Surprisals computed from a model

Usage

# S3 method for class 'lm'
surprisals(
  object,
  probability = TRUE,
  approximation = c("none", "gpd", "empirical"),
  threshold_probability = 0.1,
  loo = FALSE,
  ...
)

# S3 method for class 'gam'
surprisals(
  object,
  probability = TRUE,
  approximation = c("none", "gpd", "empirical"),
  threshold_probability = 0.1,
  loo = FALSE,
  ...
)

Arguments

object: A model object such as returned by lm, or gam.
probability: Should surprisal probabilities be computed, or the surprisal values?
approximation: Character string specifying the approximation to use in computing the surprisal probabilities. Ignored if probability = FALSE. : none specifies that no approximation is to be used; gpd specifies that the Generalized Pareto distribution should be used; while empirical specifies that the probabilities should be estimated empirically.
threshold_probability: Probability threshold when computing the GPD approximation. This is the probability below which the GPD is fitted. Only used if approximation = "gpd").
loo: Should leave-one-out surprisals be computed?
...: Other arguments are ignored.

Examples

# surprisals computed from linear model
of <- oldfaithful |>
  filter(duration < 7200, waiting < 7200)
lm_of <- lm(waiting ~ duration, data = of)
of |>
  mutate(
    fscore = surprisals(lm_of),
    loo_fscore = surprisals(lm_of, loo = TRUE),
    # lookout_prob = lookout(surprisals = fscore, loo_scores = loo_fscore)
  ) |>
  ggplot(aes(
    x = duration, y = waiting,
    color = loo_fscore > quantile(loo_fscore, 0.99)
  )) +
  geom_point()

# surprisals computed from GAM
of <- oldfaithful |>
  filter(duration > 1, duration < 7200, waiting < 7200)
gam_of <- mgcv::gam(waiting ~ s(duration), data = of)
of |>
  mutate(fscore = surprisals(gam_of))
#> # A tibble: 2,196 × 4
#>    time                duration waiting fscore
#>    <dttm>                 <dbl>   <dbl>  <dbl>
#>  1 2015-01-02 14:53:00      271    5040  0.119
#>  2 2015-01-09 23:55:00      247    6060  0.402
#>  3 2015-02-07 00:49:00      203    5460  0.890
#>  4 2015-02-14 01:09:00      195    5221  0.717
#>  5 2015-02-21 01:12:00      210    5401  0.787
#>  6 2015-02-28 01:11:00      185    5520  0.948
#>  7 2015-03-07 00:50:00      160    5281  0.817
#>  8 2015-03-13 21:57:00      226    6000  0.361
#>  9 2015-03-13 23:37:00      190    5341  0.862
#> 10 2015-03-20 22:26:00      102    3961  0.960
#> # ℹ 2,186 more rows