Package: VIM 6.2.4

Matthias Templ

VIM: 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.

Authors:Matthias Templ [aut, cre], Alexander Kowarik [aut], Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]

VIM_6.2.4.tar.gz
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VIM_6.2.4.tar.gz(r-4.5-noble)VIM_6.2.4.tar.gz(r-4.4-noble)
VIM.pdf |VIM.html✨
VIM/json (API)
NEWS

# Install 'VIM' in R:

install.packages('VIM', repos = c('https://statistikat.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/statistikat/vim/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

Animals_na - Animals_na
SBS5242 - Synthetic subset of the Austrian structural business statistics data
bcancer - Breast cancer Wisconsin data set
brittleness - Brittleness index data set
chorizonDL - C-horizon of the Kola data with missing values
colic - Colic horse data set
collisions - Subset of the collision data
diabetes - Indian Prime Diabetes Data
food - Food consumption
kola.background - Background map for the Kola project data
pulplignin - Pulp lignin content
sleep - Mammal sleep data
tao - Tropical Atmosphere Ocean (TAO) project data
testdata - Simulated data set for testing purpose
toydataMiss - Simulated toy data set for examples
wine - Wine tasting and price

On CRAN:

hotdeck imputation-methods model-predictions visualization

14.49 score 83 stars 14 packages 2.4k scripts 19k downloads 64 mentions 47 exports 74 dependencies

Last updated 3 months agofrom:8ea37f292b. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-win-x86_64	NOTE	Nov 05 2024
R-4.5-linux-x86_64	NOTE	Nov 05 2024
R-4.4-win-x86_64	NOTE	Nov 05 2024
R-4.4-mac-x86_64	NOTE	Nov 05 2024
R-4.4-mac-aarch64	NOTE	Nov 05 2024
R-4.3-win-x86_64	NOTE	Nov 05 2024
R-4.3-mac-x86_64	NOTE	Nov 05 2024
R-4.3-mac-aarch64	NOTE	Nov 05 2024

Exports:aggr alphablend barMiss bgmap colormapMiss colormapMissLegend colSequence colSequenceHCL colSequenceRGB countInf countNA evaluation gapMiss gowerD growdotMiss histMiss hotdeck impPCA initialise irmi kNN mapMiss marginmatrix marginplot matchImpute matrixplot maxCat medianSamp mosaicMiss msecor msecov nrmse pairsVIM parcoordMiss pbox pfc prepare rangerImpute regressionImp rugNA sampleCat scattJitt scattmatrixMiss scattMiss spineMiss tableMiss xgboostImpute

Dependencies:abind backports boot broom car carData class cli colorspace cowplot cpp11 data.table DEoptimR Deriv doBy dplyr e1071 fansi farver Formula generics ggplot2 glue gtable isoband jsonlite labeling laeken lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa modelr munsell nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig proxy purrr quantreg R6 ranger RColorBrewer Rcpp RcppEigen rlang robustbase scales sp SparseM stringi stringr survival tibble tidyr tidyselect utf8 vcd vctrs viridisLite withr xgboost zoo

Donor based Imputation Methods

Wolfgang Rannetbauer

Rendered fromdonorImp.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-12-19
Started: 2020-07-15

Imputation Method based on Iterative EM PCA

Birgit Karlhuber

Rendered fromimpPCA.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2024-07-17
Started: 2024-07-17

Imputation Method based on xgboost

Birgit Karlhuber

Rendered fromxgboostImpute.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2024-08-07
Started: 2024-07-17

Imputation Method IRMI

Wolfgang Rannetbauer

Rendered fromirmi.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2023-11-10
Started: 2020-07-14

Model based Imputation Methods

Gregor de Cillia

Rendered frommodelImp.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2022-12-19
Started: 2020-04-17

Supportive Graphic Methods

Wolfgang Rannetbauer, Birgit Karlhuber

Rendered fromVisualImp.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2024-07-17
Started: 2020-07-15

VIM

Rendered fromVIM.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2020-07-17
Started: 2020-04-14

Help page	Topics
Visualization and Imputation of Missing Values	VIM-package VIM
Aggregations for missing/imputed values	aggr plot.aggr print.aggr print.summary.aggr summary.aggr
Alphablending for colors	alphablend
Animals_na	Animals_na
Barplot with information about missing/imputed values	barMiss
Breast cancer Wisconsin data set	bcancer
Backgound map	bgmap
Brittleness index data set	brittleness
C-horizon of the Kola data with missing values	chorizonDL
Colic horse data set	colic
Subset of the collision data	collisions
Colored map with information about missing/imputed values	colormapMiss colormapMissLegend
HCL and RGB color sequences	colSequence colSequenceHCL colSequenceRGB
Count number of infinite or missing values	countInf countNA
Indian Prime Diabetes Data	diabetes
Error performance measures	evaluation msecor msecov nrmse pfc
Food consumption	food
Missing value gap statistics	gapMiss
Computes the extended Gower distance of two data sets	gowerD
Growing dot map with information about missing/imputed values	bubbleMiss growdotMiss
Histogram with information about missing/imputed values	histMiss
Hot-Deck Imputation	hotdeck
Iterative EM PCA imputation	impPCA
Initialization of missing values	initialise
Iterative robust model-based imputation (IRMI)	irmi
k-Nearest Neighbour Imputation	kNN
Background map for the Kola project data	kola.background
Map with information about missing/imputed values	mapMiss
Marginplot Matrix	marginmatrix
Scatterplot with additional information in the margins	marginplot
Fast matching/imputation based on categorical variable	matchImpute
Matrix plot	iimagMiss matrixplot TKRmatrixplot
Aggregation function for a factor variable	maxCat
Aggregation function for a ordinal variable	medianSamp
Mosaic plot with information about missing/imputed values	mosaicMiss
Scatterplot Matrices	pairsVIM
Parallel coordinate plot with information about missing/imputed values	parcoordMiss
Parallel boxplots with information about missing/imputed values	pbox
Transformation and standardization	prepare
Pulp lignin content	pulplignin
Random Forest Imputation	rangerImpute
Regression Imputation	regressionImp
Rug representation of missing/imputed values	rugNA
Random aggregation function for a factor variable	sampleCat
Synthetic subset of the Austrian structural business statistics data	SBS5242
Bivariate jitter plot	scattJitt
Scatterplot matrix with information about missing/imputed values	scattmatrixMiss
Scatterplot with information about missing/imputed values	scattMiss
Mammal sleep data	sleep
Spineplot with information about missing/imputed values	spineMiss
create table with highlighted missings/imputations	tableMiss
Tropical Atmosphere Ocean (TAO) project data	tao
Simulated data set for testing purpose	testdata
Simulated toy data set for examples	toydataMiss
Wine tasting and price	wine
Xgboost Imputation	xgboostImpute

Package: VIM 6.2.4

VIM: Visualization and Imputation of Missing Values

Donor based Imputation Methods

Imputation Method based on Iterative EM PCA

Imputation Method based on xgboost

Imputation Method IRMI

Model based Imputation Methods

Supportive Graphic Methods

VIM

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)