Visualization and Imputation of Missing Values
Aggregations for missing/imputed values
Alphablending for colors
Barplot with information about missing/imputed values
Backgound map
Colored map with information about missing/imputed values
HCL and RGB color sequences
Count number of infinite or missing values
Error performance measures
Missing value gap statistics
Computes the extended Gower distance of two data sets
Growing dot map with information about missing/imputed values
Histogram with information about missing/imputed values
Hot-Deck Imputation
Iterative EM PCA imputation
Robust imputation
FUNCTION_TITLE
Initialization of missing values
Iterative robust model-based imputation (IRMI)
k-Nearest Neighbour Imputation
Map with information about missing/imputed values
Marginplot Matrix
Scatterplot with additional information in the margins
Fast matching/imputation based on categorical variable
Matrix plot
Aggregation function for a factor variable
Aggregation function for a ordinal variable
Mosaic plot with information about missing/imputed values
Scatterplot Matrices
Parallel coordinate plot with information about missing/imputed values
Parallel boxplots with information about missing/imputed values
Transformation and standardization
Random Forest Imputation
Regression Imputation
Rug representation of missing/imputed values
Random aggregation function for a factor variable
Bivariate jitter plot
Scatterplot matrix with information about missing/imputed values
Scatterplot with information about missing/imputed values
Spineplot with information about missing/imputed values
create table with highlighted missings/imputations
The VIM Package: Visualization and Imputation of Missing Values
Impute missing values with prefered Model, sequentially, with hyperpar...
Xgboost Imputation
Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, <doi:10.1007/s11634-011-0102-y>. The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, <doi:10.18637/jss.v074.i07>, iterative robust model-based multiple imputation (Templ 2011, <doi:10.1016/j.csda.2011.04.012>; Templ 2023, <doi:10.3390/math11122729>), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, <doi:10.1007/s11222-024-10429-1>) as well as gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., <doi:10.1177/18747655251339401>). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) <doi:10.1007/978-3-031-30073-8>.