Visualization and Imputation of Missing Values

Documentation for package ‘VIM’ version 7.3.0

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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