| smdi_rf {smdi} | R Documentation |
Computes random forest-based AUC
Description
The function trains and fits a random forest model to assess the ability to predict missingness for the specified covariate(s). If missing indicator can be predicted as a function of observed covariates, MAR may be a likely scenario and would imply that imputation may be feasible.
Important: don't include variables like ID variables, ZIP codes, dates, etc.
Usage
smdi_rf(
data = NULL,
covar = NULL,
train_test_ratio = c(0.7, 0.3),
tune = FALSE,
set_seed = 42,
ntree = 1000,
n_cores = 1
)
Arguments
data |
dataframe or tibble object with partially observed/missing variables |
covar |
character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation and all remaining covariates will be used as predictors |
train_test_ratio |
numeric vector to indicate the test/train split ratio, e.g. c(.7, .3) which is the default |
tune |
logical,if TRUE, a 5-fold cross validation is performed combined with a random search for the optimal number of optimal number of variables randomly sampled as candidates at each split (mtry). FALSE is the default due to potentially extensive computation times. |
set_seed |
seed for reproducibility, defaults to 42 |
ntree |
integer, number of trees (defaults to 1000 trees) |
n_cores |
integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems) |
Details
The random forest utilizes the randomForest engine.
CAVE: If the missingness indicator variables of other partially observed covariates (indicated by suffix _NA) have an extremely high variable importance (combined with an unusually high AUC), this might be an indicator of a monotone missing data pattern. In this case it is advisable to exclude other partially observed covariates and run missingness diagnostics separately.
Value
returns an rf object which comes as a list that contains the ROC AUC value and corresponding variable importance in training dataset (latter as ggplot object). That is, for each covar, the following outputs are provided:
rf_table: The area under the receiver operating curve (AUC) as a measure of the ability to predict the missingness of the partially observed covariate
rf_plot: ggplot object illustrating the variable importance for the prediction made expressed by the mean decrease in accuracy per predictor. That is how much would the accuracy of the prediction (# of correct predictions/Total # of predictions made) decrease, had we left out this specific predictor.
OOB: estimated OOB error for each investigated partially observed confounder (indicates the performance of the random forest model for data points that are not used in training a tree.)
References
Sondhi A, Weberpals J, Yerram P, Jiang C, Taylor M, Samant M, Cherng S. A systematic approach towards missing lab data in electronic health records: A case study in non-small cell lung cancer and multiple myeloma. CPT Pharmacometrics Syst Pharmacol. 2023 Jun 15. <doi: 10.1002/psp4.12998.> Epub ahead of print. PMID: 37322818.
See Also
Examples
library(smdi)
smdi_rf(data = smdi_data, covar = "ecog_cat")