resistance_predict: Predict Antimicrobial Resistance

In AMR: Antimicrobial Resistance Data Analysis

Predict Antimicrobial Resistance

Description

Create a prediction model to predict antimicrobial resistance for the next years. Standard errors (SE) will be returned as columns se_min and se_max. See Examples for a real live example.

NOTE: These functions are deprecated and will be removed in a future version. Use the AMR package combined with the tidymodels framework instead, for which we have written a basic and short introduction on our website.

Usage

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 = interactive(), ...)

sir_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 = interactive(), ...)

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

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

## S3 method for class 'resistance_predict'
autoplot(object,
  main = paste("Resistance Prediction of", x_name), ribbon = TRUE, ...)

Arguments

x	A data.frame containing isolates. Can be left blank for automatic determination, see Examples.
col_ab	Column name of x containing 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 - the default is 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 - the default is 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"). 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.
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.
...	Arguments passed on to functions.
main	Title of the plot.
ribbon	A logical to indicate whether a ribbon should be shown (default) or error bars.
object	Model data to be plotted.

Details

Valid options for the statistical model (argument 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

Value

A 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.

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr).

This AMR package follows insight; use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.

See Also

The proportion() functions to calculate resistance

Models: lm() glm()

Examples

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

if (require("ggplot2")) {
  ggplot_sir_predict(x)
}

# using dplyr:
if (require("dplyr")) {
  x <- example_isolates %>%
    filter_first_isolate() %>%
    filter(mo_genus(mo) == "Staphylococcus") %>%
    resistance_predict("PEN", model = "binomial")
  print(plot(x))

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

# create nice plots with ggplot2 yourself
if (require("dplyr") && 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
    )
  head(data)
  autoplot(data)
}

AMR documentation built on June 8, 2025, 10:17 a.m.