evaluate_aic() is a a generic for evaluating the Akaike's
'An Information Criterion' for a given input
evaluate_aic(x, ...)# S3 method for default
evaluate_aic(x, ...)
# S3 method for trending_model
evaluate_aic(x, data, as_tibble = FALSE, ...)
# S3 method for list
evaluate_aic(x, data, ...)
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.
An R object.
Not currently used.
a data.frame containing data (including the response variable
and all predictors) used in the specified model.
Should the result be returned as tibble
(as_tibble = TRUE) or a list (as_tibble = FALSE).
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)
Specific methods are given for
trending_fit and lists of these
models.