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Akaike Information Criterion

Source: R/lifelihood.R
AIC.lifelihoodResults.Rd

S3 method to compute AIC (Akaike Information Criterion).

Usage

# S3 method for class 'lifelihoodResults'
AIC(object, ..., k = length(coef(object)))

Arguments

object

output of lifelihood()

...

Ignored

k

Number of estimated parameter of the modèle. Default to length(coef(object)).

Value

The AIC

See also

AICc(), BIC()

Examples

library(lifelihood)
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
#> ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
#> ✔ purrr     1.0.4     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

df <- lifelihood::fakesample |>
  mutate(
    type = as.factor(type),
    geno = as.factor(geno)
  )

clutchs <- c(
  "clutch_start1", "clutch_end1", "clutch_size1",
  "clutch_start2", "clutch_end2", "clutch_size2"
)

dataLFH <- lifelihoodData(
  df = df,
  sex = "sex",
  sex_start = "sex_start",
  sex_end = "sex_end",
  maturity_start = "mat_start",
  maturity_end = "mat_end",
  clutchs = clutchs,
  death_start = "death_start",
  death_end = "death_end",
  covariates = c("geno", "type"),
  model_specs = c("gam", "lgn", "wei")
)

results <- lifelihood(
  lifelihoodData = dataLFH,
  path_config = get_config_path("config"),
  seeds = c(1, 2, 3, 4),
  raise_estimation_warning = FALSE
)
AIC(results)
#> [1] 1760.116