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Probability integral transform from a predictive CDF

Source: R/criteria.R
tulpa_pit.Rd

The generic, family-agnostic half of a PIT residual check: the model package supplies the posterior-predictive CDF evaluated at each observation (a [n_draws x n_obs] matrix, or a draw-averaged [n_obs] vector), and this returns the PIT value per observation. For a discrete or mixed response (a hurdle has a point mass at zero) supply the left limit cdf_lower (P(Y < y)); the randomized PIT then draws one uniform per observation and interpolates F(y^-) + U (F(y) - F(y^-)), which is uniform under a correct model. With cdf_lower = NULL the response is treated as continuous and the PIT is the draw-averaged CDF.

Usage

tulpa_pit(
  cdf,
  cdf_lower = NULL,
  jitter = TRUE,
  log_lik = NULL,
  tail_points = NULL,
  n_threads = 1L
)

Arguments

cdf

Posterior-predictive CDF at the observed value, P(Y <= y). A [n_draws x n_obs] matrix (averaged over draws here) or an [n_obs] vector.

cdf_lower

Optional left-limit CDF P(Y < y), same shape as cdf, for the randomized PIT of a discrete / mixed response.

jitter

If TRUE (default) and cdf_lower is NULL, add a tiny uniform jitter to break ties from a discretized CDF; ignored when cdf_lower is supplied (the interpolation already randomizes).

log_lik

Optional [n_draws x n_obs] matrix of the pointwise log-likelihood at each draw. When supplied, the PIT is the leave-one-out PIT: cdf / cdf_lower are reweighted by that observation's PSIS leave-one-out weights instead of being column-averaged.

tail_points

Optional override for the PSIS tail size used by the leave-one-out weighting (see tulpa_psis()); NULL uses the automatic rule. Ignored unless log_lik is supplied.

n_threads

Number of threads for the leave-one-out weighting (one observation per thread). Ignored unless log_lik is supplied.

Value

Numeric vector of length n_obs of PIT values in [0, 1].

Details

Supplying log_lik switches to the leave-one-out PIT (as in INLA's cpo$pit or loo::psis_loo()'s LOO-PIT): each observation's CDF limits are averaged over draws with PSIS leave-one-out weights (from that observation's own pointwise log-likelihood) instead of equal weights, so the PIT does not use the observation to predict itself. A column whose importance ratio is not all finite falls back to the equal-weight average for that observation.

See also

tulpa_criteria(), tulpa_psis()

Examples

set.seed(1)
Fl <- matrix(runif(200 * 10, 0, 0.4), 200, 10)   # per-draw CDF limits
Fu <- Fl + 0.3
tulpa_pit(Fu, cdf_lower = Fl)