Mean squared error (mse) — measure

Computes MSE between observed outcomes and posterior predictive point predictions. Point predictions are obtained by averaging mupred draws, or by PSIS-weighted averaging when log_weights is provided.

Usage

measure_mse(y, mupred, log_weights = NULL, pointwise = NULL)

Arguments

y: A numeric vector of observed outcomes.
mupred: A numeric matrix of posterior expected predictions with dimensions draws x observations. A length-n vector is also accepted.
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 squared errors. If provided, y, mupred, 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

y <- c(1, 2, 3)
mupred <- matrix(c(0.9, 2.1, 2.8, 1.2, 1.9, 3.1), nrow = 2)
measure_mse(y, mupred)
#> 
#> Computed from 2 draws by 3 observations.
#> 
#>     Estimate    SE
#> mse    0.167 0.083