useR! 2024

Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The newly released missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. The algorithm iteratively imputes variables using random forests until a convergence criterion (unified for continuous and categorical variables and based on the out-of-bag error) is met. The imputation models are saved for each variable and iteration and can be applied later to new observations. The missForestPredict package offers extended error monitoring, control over variables used in the imputation and custom initialization. This allows users to tailor the imputation to their specific needs. The missForestPredict algorithm is further compared to mean/mode imputation, k-nearest neighbours, bagging and two iterative algorithms (miceRanger and IterativeImputer) on 8 simulated datasets with simulated missingness and 8 public datasets using different prediction models. missForestPredict provides satisfactory results within short computation times.