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
- x
An R object.
- ...
Not currently used.
- data
a
data.framecontaining data (including the response variable and all predictors) used in the specified model.- as_tibble
Should the result be returned as tibble (
as_tibble = TRUE) or a list (as_tibble = FALSE).
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.
Details
Specific methods are given for
trending_fit and
trending_fit_tbl objects. The default
method applies stats::AIC() directly.
Author
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)