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VIM (version 6.0.0)

Visualization and Imputation of Missing Values

Description

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.

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Version

Install

install.packages('VIM')

Monthly Downloads

20,480

Version

6.0.0

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Maintainer

Matthias Templ

Last Published

May 8th, 2020

Functions in VIM (6.0.0)

colic

Colic horse data set
kola.background

Background map for the Kola project data
food

Food consumption
rangerImpute

Random Forest Imputation
colormapMiss

Colored map with information about missing/imputed values
gapMiss

Missing value gap statistics
gowerD

Computes the extended Gower distance of two data sets
mosaicMiss

Mosaic plot with information about missing/imputed values
kNN

k-Nearest Neighbour Imputation
evaluation

Error performance measures
collisions

Subset of the collision data
spineMiss

Spineplot with information about missing/imputed values
diabetes

Indian Prime Diabetes Data
scattMiss

Scatterplot with information about missing/imputed values
growdotMiss

Growing dot map with information about missing/imputed values
regressionImp

Regression Imputation
marginplot

Scatterplot with additional information in the margins
testdata

Simulated data set for testing purpose
pairsVIM

Scatterplot Matrices
irmi

Iterative robust model-based imputation (IRMI)
tao

Tropical Atmosphere Ocean (TAO) project data
initialise

Initialization of missing values
VIM-package

Visualization and Imputation of Missing Values
pbox

Parallel boxplots with information about missing/imputed values
marginmatrix

Marginplot Matrix
mapMiss

Map with information about missing/imputed values
parcoordMiss

Parallel coordinate plot with information about missing/imputed values
toydataMiss

Simulated toy data set for examples
countInf

Count number of infinite or missing values
matchImpute

Fast matching/imputation based on categorical variable
prepare

Transformation and standardization
maxCat

Aggregation function for a factor variable
scattmatrixMiss

Scatterplot matrix with information about missing/imputed values
scattJitt

Bivariate jitter plot
matrixplot

Matrix plot
histMiss

Histogram with information about missing/imputed values
hotdeck

Hot-Deck Imputation
rugNA

Rug representation of missing/imputed values
sampleCat

Random aggregation function for a factor variable
pulplignin

Pulp lignin content
wine

Wine tasting and price
sleep

Mammal sleep data
barMiss

Barplot with information about missing/imputed values
bgmap

Backgound map
alphablend

Alphablending for colors
aggr

Aggregations for missing/imputed values
bcancer

Breast cancer Wisconsin data set
brittleness

Brittleness index data set
colSequence

HCL and RGB color sequences
chorizonDL

C-horizon of the Kola data with missing values
SBS5242

Synthetic subset of the Austrian structural business statistics data