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
- 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
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.
Details
Specific methods are given for
trending_fit and lists of these
models.
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)
evaluate_aic(poisson_model, dat) evaluate_aic(list(poisson_model, negbin_model), data = dat)