Visualization and Imputation of 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
Initialization of missing values
Iterative robust model-based imputation (IRMI)
k-Nearest Neighbour Imputation
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
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
Visualization and Imputation of Missing Values
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.