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Out-of-sample Log-Likelihood (Proper Scoring Rule)

Source: R/gof.R
get_oos_likelihood.Rd

This function computes the out-of-sample log-likelihood (a strictly proper scoring rule) for each test event under a fitted REM or DEM.

Usage

get_oos_likelihood(
  object,
  verbose = FALSE,
  edgelist_test,
  edgelist_train = NULL,
  baseline_method = c("last", "trend", "mean", "beginning"),
  loess_span = 0.75
)

Arguments

object

A redeem object (either rem or dem).

verbose

Logical; if `TRUE`, prints verbose output. Defaults to FALSE.

edgelist_test

A matrix or data frame of test events (timing, from, to, type).

edgelist_train

A matrix or data frame of train events (timing, from, to, type). Defaults to `NULL`, in which case it retrieves the training events from the `object` or the preprocessed data.

baseline_method

Character; how to compute the fixed log-baseline intensity used for out-of-sample scoring. One of: `"last"` (uses the last estimated baseline value), `"trend"` (extrapolates a LOESS trend), `"mean"`, or `"beginning"`. Defaults to `"last"`.

loess_span

Numeric; LOESS span (0, 1] passed to predict_baseline_trend when baseline_method = "trend". Defaults to 0.75.

Value

A numeric vector of log-likelihoods for each test event.

See also

rem_object and dem_object for details on prediction methods.