggplot_rsi_predict()</a></code> </p>
|
||||
</td>
|
||||
<td><p>Predict antimicrobial resistance</p></td>
|
||||
</tr><tr>
|
||||
|
@ -227,14 +227,22 @@
|
||||
|
||||
</div>
|
||||
|
||||
<pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
|
||||
<pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
|
||||
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
|
||||
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
|
||||
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
|
||||
|
||||
<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
|
||||
<span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>, <span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
|
||||
<span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
|
||||
<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
|
||||
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
|
||||
<span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
|
||||
<span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
|
||||
|
||||
<span class='co'># S3 method for resistance_predict</span>
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>,
|
||||
<span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)
|
||||
|
||||
<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>, <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>,
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)</pre>
|
||||
|
||||
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
|
||||
<table class="ref-arguments">
|
||||
@ -249,7 +257,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<th>col_date</th>
|
||||
<td><p>column name of the date, will be used to calculate years if this column doesn't consist of years already</p></td>
|
||||
<td><p>column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>year_min</th>
|
||||
@ -269,7 +277,7 @@
|
||||
</tr>
|
||||
<tr>
|
||||
<th>model</th>
|
||||
<td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
|
||||
<td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> (or <code>"poisson"</code>) or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>I_as_R</th>
|
||||
@ -283,11 +291,21 @@
|
||||
<th>info</th>
|
||||
<td><p>a logical to indicate whether textual analysis should be printed with the name and <code><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></code> of the statistical model.</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>x</th>
|
||||
<td><p>the coordinates of points in the plot. Alternatively, a
|
||||
single plotting structure, function or <em>any <span style="R">R</span> object with a
|
||||
<code>plot</code> method</em> can be provided.</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>...</th>
|
||||
<td><p>parameters passed on to the <code>first_isolate</code> function</p></td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
|
||||
|
||||
<p><code>data.frame</code> with columns:</p><ul>
|
||||
<p><code>data.frame</code> with extra class <code>"resistance_predict"</code> with columns:</p><ul>
|
||||
<li><p><code>year</code></p></li>
|
||||
<li><p><code>value</code>, the same as <code>estimated</code> when <code>preserve_measurements = FALSE</code>, and a combination of <code>observed</code> and <code>estimated</code> otherwise</p></li>
|
||||
<li><p><code>se_min</code>, the lower bound of the standard error with a minimum of <code>0</code> (so the standard error will never go below 0%)</p></li>
|
||||
@ -311,37 +329,20 @@ On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitla
|
||||
|
||||
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
|
||||
<pre class="examples"><span class='co'># NOT RUN {</span>
|
||||
<span class='co'># use it with base R:</span>
|
||||
<span class='fu'>resistance_predict</span>(<span class='kw'>tbl</span> <span class='kw'>=</span> <span class='no'>tbl</span>[<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/which'>which</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span> <span class='kw'>&</span> <span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>),],
|
||||
<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amcl"</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>)
|
||||
<span class='no'>x</span> <span class='kw'><-</span> <span class='fu'>resistance_predict</span>(<span class='no'>septic_patients</span>, <span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amox"</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='fl'>2010</span>)
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>)
|
||||
<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>)
|
||||
|
||||
<span class='co'># or use dplyr so you can actually read it:</span>
|
||||
<span class='co'># use dplyr so you can actually read it:</span>
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
|
||||
<span class='no'>tbl</span> <span class='kw'>%>%</span>
|
||||
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>,
|
||||
<span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>) <span class='kw'>%>%</span>
|
||||
<span class='fu'>resistance_predict</span>(<span class='no'>amcl</span>, <span class='no'>date</span>)
|
||||
<span class='co'># }</span><span class='co'># NOT RUN {</span>
|
||||
<span class='no'>x</span> <span class='kw'><-</span> <span class='no'>septic_patients</span> <span class='kw'>%>%</span>
|
||||
<span class='fu'><a href='first_isolate.html'>filter_first_isolate</a></span>() <span class='kw'>%>%</span>
|
||||
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%>%</span>
|
||||
<span class='fu'>resistance_predict</span>(<span class='st'>"peni"</span>)
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>)
|
||||
|
||||
<span class='co'># real live example:</span>
|
||||
<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
|
||||
<span class='no'>septic_patients</span> <span class='kw'>%>%</span>
|
||||
<span class='co'># get bacteria properties like genus and species</span>
|
||||
<span class='fu'><a href='join.html'>left_join_microorganisms</a></span>(<span class='st'>"mo"</span>) <span class='kw'>%>%</span>
|
||||
<span class='co'># calculate first isolates</span>
|
||||
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>first_isolate</span> <span class='kw'>=</span> <span class='fu'><a href='first_isolate.html'>first_isolate</a></span>(<span class='no'>.</span>)) <span class='kw'>%>%</span>
|
||||
<span class='co'># filter on first E. coli isolates</span>
|
||||
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Escherichia"</span>,
|
||||
<span class='no'>species</span> <span class='kw'>==</span> <span class='st'>"coli"</span>,
|
||||
<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>) <span class='kw'>%>%</span>
|
||||
<span class='co'># predict resistance of cefotaxime for next years</span>
|
||||
<span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"cfot"</span>,
|
||||
<span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
|
||||
<span class='kw'>year_max</span> <span class='kw'>=</span> <span class='fl'>2025</span>,
|
||||
<span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
|
||||
<span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>0</span>)
|
||||
|
||||
<span class='co'># create nice plots with ggplot</span>
|
||||
<span class='co'># create nice plots with ggplot yourself</span>
|
||||
<span class='kw'>if</span> (!<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>require</a></span>(<span class='no'>ggplot2</span>)) {
|
||||
|
||||
<span class='no'>data</span> <span class='kw'><-</span> <span class='no'>septic_patients</span> <span class='kw'>%>%</span>
|
||||
|
@ -26,7 +26,7 @@ filter_first_weighted_isolate(tbl, col_date = NULL,
|
||||
\arguments{
|
||||
\item{tbl}{a \code{data.frame} containing isolates.}
|
||||
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}}
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
|
||||
|
||||
\item{col_patient_id}{column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)}
|
||||
|
||||
|
@ -3,23 +3,32 @@
|
||||
\name{resistance_predict}
|
||||
\alias{resistance_predict}
|
||||
\alias{rsi_predict}
|
||||
\alias{plot.resistance_predict}
|
||||
\alias{ggplot_rsi_predict}
|
||||
\title{Predict antimicrobial resistance}
|
||||
\usage{
|
||||
resistance_predict(tbl, col_ab, col_date, year_min = NULL,
|
||||
resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
|
||||
year_max = NULL, year_every = 1, minimum = 30,
|
||||
model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
|
||||
info = TRUE)
|
||||
|
||||
rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
|
||||
year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
|
||||
preserve_measurements = TRUE, info = TRUE)
|
||||
rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
|
||||
year_max = NULL, year_every = 1, minimum = 30,
|
||||
model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
|
||||
info = TRUE)
|
||||
|
||||
\method{plot}{resistance_predict}(x,
|
||||
main = paste("Resistance prediction of", attributes(x)$ab), ...)
|
||||
|
||||
ggplot_rsi_predict(x, main = paste("Resistance prediction of",
|
||||
attributes(x)$ab), ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{tbl}{a \code{data.frame} containing isolates.}
|
||||
|
||||
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
|
||||
|
||||
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already}
|
||||
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class}
|
||||
|
||||
\item{year_min}{lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}}
|
||||
|
||||
@ -29,16 +38,22 @@ rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
|
||||
|
||||
\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
|
||||
|
||||
\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).}
|
||||
\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) or \code{"linear"} (or \code{"lin"}).}
|
||||
|
||||
\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
|
||||
|
||||
\item{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 \code{NA}.}
|
||||
|
||||
\item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
|
||||
|
||||
\item{x}{the coordinates of points in the plot. Alternatively, a
|
||||
single plotting structure, function or \emph{any \R object with a
|
||||
\code{plot} method} can be provided.}
|
||||
|
||||
\item{...}{parameters passed on to the \code{first_isolate} function}
|
||||
}
|
||||
\value{
|
||||
\code{data.frame} with columns:
|
||||
\code{data.frame} with extra class \code{"resistance_predict"} with columns:
|
||||
\itemize{
|
||||
\item{\code{year}}
|
||||
\item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
@ -59,39 +74,20 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
|
||||
}
|
||||
|
||||
\examples{
|
||||
\dontrun{
|
||||
# use it with base R:
|
||||
resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
col_ab = "amcl", col_date = "date")
|
||||
x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
|
||||
plot(x)
|
||||
ggplot_rsi_predict(x)
|
||||
|
||||
# or use dplyr so you can actually read it:
|
||||
# use dplyr so you can actually read it:
|
||||
library(dplyr)
|
||||
tbl \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
genus == "Haemophilus") \%>\%
|
||||
resistance_predict(amcl, date)
|
||||
}
|
||||
x <- septic_patients \%>\%
|
||||
filter_first_isolate() \%>\%
|
||||
filter(mo_genus(mo) == "Staphylococcus") \%>\%
|
||||
resistance_predict("peni")
|
||||
plot(x)
|
||||
|
||||
|
||||
# real live example:
|
||||
library(dplyr)
|
||||
septic_patients \%>\%
|
||||
# get bacteria properties like genus and species
|
||||
left_join_microorganisms("mo") \%>\%
|
||||
# calculate first isolates
|
||||
mutate(first_isolate = first_isolate(.)) \%>\%
|
||||
# filter on first E. coli isolates
|
||||
filter(genus == "Escherichia",
|
||||
species == "coli",
|
||||
first_isolate == TRUE) \%>\%
|
||||
# predict resistance of cefotaxime for next years
|
||||
resistance_predict(col_ab = "cfot",
|
||||
col_date = "date",
|
||||
year_max = 2025,
|
||||
preserve_measurements = TRUE,
|
||||
minimum = 0)
|
||||
|
||||
# create nice plots with ggplot
|
||||
# create nice plots with ggplot yourself
|
||||
if (!require(ggplot2)) {
|
||||
|
||||
data <- septic_patients \%>\%
|
||||
|
@ -32,6 +32,11 @@ test_that("prediction of rsi works", {
|
||||
# amox resistance will increase according to data set `septic_patients`
|
||||
expect_true(amox_R[3] < amox_R[20])
|
||||
|
||||
x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
|
||||
plot(x)
|
||||
ggplot_rsi_predict(x)
|
||||
expect_error(ggplot_rsi_predict(septic_patients))
|
||||
|
||||
library(dplyr)
|
||||
|
||||
expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),
|
||||
|
Loading…
Reference in New Issue
Block a user