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VIM (version 6.2.2)

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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Install

install.packages('VIM')

Monthly Downloads

19,973

Version

6.2.2

License

GPL (>= 2)

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Maintainer

Last Published

August 25th, 2022

Functions in VIM (6.2.2)

colic

Colic horse data set
colSequence

HCL and RGB color sequences
VIM-package

Visualization and Imputation of Missing Values
evaluation

Error performance measures
kNN

k-Nearest Neighbour Imputation
kola.background

Background map for the Kola project data
bcancer

Breast cancer Wisconsin data set
bgmap

Backgound map
countInf

Count number of infinite or missing values
Animals_na

Animals_na
hotdeck

Hot-Deck Imputation
parcoordMiss

Parallel coordinate plot with information about missing/imputed values
rugNA

Rug representation of missing/imputed values
food

Food consumption
histMiss

Histogram with information about missing/imputed values
SBS5242

Synthetic subset of the Austrian structural business statistics data
prepare

Transformation and standardization
pbox

Parallel boxplots with information about missing/imputed values
regressionImp

Regression Imputation
pairsVIM

Scatterplot Matrices
testdata

Simulated data set for testing purpose
impPCA

Iterative EM PCA imputation
toydataMiss

Simulated toy data set for examples
sleep

Mammal sleep data
spineMiss

Spineplot with information about missing/imputed values
diabetes

Indian Prime Diabetes Data
pulplignin

Pulp lignin content
sampleCat

Random aggregation function for a factor variable
growdotMiss

Growing dot map with information about missing/imputed values
matrixplot

Matrix plot
maxCat

Aggregation function for a factor variable
marginplot

Scatterplot with additional information in the margins
rangerImpute

Random Forest Imputation
collisions

Subset of the collision data
alphablend

Alphablending for colors
barMiss

Barplot with information about missing/imputed values
mosaicMiss

Mosaic plot with information about missing/imputed values
mapMiss

Map with information about missing/imputed values
marginmatrix

Marginplot Matrix
gowerD

Computes the extended Gower distance of two data sets
gapMiss

Missing value gap statistics
initialise

Initialization of missing values
colormapMiss

Colored map with information about missing/imputed values
wine

Wine tasting and price
medianSamp

Aggregation function for a ordinal variable
irmi

Iterative robust model-based imputation (IRMI)
scattJitt

Bivariate jitter plot
tableMiss

create table with highlighted missings/imputations
tao

Tropical Atmosphere Ocean (TAO) project data
scattMiss

Scatterplot with information about missing/imputed values
matchImpute

Fast matching/imputation based on categorical variable
scattmatrixMiss

Scatterplot matrix with information about missing/imputed values
aggr

Aggregations for missing/imputed values
chorizonDL

C-horizon of the Kola data with missing values
brittleness

Brittleness index data set