overimpute: Overimputation diagnostic

In rMIDAS2: Multiple Imputation with 'MIDAS2' Denoising Autoencoders

Overimputation diagnostic

Description

Masks a fraction of observed values, re-imputes them, and computes RMSE to assess imputation quality.

Usage

overimpute(model_id, mask_frac = 0.1, m = 5L, seed = NULL, ...)

Arguments

model_id	A character model ID, or a fitted model object (list with a ⁠$model_id⁠ element) as returned by midas_fit() or midas().
mask_frac	Numeric. Fraction of observed values to mask (default 0.1).
m	Integer. Number of imputations for the diagnostic (default 5).
seed	Integer or NULL. Random seed.
...	Arguments forwarded to ensure_server().

Value

A list with rmse (named numeric vector) and mean_rmse.

Examples

## Not run: 
df <- data.frame(X1 = rnorm(200), X2 = rnorm(200))
df$X1[sample(200, 40)] <- NA
fit <- midas_fit(df, epochs = 10L)
diag <- overimpute(fit, mask_frac = 0.1)
diag$mean_rmse

## End(Not run)

rMIDAS2 documentation built on March 12, 2026, 9:07 a.m.