AMR/reference/resistance_predict.md

# Predict Antimicrobial Resistance

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](https://amr-for-r.org/reference/AMR-deprecated.md) 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](https://amr-for-r.org/articles/AMR_with_tidymodels.html).

## Usage

``` r
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](https://rdrr.io/r/base/data.frame.html) 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](https://rdrr.io/r/base/logical.html) 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](https://rdrr.io/r/base/logical.html) 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](https://rdrr.io/r/base/logical.html) to indicate whether
  textual analysis should be printed with the name and
  [`summary()`](https://rdrr.io/r/base/summary.html) of the statistical
  model.

- ...:

  Arguments passed on to functions.

- main:

  Title of the plot.

- ribbon:

  A [logical](https://rdrr.io/r/base/logical.html) to indicate whether a
  ribbon should be shown (default) or error bars.

- object:

  Model data to be plotted.

## Value

A [data.frame](https://rdrr.io/r/base/data.frame.html) 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 (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

## 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()`](https://amr-for-r.org/reference/proportion.md)
(equal to
[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md)) to
determine antimicrobial susceptibility and
[`count_susceptible()`](https://amr-for-r.org/reference/count.md) (equal
to [`count_SI()`](https://amr-for-r.org/reference/count.md)) to count
susceptible isolates.

## See also

The [`proportion()`](https://amr-for-r.org/reference/proportion.md)
functions to calculate resistance

Models: [`lm()`](https://rdrr.io/r/stats/lm.html)
[`glm()`](https://rdrr.io/r/stats/glm.html)

## Examples

``` r
x <- resistance_predict(example_isolates,
  col_ab = "AMX",
  year_min = 2010,
  model = "binomial"
)
#> Warning: The `resistance_predict()` function is deprecated and will be removed in a
#> future version, see `?AMR-deprecated`. Use the tidymodels framework
#> instead, for which we have written a basic and short introduction on our
#> website: https://amr-for-r.org/articles/AMR_with_tidymodels.html
#> This warning will be shown once per session.
plot(x)

# \donttest{
if (require("ggplot2")) {
  ggplot_sir_predict(x)
}
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).


# 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)
}

#> NULL
#> 
#> Call:
#> glm(formula = df_matrix ~ year, family = binomial)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 35.76101   72.29172   0.495    0.621
#> year        -0.01720    0.03603  -0.477    0.633
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 5.3681  on 11  degrees of freedom
#> Residual deviance: 5.1408  on 10  degrees of freedom
#> AIC: 50.271
#> 
#> Number of Fisher Scoring iterations: 4
#> 

# 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)
}
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).

# }
```