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Run missingness benchmark (target-masking with LAG features)

Source: R/run_bench.R
run_missingness_benchmark.Rd

[Deprecated]

This function is deprecated. Use run_missing_glucose_imputation() for real missing glucose values.

This function implements missingness benchmarking by masking the target column at various rates and evaluating imputation and predictive performance of MICE, Random Forest, and KNN methods. Additionally, it includes LAG features of the target variable to assess their impact on imputation and prediction. The function returns a data.frame summarizing the Mask Rate, Method, MRD (Mean Relative Difference), and Masked Count for each method and mask rate.

Usage

run_missingness_benchmark(
  data,
  target_col,
  feature_cols = NULL,
  id_col = "USUBJID",
  time_col = "TimeSeries",
  mask_rates = c(0.05, 0.1, 0.2, 0.3, 0.4),
  mask_type = c("random", "block"),
  rf_n_estimators = 400,
  knn_k = 7,
  seed = 42,
  lag_k = c(1, 2, 3),
  add_rollmean = TRUE,
  roll_window = 3
)

Arguments

data

A data.frame (or object coercible to data.frame), OR a path to a CSV file.

target_col

Single character string: name of the outcome column to mask/impute (e.g., "LBORRES", "Glucose").

feature_cols

Character vector of base feature columns (excluding the target). If NULL, uses all columns except target_col.

id_col

Character string: subject identifier column used for LAG features (default "USUBJID").

time_col

Character string: time-ordering column used for LAG features (default "TimeSeries").

mask_rates

Numeric vector in (0, 1): fraction of rows to mask (default 0.05, 0.10, 0.20, 0.30, 0.40).

mask_type

One of "random" or "block".

rf_n_estimators

Integer: number of trees for random forest (default 400).

knn_k

Integer: number of neighbors for kNN (default 7).

seed

Integer: random seed used for MICE and models (default 42).

lag_k

Integer vector of lags to compute on the target (default c(1,2,3)).

add_rollmean

Logical: add rolling mean feature of prior target values (default TRUE).

roll_window

Integer: rolling window length for rollmean (default 3).

Value

A data.frame with columns: MaskRate, Method, MRD, MaskedCount.

Details

LAG features are computed using data.table::shift() (fast lag/lead). The rolling mean is computed with data.table::frollmean() using align="right" and fill=NA.