Title:	Evaluate Trending Models
Version:	0.1.1
Description:	Provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON (https://www.repidemicsconsortium.org/) toolkit for outbreak analysis.
URL:	https://github.com/reconverse/trendeval
BugReports:	https://github.com/reconverse/trendeval/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	trending, yardstick, rsample, stats, tibble, utils
Suggests:	testthat (≥ 3.0.0), dplyr, outbreaks
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-04-25 09:15:45 UTC; thiba
Author:	Dirk Schumacher [aut], Thibaut Jombart [aut, cre], Tim Taylor [ctb]
Maintainer:	Thibaut Jombart <thibautjombart@gmail.com>
Repository:	CRAN
Date/Publication:	2025-04-25 15:20:02 UTC

Generic for calculating the AIC

Description

Generic calculate_aic() returns the Akaike's 'An Information Criterion' for the given input.

Usage

calculate_aic(x, ...)

## Default S3 method:
calculate_aic(x, ...)

## S3 method for class 'trending_model'
calculate_aic(x, data, as_tibble = FALSE, ...)

## S3 method for class 'list'
calculate_aic(x, data, ...)

## S3 method for class 'trending_fit'
calculate_aic(x, as_tibble = FALSE, ...)

## S3 method for class 'trending_fit_tbl'
calculate_aic(x, ...)

Arguments

Details

Specific methods are given for trending_fit and trending_fit_tbl objects. The default method applies stats::AIC() directly.

Value

For a single trending_fit input, if as_tibble = FALSE the object returned will be a list with entries:

• metric: "AIC"

• result: the resulting AIC value fit (NULL if the calculation failed)

• warnings: any warnings generated during calculation

• errors: any errors generated during calculation

If as_tibble = TRUE, or the input is a trending_fit_tbl, then the output will be a tibble with one row for each fitted model columns corresponding to output generated with single model input.

Author(s)

Tim Taylor

#' @examples x = rnorm(100, mean = 0) y = rpois(n = 100, lambda = exp(1.5 + 0.5*x)) dat <- data.frame(x = x, y = y) poisson_model <- glm_model(y ~ x , family = "poisson") negbin_model <- glm_nb_model(y ~ x) fitted_model <- fit(poisson_model, dat) fitted_models <- fit(list(poisson_model, negbin_model), data = dat)

calculate_aic(poisson_model, dat) calculate_aic(fitted_model) calculate_aic(fitted_model, as_tibble = TRUE) calculate_aic(fitted_models)

Generic for calculating the root mean squared error

Description

Generic calculate_mae() returns the root mean square error for the given input.

Usage

calculate_mae(x, ...)

## Default S3 method:
calculate_mae(x, ...)

## S3 method for class 'trending_model'
calculate_mae(x, data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'list'
calculate_mae(x, data, na.rm = TRUE, ...)

## S3 method for class 'trending_fit'
calculate_mae(x, new_data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_fit_tbl'
calculate_mae(x, new_data, na.rm = TRUE, ...)

## S3 method for class 'trending_predict'
calculate_mae(x, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_predict_tbl'
calculate_mae(x, na.rm = TRUE, ...)

## S3 method for class 'trending_prediction'
calculate_mae(x, na.rm = TRUE, as_tibble = TRUE, ...)

Arguments

Details

Specific methods are given for trending_model (and lists of these), trending_fit, trending_fit_tbl, trending_predict_tbl, trending_predict_tbl and trending_prediction objects. Each of these are simply wrappers around the yardstick::mae_vec with the addition of explicit error handling.

Value

For a single trending_fit input, if as_tibble = FALSE the object returned will be a list with entries:

• metric: "mae"

• result: the resulting mae value (NULL if the calculation failed)

• warnings: any warnings generated during calculation

• errors: any errors generated during calculation

If as_tibble = TRUE, or for the other trending classes, then the output will be a tibble with one row for each fitted model columns corresponding to output generated with single model input.

Author(s)

Tim Taylor

#' @examples x = rnorm(100, mean = 0) y = rpois(n = 100, lambda = exp(1.5 + 0.5*x)) dat <- data.frame(x = x, y = y) poisson_model <- glm_model(y ~ x , family = "poisson") negbin_model <- glm_nb_model(y ~ x) fitted_model <- fit(poisson_model, dat) fitted_models <- fit(list(poisson_model, negbin_model), data = dat)

calculate_mae(poisson_model, dat) calculate_mae(fitted_model) calculate_mae(fitted_model, as_tibble = TRUE) calculate_mae(fitted_models)

Generic for calculating the root mean squared error

Description

Generic calculate_rmse() returns the root mean square error for the given input.

Usage

calculate_rmse(x, ...)

## Default S3 method:
calculate_rmse(x, ...)

## S3 method for class 'trending_model'
calculate_rmse(x, data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'list'
calculate_rmse(x, data, na.rm = TRUE, ...)

## S3 method for class 'trending_fit'
calculate_rmse(x, new_data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_fit_tbl'
calculate_rmse(x, new_data, na.rm = TRUE, ...)

## S3 method for class 'trending_predict'
calculate_rmse(x, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_predict_tbl'
calculate_rmse(x, na.rm = TRUE, ...)

## S3 method for class 'trending_prediction'
calculate_rmse(x, na.rm = TRUE, as_tibble = TRUE, ...)

Arguments

Details

Specific methods are given for trending_model (and lists of these), trending_fit, trending_fit_tbl, trending_predict_tbl, trending_predict_tbl and trending_prediction objects. Each of these are simply wrappers around the yardstick::rmse_vec with the addition of explicit error handling.

Value

For a single trending_fit input, if as_tibble = FALSE the object returned will be a list with entries:

• metric: "rmse"

• result: the resulting rmse value (NULL if the calculation failed)

• warnings: any warnings generated during calculation

• errors: any errors generated during calculation

If as_tibble = TRUE, or for the other trending classes, then the output will be a tibble with one row for each fitted model columns corresponding to output generated with single model input.

Author(s)

Tim Taylor

#' @examples x = rnorm(100, mean = 0) y = rpois(n = 100, lambda = exp(1.5 + 0.5*x)) dat <- data.frame(x = x, y = y) poisson_model <- glm_model(y ~ x , family = "poisson") negbin_model <- glm_nb_model(y ~ x) fitted_model <- fit(poisson_model, dat) fitted_models <- fit(list(poisson_model, negbin_model), data = dat)

calculate_rmse(poisson_model, dat) calculate_rmse(fitted_model) calculate_rmse(fitted_model, as_tibble = TRUE) calculate_rmse(fitted_models)

Generic for calculating the root mean squared error

Description

Generic calculate_rsq() returns the root mean square error for the given input.

Usage

calculate_rsq(x, ...)

## Default S3 method:
calculate_rsq(x, ...)

## S3 method for class 'trending_model'
calculate_rsq(x, data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'list'
calculate_rsq(x, data, na.rm = TRUE, ...)

## S3 method for class 'trending_fit'
calculate_rsq(x, new_data, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_fit_tbl'
calculate_rsq(x, new_data, na.rm = TRUE, ...)

## S3 method for class 'trending_predict'
calculate_rsq(x, na.rm = TRUE, as_tibble = TRUE, ...)

## S3 method for class 'trending_predict_tbl'
calculate_rsq(x, na.rm = TRUE, ...)

## S3 method for class 'trending_prediction'
calculate_rsq(x, na.rm = TRUE, as_tibble = TRUE, ...)

Arguments

Details

Specific methods are given for trending_model (and lists of these), trending_fit, trending_fit_tbl, trending_predict_tbl, trending_predict_tbl and trending_prediction objects. Each of these are simply wrappers around the yardstick::rsq_vec with the addition of explicit error handling.

Value

For a single trending_fit input, if as_tibble = FALSE the object returned will be a list with entries:

• metric: "rsq"

• result: the resulting rsq value (NULL if the calculation failed)

• warnings: any warnings generated during calculation

• errors: any errors generated during calculation

If as_tibble = TRUE, or for the other trending classes, then the output will be a tibble with one row for each fitted model columns corresponding to output generated with single model input.

Author(s)

Tim Taylor

#' @examples x = rnorm(100, mean = 0) y = rpois(n = 100, lambda = exp(1.5 + 0.5*x)) dat <- data.frame(x = x, y = y) poisson_model <- glm_model(y ~ x , family = "poisson") negbin_model <- glm_nb_model(y ~ x) fitted_model <- fit(poisson_model, dat) fitted_models <- fit(list(poisson_model, negbin_model), data = dat)

calculate_rsq(poisson_model, dat) calculate_rsq(fitted_model) calculate_rsq(fitted_model, as_tibble = TRUE) calculate_rsq(fitted_models)

Generic for calculating the AIC

Description

evaluate_aic() is a a generic for evaluating the Akaike's 'An Information Criterion' for a given input

Usage

evaluate_aic(x, ...)

## Default S3 method:
evaluate_aic(x, ...)

## S3 method for class 'trending_model'
evaluate_aic(x, data, as_tibble = FALSE, ...)

## S3 method for class 'list'
evaluate_aic(x, data, ...)

Arguments

Details

Specific methods are given for trending_fit and lists of these models.

Value

If as_tibble = TRUE, or the input is a list of models then the output will be a tibble with one row for each fitted model columns corresponding to output generated with single model input.

Author(s)

Tim Taylor

#' @examples x = rnorm(100, mean = 0) y = rpois(n = 100, lambda = exp(1.5 + 0.5*x)) dat <- data.frame(x = x, y = y) poisson_model <- glm_model(y ~ x , family = "poisson") negbin_model <- glm_nb_model(y ~ x)

evaluate_aic(poisson_model, dat) evaluate_aic(list(poisson_model, negbin_model), data = dat)

Resampling approach for model evaluation

Description

evaluate_resampling() uses repeated K-fold cross-validation and the Root Mean Square Error (RMSE) of testing sets to measure the predictive power of a single model. Methods are provided for trending::trending_model (and lists of these) objects.

Usage

evaluate_resampling(x, ...)

## Default S3 method:
evaluate_resampling(x, ...)

## S3 method for class 'trending_model'
evaluate_resampling(
  x,
  data,
  metric = c("rmse", "rsq", "mae"),
  metric_arguments = list(na.rm = TRUE),
  v = 5,
  repeats = 1,
  ...
)

## S3 method for class 'list'
evaluate_resampling(
  x,
  data,
  metric = c("rmse", "rsq", "mae"),
  metric_arguments = list(na.rm = TRUE),
  v = 5,
  repeats = 1,
  ...
)

Arguments

Details

These functions wrap around existing functions from several packages. evaluate_resampling.trending_model() and evaluate_resampling.list() both use rsample::vfold_cv() for sampling and, for the calculating the different metrics, the yardstick package.

See Also

calculate_aic(), calculate_rmse(), calculate_mae() and calculate_rsq().

Examples

x <- rnorm(100, mean = 0)
y <- rpois(n = 100, lambda = exp(x + 1))
dat <- data.frame(x = x, y = y)
model <- trending::glm_model(y ~ x, poisson)
models <- list(
  poisson_model = trending::glm_model(y ~ x, poisson),
  linear_model = trending::lm_model(y ~ x)
)

evaluate_resampling(model, dat)
evaluate_resampling(models, dat)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

trending

brm_model, glm_model, glm_nb_model, lm_model