Learn R Programming

⚠️There's a newer version (7.0.0) of this package.Take me there.

VIM (version 6.1.1)

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.

Copy Link

Version

Install

install.packages('VIM')

Monthly Downloads

19,631

Version

6.1.1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Matthias Templ

Last Published

July 22nd, 2021

Functions in VIM (6.1.1)

chorizonDL

C-horizon of the Kola data with missing values
bcancer

Breast cancer Wisconsin data set
barMiss

Barplot with information about missing/imputed values
colSequence

HCL and RGB color sequences
gapMiss

Missing value gap statistics
food

Food consumption
diabetes

Indian Prime Diabetes Data
VIM-package

Visualization and Imputation of Missing Values
SBS5242

Synthetic subset of the Austrian structural business statistics data
colormapMiss

Colored map with information about missing/imputed values
evaluation

Error performance measures
matchImpute

Fast matching/imputation based on categorical variable
marginplot

Scatterplot with additional information in the margins
histMiss

Histogram with information about missing/imputed values
countInf

Count number of infinite or missing values
initialise

Initialization of missing values
pulplignin

Pulp lignin content
irmi

Iterative robust model-based imputation (IRMI)
bgmap

Backgound map
alphablend

Alphablending for colors
gowerD

Computes the extended Gower distance of two data sets
rangerImpute

Random Forest Imputation
aggr

Aggregations for missing/imputed values
scattMiss

Scatterplot with information about missing/imputed values
hotdeck

Hot-Deck Imputation
collisions

Subset of the collision data
colic

Colic horse data set
brittleness

Brittleness index data set
marginmatrix

Marginplot Matrix
matrixplot

Matrix plot
mapMiss

Map with information about missing/imputed values
kola.background

Background map for the Kola project data
kNN

k-Nearest Neighbour Imputation
maxCat

Aggregation function for a factor variable
spineMiss

Spineplot with information about missing/imputed values
pairsVIM

Scatterplot Matrices
sleep

Mammal sleep data
parcoordMiss

Parallel coordinate plot with information about missing/imputed values
testdata

Simulated data set for testing purpose
scattmatrixMiss

Scatterplot matrix with information about missing/imputed values
toydataMiss

Simulated toy data set for examples
pbox

Parallel boxplots with information about missing/imputed values
growdotMiss

Growing dot map with information about missing/imputed values
scattJitt

Bivariate jitter plot
prepare

Transformation and standardization
sampleCat

Random aggregation function for a factor variable
tableMiss

create table with highlighted missings/imputations
tao

Tropical Atmosphere Ocean (TAO) project data
wine

Wine tasting and price
medianSamp

Aggregation function for a ordinal variable
regressionImp

Regression Imputation
mosaicMiss

Mosaic plot with information about missing/imputed values
rugNA

Rug representation of missing/imputed values