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Information Criteria (ic)

Source: R/pred_measure-builtin.R
measure_ic.Rd

Computes the information criteria as -2 x log predictive density (lppd_i) values. Inputs follow the same conventions as measure_elpd() and measure_mlpd().

Usage

measure_ic(ylp, log_weights = NULL, pointwise = NULL)

Arguments

ylp

A numeric matrix or three-dimensional array of log predictive densities or probabilities:

  • Matrix (\(S \times n\)): \(S\) posterior draws (chains merged) by \(n\) observations.

  • Array (\(I \times C \times n\)): \(I\) MCMC iterations per chain, \(C\) chains, and \(n\) observations. Converted to an \(S \times n\) matrix internally.

log_weights

Optional numeric matrix of unnormalized log-importance weights with dimensions \(S \times n\). Weights are column-normalized before computing each per-observation contribution.

pointwise

Optional numeric vector of precomputed pointwise contributions \(\mathrm{ic}_i = -2 \cdot \mathrm{lppd}_i\). If provided, ylp and log_weights are ignored.

Value

An object of class "measure": a list with:

estimates

A 1 \(\times\) 2 matrix. The row is named after the measure, the columns are Estimate and SE (standard error).

pointwise

An \(n \times 1\) matrix of per-observation values. The column is named after the measure.

extra

Optional. Auxiliary data for the standard error of a difference. Most measures do not set it.

Attributes measure (i.e., measure name) and dims (draws \(\times\) observations) are also set. Use print() for a readable summary.

Examples

ylp <- matrix(log(c(0.2, 0.4, 0.3, 0.8)), nrow = 2)
measure_ic(ylp)
#> 
#> Computed from 2 draws by 2 observations.
#> 
#>    Estimate  SE
#> ic      3.6 1.2