Parameter method
(default: “ranger” for all variables)
Specifies the machine learning method used for imputation of each
variable. In this example, different imputation methods are specified
for each variable. The NonD variable uses a robust method,
Dream and Span are using ranger,
Sleep uses xgboost, Gest uses a regularized
method and class uses a robust method.
"robust": Robust regression models
lmrob for numeric variables: Implements MM-estimation
for resistance to outliersglmrob for binary factors: Uses robust estimators to
reduce outlier influence
"regularized": Regularized regression
(glmnet)
- Uses elastic net regularization
- Automatically handles multicollinearity
"ranger": Random Forest
- Fast implementation of random forests
- Handles non-linear relationships well
"xgboost": Gradient Boosted Trees
- State-of-the-art tree boosting
- Handles mixed data types well
result_mixed <- vimpute(
data = data,
method = list(NonD = "robust", Dream = "ranger", Sleep = "xgboost", Span = "ranger" , Gest = "regularized"),
pred_history = TRUE
)
dataDS <- as.data.frame(result_mixed$data[, c("Dream", "Sleep", "Dream_imp", "Sleep_imp")])
marginplot(dataDS, delimiter = "_imp", main = "Imputation with different Models for each Variable")
The side-by-side margin plots compare the performance of two
imputation methods: xgboost (left) and regularized (right):
xgboost handles missing values with data-driven, uneven imputations
that capture complex patterns but may be less stable, while regularized
methods produce smoother, more conservative estimates that are less
prone to overfitting. The key difference lies in flexibility (xgboost)
versus robustness (regularization).
Parameter pmm
(default: TRUE for all numeric variables)
result <- vimpute(
data = data,
method = list(NonD = "robust",
Dream = "ranger",
Sleep = "xgboost",
Span = "ranger" ,
Gest = "regularized"),
pmm = list(NonD = FALSE, Dream = TRUE, Sleep = FALSE, Span = FALSE , Gest = TRUE)
)
If TRUE, imputed values are restricted to actual observed values in
the dataset, ensuring realism but potentially limiting variability. If
FALSE, raw model predictions are used, allowing greater flexibility but
risking implausible or extreme imputations.
Parameters nseq and eps
(default: 10 and default: 0.005)
result <- vimpute(
data = data,
nseq = 20,
eps = 0.01
)
nseq describes the number of sequential imputation
iterations. Higher values:
- Allow more refinement
- Increase computation time
eps describes the convergence threshold for sequential
imputation:
- Stops early if changes between iterations < eps
- Smaller values: Require more precise convergence but may need more
iterations
Parameter imp_var
(default: TRUE)
result <- vimpute(
data = data,
imp_var = TRUE
)
Creating indicator variables for imputed values adds “_imp” columns
(TRUE/FALSE) to mark which data points were imputed. This is
particularly useful for tracking imputation effects and conducting
diagnostic analyses.
Parameter pred_history
(default: TRUE)
print(tail(result$pred_history, 9))
#> iteration variable index predicted_values
#> <int> <char> <int> <num>
#> 1: 10 Sleep 62 10.7
#> 2: 10 Span 4 7.0
#> 3: 10 Span 13 6.0
#> 4: 10 Span 35 6.0
#> 5: 10 Span 36 7.0
#> 6: 10 Gest 13 42.0
#> 7: 10 Gest 19 225.0
#> 8: 10 Gest 20 90.0
#> 9: 10 Gest 56 33.0
When enabled (TRUE), this option saves prediction trajectories in
$pred_history, allowing users to track how imputed values
evolve across iterations. This feature is particularly useful for
diagnosing convergence issues.
