Root mean squared error (rmse)

Computes RMSE as the square root of MSE and propagates uncertainty via a first-order delta-method approximation.

Usage

measure_rmse(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_rmse(y, mupred)
#> 
#> Computed from 2 draws by 3 observations.
#> 
#>      Estimate   SE
#> rmse     0.41 0.10