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AMR/R/resistance_predict.R

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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# 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. #
# #
# This R package was created for academic research and 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.gitab.io/AMR. #
# ==================================================================== #
#' Predict antimicrobial resistance
#'
#' 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, 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. Defaults to a generalised linear regression model with binomial distribution, 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 valid options.
#' @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.
#' @param main title of the plot
#' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars
#' @details Valid options for the statistical model are:
#' \itemize{
#' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution}
#' \item{\code{"loglin"} or \code{"poisson"}: a generalised log-linear regression model with poisson distribution}
#' \item{\code{"lin"} or \code{"linear"}: a linear regression model}
#' }
#' @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}
#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)}
#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)}
#' \item{\code{observations}, the total number of available observations in that year, i.e. S + I + R}
#' \item{\code{observed}, the original observed resistant percentages}
#' \item{\code{estimated}, the estimated resistant percentages, calculated by the model}
#' }
#' Furthermore, the model itself is available as an attribute: \code{attributes(x)$model}, see Examples.
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
#' @rdname resistance_predict
#' @export
#' @importFrom stats predict glm lm
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when n_groups transmute
#' @inheritSection AMR Read more on our website!
#' @examples
#' x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
#' plot(x)
#' ggplot_rsi_predict(x)
#'
#' # use dplyr so you can actually read it:
#' library(dplyr)
#' x <- septic_patients %>%
#' filter_first_isolate() %>%
#' filter(mo_genus(mo) == "Staphylococcus") %>%
#' resistance_predict("peni")
#' plot(x)
#'
#'
#' # get the model from the object
#' mymodel <- attributes(x)$model
#' summary(mymodel)
#'
#'
#' # create nice plots with ggplot2 yourself
#' if (!require(ggplot2)) {
#'
#' data <- septic_patients %>%
#' filter(mo == as.mo("E. coli")) %>%
#' resistance_predict(col_ab = "amox",
#' col_date = "date",
#' info = FALSE,
#' minimum = 15)
#'
#' ggplot(data,
#' aes(x = year)) +
#' geom_col(aes(y = value),
#' fill = "grey75") +
#' geom_errorbar(aes(ymin = se_min,
#' ymax = se_max),
#' colour = "grey50") +
#' scale_y_continuous(limits = c(0, 1),
#' breaks = seq(0, 1, 0.1),
#' labels = paste0(seq(0, 100, 10), "%")) +
#' labs(title = expression(paste("Forecast of amoxicillin resistance in ",
#' italic("E. coli"))),
#' y = "%IR",
#' x = "Year") +
#' theme_minimal(base_size = 13)
#' }
resistance_predict <- function(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) {
if (nrow(tbl) == 0) {
stop('This table does not contain any observations.')
}
if (!col_ab %in% colnames(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 (n_groups(tbl) > 1) {
# no grouped tibbles please, mutate will throw errors
tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
}
year <- function(x) {
if (all(grepl('^[0-9]{4}$', x))) {
x
} else {
as.integer(format(as.Date(x), '%Y'))
}
}
df <- tbl %>%
mutate_at(col_ab, as.rsi) %>%
mutate_at(col_ab, droplevels) %>%
mutate_at(col_ab, funs(
if (I_as_R == TRUE) {
gsub("I", "R", .)
} else {
gsub("I", "S", .)
}
)) %>%
filter_at(col_ab, all_vars(!is.na(.))) %>%
mutate(year = pull(., col_date) %>% year()) %>%
group_by_at(c('year', col_ab)) %>%
summarise(n())
if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
stop("No variety in antimicrobial interpretations - all isolates are '",
df %>% pull(col_ab) %>% unique(), "'.",
call. = FALSE)
}
colnames(df) <- c('year', 'antibiotic', 'observations')
df <- df %>%
filter(!is.na(antibiotic)) %>%
tidyr::spread(antibiotic, observations, fill = 0) %>%
filter((R + S) >= minimum)
df_matrix <- df %>%
ungroup() %>%
select(R, S) %>%
as.matrix()
if (NROW(df) == 0) {
stop('There are no observations.')
}
year_lowest <- min(df$year)
if (is.null(year_min)) {
year_min <- year_lowest
} else {
year_min <- max(year_min, year_lowest, na.rm = TRUE)
}
if (is.null(year_max)) {
year_max <- year(Sys.Date()) + 10
}
years <- list(year = seq(from = year_min, to = year_max, by = year_every))
if (model %in% c('binomial', 'binom', 'logit')) {
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(model_lm))
}
predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} 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(model_lm))
}
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')) {
model <- "linear"
model_lm <- with(df, lm((R / (R + S)) ~ year))
if (info == TRUE) {
cat('\nLinear regression model')
cat('\n-----------------------\n')
print(summary(model_lm))
}
predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
prediction <- predictmodel$fit
se <- predictmodel$se.fit
} else {
stop('No valid model selected.')
}
# prepare the output dataframe
df_prediction <- data.frame(year = unlist(years),
value = prediction,
stringsAsFactors = FALSE) %>%
mutate(se_min = value - se,
se_max = value + se)
if (model == 'poisson') {
df_prediction <- df_prediction %>%
mutate(value = value %>%
format(scientific = FALSE) %>%
as.integer(),
se_min = as.integer(se_min),
se_max = as.integer(se_max))
} else {
df_prediction <- df_prediction %>%
# se_max not above 1
mutate(se_max = ifelse(se_max > 1, 1, se_max))
}
df_prediction <- df_prediction %>%
# se_min not below 0
mutate(se_min = ifelse(se_min < 0, 0, se_min))
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
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))
}
df_prediction <- df_prediction %>%
mutate(value = case_when(value > 1 ~ 1,
value < 0 ~ 0,
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 filter
#' @importFrom graphics plot axis arrows points
#' @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("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"),
cex.sub = 0.75)
axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
# hack for error bars: 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, lwd = 1.5)
# overlay grey points for prediction
points(x = filter(x, is.na(observations))$year,
y = filter(x, is.na(observations))$value,
pch = 19,
col = "grey40")
}
#' @rdname resistance_predict
#' @importFrom dplyr filter
#' @export
ggplot_rsi_predict <- function(x,
main = paste("Resistance prediction of", attributes(x)$ab),
ribbon = TRUE,
...) {
if (!"resistance_predict" %in% class(x)) {
stop("`x` must be a resistance prediction model created with resistance_predict().")
}
if (attributes(x)$I_as_R == TRUE) {
ylab <- "%IR"
} else {
ylab <- "%R"
}
p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
ggplot2::geom_point(data = filter(x, !is.na(observations)),
size = 2) +
scale_y_percent(limits = c(0, 1)) +
ggplot2::labs(title = main,
y = paste0("Percentage (", ylab, ")"),
x = "Year",
caption = paste0("(n = ", sum(x$observations, na.rm = TRUE),
", model: ", attributes(x)$model_title, ")"))
if (ribbon == TRUE) {
p <- p + ggplot2::geom_ribbon(ggplot2::aes(ymin = se_min, ymax = se_max), alpha = 0.25)
} else {
p <- p + ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max), na.rm = TRUE, width = 0.5)
}
p <- p +
# overlay grey points for prediction
ggplot2::geom_point(data = filter(x, is.na(observations)),
size = 2,
colour = "grey40")
p
}
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