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 se_min and se_max. See Examples for a real live example.

resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,
  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)

rsi_predict(x, col_ab, col_date = NULL, year_min = NULL,
  year_max = NULL, year_every = 1, minimum = 30, model = NULL,
  I_as_S = TRUE, preserve_measurements = TRUE, info = TRUE, ...)

# S3 method for resistance_predict
plot(x,
  main = paste("Resistance Prediction of", x_name), ...)

ggplot_rsi_predict(x, main = paste("Resistance Prediction of", x_name),
  ribbon = TRUE, ...)

Arguments

x	a `data.frame` containing isolates.
col_ab	column name of `x` with antimicrobial interpretations (`R`, `I` and `S`)
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
year_min	lowest year to use in the prediction model, dafaults to the lowest year in `col_date`
year_max	highest year to use in the prediction model, defaults to 10 years after today
year_every	unit of sequence between lowest year found in the data and `year_max`
minimum	minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
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.
I_as_S	a logical to indicate whether values `I` should be treated as `S` (will otherwise be treated as `R`)
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`.
info	a logical to indicate whether textual analysis should be printed with the name and `summary` of the statistical model.
...	parameters passed on to functions
main	title of the plot
ribbon	a logical to indicate whether a ribbon should be shown (default) or error bars

Value

data.frame with extra class "resistance_predict" with columns:

year
value, the same as estimated when preserve_measurements = FALSE, and a combination of observed and estimated otherwise
se_min, the lower bound of the standard error with a minimum of 0 (so the standard error will never go below 0%)
se_max the upper bound of the standard error with a maximum of 1 (so the standard error will never go above 100%)
observations, the total number of available observations in that year, i.e. S + I + R
observed, the original observed resistant percentages
estimated, the estimated resistant percentages, calculated by the model

Furthermore, the model itself is available as an attribute: attributes(x)$model, see Examples.

Details

Valid options for the statistical model are:

"binomial" or "binom" or "logit": a generalised linear regression model with binomial distribution
"loglin" or "poisson": a generalised log-linear regression model with poisson distribution
"lin" or "linear": a linear regression model

Examples

x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial")
plot(x)
ggplot_rsi_predict(x)

# use dplyr so you can actually read it:
library(dplyr)
x <- example_isolates %>%
  filter_first_isolate() %>%
  filter(mo_genus(mo) == "Staphylococcus") %>%
  resistance_predict("PEN", model = "binomial")
plot(x)


# get the model from the object
mymodel <- attributes(x)$model
summary(mymodel)


# create nice plots with ggplot2 yourself
if (!require(ggplot2)) {

  data <- example_isolates %>%
    filter(mo == as.mo("E. coli")) %>%
    resistance_predict(col_ab = "AMX",
                       col_date = "date",
                       model = "binomial",
                       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)
}

Arguments

Value

Details

Read more on our website!

See also

Examples

Contents