resistance_predict {AMR}R Documentation

Predict Antimicrobial Resistance

Description

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.

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:

Value

A data.frame with extra class resistance_predict with columns:

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 as shown below (https://www.eucast.org/newsiandr):

This AMR package honours this 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)
}


[Package AMR version 2.1.1 Index]