# Visualization and Imputation of Missing Values

## Documentation for package ‘VIM’ version 7.3.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

A B C D E F G H I K L M N O P R S T U V W X misc

## -- A --

aggr | Aggregations for missing/imputed values
alphablend | Alphablending for colors
Animals_na | Animals_na
are | k-Nearest Neighbour Imputation
as.mids.vimmi | Convert a vimmi object to a mice mids object

## -- B --

barMiss | Barplot with information about missing/imputed values
bcancer | Breast cancer Wisconsin data set
bgmap | Backgound map
brittleness | Brittleness index data set
bubbleMiss | Growing dot map with information about missing/imputed values
by | k-Nearest Neighbour Imputation

## -- C --

cellWeightsMCD | Compute per-cell weights using MCD-based conditional residuals
check`); | k-Nearest Neighbour Imputation
chorizonDL | C-horizon of the Kola data with missing values
CMD | k-Nearest Neighbour Imputation
colic | Colic horse data set
collisions | Subset of the collision data
colormapMiss | Colored map with information about missing/imputed values
colormapMissLegend | Colored map with information about missing/imputed values
colSequence | HCL and RGB color sequences
colSequenceHCL | HCL and RGB color sequences
colSequenceRGB | HCL and RGB color sequences
complete.vimmi | Extract completed datasets from a vimmi object
computed | k-Nearest Neighbour Imputation
countInf | Count number of infinite or missing values
countNA | Count number of infinite or missing values

## -- D --

diabetes | Synthetic Pima Indians Diabetes Data
Distances | k-Nearest Neighbour Imputation
during | k-Nearest Neighbour Imputation

## -- E --

evaluation | Error performance measures

## -- F --

food | Food consumption

## -- G --

gapMiss | Missing value gap statistics
governed | k-Nearest Neighbour Imputation
gowerD | Computes the extended Gower distance of two data sets
growdotMiss | Growing dot map with information about missing/imputed values

## -- H --

histMiss | Histogram with information about missing/imputed values
hotdeck | Hot-Deck Imputation

## -- I --

iimagMiss | Matrix plot
impPCA | Iterative EM PCA imputation
imputeCellEM | Cellwise-robust EM imputation for mixed data
imputeCellIRMI | Cellwise-robust iterative regression imputation for mixed data
imputeCellM | Cellwise M-estimation imputation
imputeCellMCD | Cellwise MCD-based imputation for mixed data
imputeCellMM | Cell-weighted MM imputation for mixed data (Path A)
imputeCellReg | Cellwise-robust regression imputation for mixed data
imputeCellwise | Unified cellwise-robust imputation dispatcher
imputeRobust | Robust imputation
imputeRobustChain | FUNCTION_TITLE
initialise | Initialization of missing values
irmi | Iterative robust model-based imputation (IRMI)

## -- K --

kNN | k-Nearest Neighbour Imputation
kola.background | Background map for the Kola project data

## -- L --

lse_synthetic | Synthetic Austrian Structural Business Survey data
lse_synthetic_rules | Validation rules for the synthetic LSE data

## -- M --

makeMissing | Generate MCAR/MAR/MNAR missingness in complete data
mapMiss | Map with information about missing/imputed values
marginmatrix | Marginplot Matrix
marginplot | Scatterplot with additional information in the margins
matchImpute | Fast matching/imputation based on categorical variable
matrixplot | Matrix plot
maxCat | Aggregation function for a factor variable
medianSamp | Aggregation function for a ordinal variable
mosaicMiss | Mosaic plot with information about missing/imputed values
most | k-Nearest Neighbour Imputation
msecor | Error performance measures
msecov | Error performance measures

## -- N --

nrmse | Error performance measures

## -- O --

OpenMP | k-Nearest Neighbour Imputation
overimpute | Overimputation: calibration diagnostic for an imputation model

## -- P --

pairsVIM | Scatterplot Matrices
parcoordMiss | Parallel coordinate plot with information about missing/imputed values
pbox | Parallel boxplots with information about missing/imputed values
pfc | Error performance measures
plot.aggr | Aggregations for missing/imputed values
plot.vimmi | Diagnostic plots for a vimmi object
plot.vimpute_overimpute | Overimputation: calibration diagnostic for an imputation model
prepare | Transformation and standardization
print.aggr | Aggregations for missing/imputed values
print.summary.aggr | Aggregations for missing/imputed values
print.vimmi | VIM Multiple Imputations (vimmi)
print.vimpute_overimpute | Overimputation: calibration diagnostic for an imputation model
print.vimpute_spec | Per-variable imputation specification for 'vimpute()'
pulplignin | Pulp lignin content

## -- R --

rangerImpute | Random Forest Imputation
register_vimpute_method | Register an imputation method for 'vimpute()'
regressionImp | Regression Imputation (via vimpute)
rugNA | Rug representation of missing/imputed values

## -- S --

sampleCat | Random aggregation function for a factor variable
SBS5242 | Synthetic subset of the Austrian structural business statistics data
scattJitt | Bivariate jitter plot
scattmatrixMiss | Scatterplot matrix with information about missing/imputed values
scattMiss | Scatterplot with information about missing/imputed values
see | k-Nearest Neighbour Imputation
sleep | Mammal sleep data
spineMiss | Spineplot with information about missing/imputed values
summary.aggr | Aggregations for missing/imputed values
summary.vimmi | VIM Multiple Imputations (vimmi)

## -- T --

tableMiss | create table with highlighted missings/imputations
tao | Tropical Atmosphere Ocean (TAO) project data
testdata | Simulated data set for testing purpose
there. | k-Nearest Neighbour Imputation
threads | k-Nearest Neighbour Imputation
TKRmatrixplot | Matrix plot
toydataMiss | Simulated toy data set for examples

## -- U --

unregister_vimpute_method | Remove a user-registered 'vimpute()' method

## -- V --

vimmi | VIM Multiple Imputations (vimmi)
vimpute | Impute missing values with prefered model, sequentially, with hyperparametertuning and with PMM (if wanted)
vimpute_methods | List the imputation methods registered for 'vimpute()'
vimpute_search_space | The built-in tuning search space of a learner
vimpute_spec | Per-variable imputation specification for 'vimpute()'
vimpute_tune_control | Control the hyperparameter tuning of 'vimpute()'
vim_as_mids | Convert a vimmi object to a mice mids object
vim_complete | Extract completed datasets from a vimmi object
vs_gam | Per-variable imputation specification for 'vimpute()'
vs_ranger | Per-variable imputation specification for 'vimpute()'
vs_regularized | Per-variable imputation specification for 'vimpute()'
vs_robgam | Per-variable imputation specification for 'vimpute()'
vs_robust | Per-variable imputation specification for 'vimpute()'
vs_xgboost | Per-variable imputation specification for 'vimpute()'

## -- W --

whose | k-Nearest Neighbour Imputation
wine | Wine tasting and price
with.vimmi | Evaluate an expression across all imputations

## -- X --

xgboostImpute | Xgboost Imputation

## -- misc --

(at | k-Nearest Neighbour Imputation
)` | k-Nearest Neighbour Imputation
2 | k-Nearest Neighbour Imputation
= | k-Nearest Neighbour Imputation
[gowerD()], | k-Nearest Neighbour Imputation
`options(VIM.ncores | k-Nearest Neighbour Imputation
`R | k-Nearest Neighbour Imputation