Movatterモバイル変換

Version:	6.2.6
Title:	Visualization and Imputation of Missing Values
Depends:	R (≥ 4.1.0),colorspace,grid
Imports:	car, grDevices, robustbase, stats, sp, vcd, nnet, e1071,methods, Rcpp, utils, graphics, laeken, ranger, MASS, xgboost,data.table(≥ 1.9.4)
Suggests:	dplyr, tinytest, knitr, mgcv, rmarkdown, reactable, covr,withr, pdist, enetLTS, robmixglm, stringr
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.
LazyData:	TRUE
ByteCompile:	TRUE
License:	GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/statistikat/VIM
Repository:	CRAN
LinkingTo:	Rcpp
RoxygenNote:	7.3.2
Encoding:	UTF-8
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2025-09-17 14:04:34 UTC; matthias
Author:	Matthias Templ [aut, cre], Alexander Kowarik [aut], Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]
Maintainer:	Matthias Templ <matthias.templ@gmail.com>
Date/Publication:	2025-09-18 05:10:48 UTC

The VIM Package

Description

VIM provides tools for visualization, imputation, and exploration of missingand multivariate data.

Details

Visualization and Imputation of Missing Values

This package introduces new tools for the visualization of missing orimputed values in , which can be used for exploring the data and thestructure of the missing or imputed values. Depending on this structure,they may help to identify the mechanism generating the missing values orerrors, which may have happened in the imputation process. This knowledge isnecessary for selecting an appropriate imputation method in order toreliably estimate the missing values. Thus the visualization tools should beapplied before imputation and the diagnostic tools afterwards.

Detecting missing values mechanisms is usually done by statistical tests ormodels. Visualization of missing and imputed values can support the testdecision, but also reveals more details about the data structure. Mostnotably, statistical requirements for a test can be checked graphically, andproblems like outliers or skewed data distributions can be discovered.Furthermore, the included plot methods may also be able to detect missingvalues mechanisms in the first place.

A graphical user interface available in the package VIMGUI allows an easyhandling of the plot methods. In addition,VIM can be used for datafrom essentially any field.

This package includes advanced imputation methods, robust statistics,and tools for data preprocessing and diagnostics.

Author(s)

Matthias Templ, Andreas Alfons, Alexander Kowarik, Bernd Prantner

Maintainer: Matthias Templtempl@tuwien.ac.at

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise regressionimputation using standard and robust methods.Journal ofComputational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.

Animals_na

Description

Average log brain and log body weights for 28 Species

Format

A data frame with 28 observations on the following 2 variables.

lbody

log body weight

lbrain

log brain weight

Details

The original data can be found in package MASS.10 values on brain weight are set to be missing.

Source

P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley, p. 57.

References

Venables, W. N. and Ripley, B. D. (1999) Modern Applied Statistics with S-PLUS. Third Edition. Springer.

Templ, M. (2022) Visualization and Imputation of Missing Values. Springer Publishing. Upcoming book.

Examples

data(Animals_na)aggr(Animals_na)

Synthetic subset of the Austrian structural business statistics data

Description

Synthetic subset of the Austrian structural business statistics (SBS) data,namely NACE code 52.42 (retail sale of clothing).

Details

The Austrian SBS data set consists of more than 320.000 enterprises.Available raw (unedited) data set: 21669 observations in 90 variables,structured according NACE revision 1.1 with 3891 missing values.

We investigate 9 variables of NACE 52.42 (retail sale of clothing).

From these confidential raw data set a non-confidential, close-to-reality,synthetic data set was generated.

Source

http://www.statistik.at

Examples

data(SBS5242)aggr(SBS5242)

Aggregations for missing/imputed values

Description

Calculate or plot the amount of missing/imputed values in each variable andthe amount of missing/imputed values in certain combinations of variables.

Print method for objects of class"aggr".

Summary method for objects of class"aggr".

Print method for objects of class"summary.aggr".

Usage

aggr(x, delimiter = NULL, plot = TRUE, ...)## S3 method for class 'aggr'plot(  x,  col = c("skyblue", "red", "orange"),  bars = TRUE,  numbers = FALSE,  prop = TRUE,  combined = FALSE,  varheight = FALSE,  only.miss = FALSE,  border = par("fg"),  sortVars = FALSE,  sortCombs = TRUE,  ylabs = NULL,  axes = TRUE,  labels = axes,  cex.lab = 1.2,  cex.axis = par("cex"),  cex.numbers = par("cex"),  gap = 4,  ...)## S3 method for class 'aggr'print(x, ..., digits = NULL)## S3 method for class 'aggr'summary(object, ...)## S3 method for class 'summary.aggr'print(x, ...)

Arguments

Details

Often it is of interest how many missing/imputed values are contained ineach variable. Even more interesting, there may be certain combinations ofvariables with a high number of missing/imputed values.

Ifcombined isFALSE, two separate plots are drawn for themissing/imputed values in each variable and the combinations ofmissing/imputed and non-missing values. The barplot on the left hand sideshows the amount of missing/imputed values in each variable. In theaggregation plot on the right hand side, all existing combinations ofmissing/imputed and non-missing values in the observations are visualized.Available, missing and imputed data are color coded as given bycol.Additionally, there are two possibilities to represent the frequencies ofoccurrence of the different combinations. The first option is to visualizethe proportions or frequencies by a small bar plot and/or numbers. Thesecond option is to let the cell heights be given by the frequencies of thecorresponding combinations. Furthermore, variables may be sorted by thenumber of missing/imputed values and combinations by the frequency ofoccurrence to give more power to finding the structure of missing/imputedvalues.

Ifcombined isTRUE, a small version of the barplot showingthe amount of missing/imputed values in each variable is drawn on top of theaggregation plot.

The graphical parameteroma will be set unless supplied as anargument.

Value

foraggr, a list of class"aggr" containing thefollowing components:

• x the data used.

• combinations a character vector representing the combinations ofvariables.

• count the frequencies of these combinations.

• percent the percentage of these combinations.

• missings adata.frame containing the amount ofmissing/imputed values in each variable.

• tabcomb the indicator matrix for the combinations of variables.

a list of class"summary.aggr" containing the followingcomponents:

• missings adata.frame containing the amount of missing orimputed values in each variable.

• combinations adata.frame containing a character vectorrepresenting the combinations of variables along with their frequencies andpercentages.

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentslabsandnames.arg can still be supplied to...{} and are handledcorrectly. Nevertheless, they are deprecated and no longer documented. Useylabs andlabels instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications for displaying imputedvalues by Bernd Prantner

Matthias Templ, modifications by Andreas Alfons and Bernd Prantner

Matthias Templ, modifications by Andreas Alfons

Andreas Alfons, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

print.aggr(),summary.aggr()

aggr()

print.summary.aggr(),aggr()

summary.aggr(),aggr()

Other plotting functions:barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package="VIM")## for missing valuesa <- aggr(sleep)asummary(a)## for imputed valuessleep_IMPUTED <- kNN(sleep)a <- aggr(sleep_IMPUTED, delimiter="_imp")asummary(a)data(sleep, package = "VIM")a <- aggr(sleep, plot=FALSE)adata(sleep, package = "VIM")summary(aggr(sleep, plot=FALSE))data(sleep, package = "VIM")s <- summary(aggr(sleep, plot=FALSE))s

Alphablending for colors

Description

Convert colors to semitransparent colors.

Usage

alphablend(col, alpha = NULL, bg = NULL)

Arguments

Value

a vector containing the semitransparent colors.

Author(s)

Andreas Alfons

Examples

alphablend("red", 0.6)

Barplot with information about missing/imputed values

Description

Barplot with highlighting of missing/imputed values in other variables bysplitting each bar into two parts. Additionally, information aboutmissing/imputed values in the variable of interest is shown on the righthand side.

Usage

barMiss(  x,  delimiter = NULL,  pos = 1,  selection = c("any", "all"),  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),  border = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  labels = axes,  only.miss = TRUE,  miss.labels = axes,  interactive = TRUE,  ...)

Arguments

Details

If more than one variable is supplied, the bars for the variable of interestare split according to missingness/number of imputed missings in theadditional variables.

Ifonly.miss=TRUE, the missing/imputed values in the variable ofinterest are visualized by one bar on the right hand side. If additionalvariables are supplied, this bar is again split into two parts according tomissingness/number of imputed missings in the additional variables.

Otherwise, a small barplot consisting of two bars is drawn on the right handside. The first bar corresponds to observed values in the variable ofinterest and the second bar to missing/imputed values. Since these two barsare not on the same scale as the main barplot, a second y-axis is plotted onthe right (ifaxes=TRUE). Each of the two bars are again split intotwo parts according to missingness/number of imputed missings in theadditional variables. Note that this display does not make sense if onlyone variable is supplied, thereforeonly.miss is ignored in thatcase.

Ifinteractive=TRUE, clicking in the left margin of the plot resultsin switching to the previous variable and clicking in the right marginresults in switching to the next variable. Clicking anywhere else on thegraphics device quits the interactive session. When switching to acontinuous variable, a histogram is plotted rather than a barplot.

Value

a numeric vector giving the coordinates of the midpoints of thebars.

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentsaxisnames,names.arg andnames.miss can still besupplied to...{} and are handled correctly. Nevertheless, theyare deprecated and no longer documented. Uselabels andmiss.labels instead.

Author(s)

Andreas Alfons, modifications to show imputed values by BerndPrantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

spineMiss(),histMiss()

Other plotting functions:aggr(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package = "VIM")## for missing valuesx <- sleep[, c("Exp", "Sleep")]barMiss(x)barMiss(x, only.miss = FALSE)## for imputed valuesx_IMPUTED  <- kNN(sleep[, c("Exp", "Sleep")])barMiss(x_IMPUTED, delimiter = "_imp")barMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)

Breast cancer Wisconsin data set

Description

Dataset containing the original Wisconsin breast cancer data.

Format

A data frame with 699 observations on the following 11 variables.

ID

Sample ID

clump_thickness

as integer from 1 - 10

uniformity_cellsize

as integer from 1 - 10

uniformity_cellshape

as integer from 1 - 10

adhesion

as integer from 1 - 10

epithelial_cellsize

as integer from 1 - 10

bare_nuclei

as integer from 1 - 10, includes 16 missings

chromatin

as integer from 1 - 10

normal_nucleoli

as integer from 1 - 10

mitoses

as integer from 1 - 10

class

benign or malignant

References

The data downloaded and conditioned for R from the UCI machine learning repository,see https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)This breast cancer databases was obtained from the University of Wisconsin Hospitals,Madison from Dr. William H. Wolberg. If you publish results when using this database,then please include this information in your acknowledgements.Also, please cite one or more of:O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming",SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18.William H. Wolberg and O.L. Mangasarian:"Multisurface method of pattern separation for medical diagnosis applied to breast cytology",Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196.O. L. Mangasarian, R. Setiono, and W.H. Wolberg:"Pattern recognition via linear programming: Theory and application to medical diagnosis",in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors,SIAM Publications, Philadelphia 1990, pp 22-30.K. P. Bennett & O. L. Mangasarian:"Robust linear programming discrimination of two linearly inseparable sets",Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers).

Examples

data(bcancer)aggr(bcancer)

Backgound map

Description

Plot a background map.

Usage

bgmap(map, add = FALSE, ...)

Arguments

Author(s)

Andreas Alfons

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

growdotMiss(),mapMiss()

Examples

data(kola.background, package = "VIM")bgmap(kola.background)

Brittleness index data set

Description

A plastic product is produced in three parallel reactors (TK104, TK105, or TK107).For each row in the dataset, we have the same batch of raw material that was split, and fed to the 3 reactors.These values are the brittleness index for the product produced in the reactor. A simulated data set.

Format

A data frame with 23 observations on the following 3 variables.

TK104

Brittleness for batches of raw material in reactor 104

TK105

Brittleness for batches of raw material in reactor 105

TK107

Brittleness for batches of raw material in reactor 107

Source

https://openmv.net/info/brittleness-index

Examples

data(brittleness)aggr(brittleness)

C-horizon of the Kola data with missing values

Description

This data set is the same asin packagemvoutlier, except that values below the detection limitare coded asNA.

Format

A data frame with 606 observations on the following 110 variables.

*ID

a numeric vector

XCOO

anumeric vector

YCOO

a numeric vector

Ag

anumeric vector

Ag_INAA

a numeric vector

Al

anumeric vector

Al2O3

a numeric vector

As

anumeric vector

As_INAA

a numeric vector

Au_INAA

a numeric vector

B

a numeric vector

Ba

a numeric vector

Ba_INAA

a numeric vector

Be

a numeric vector

Bi

a numeric vector

Br_IC

a numeric vector

Br_INAA

a numericvector

Ca

a numeric vector

Ca_INAA

a numericvector

CaO

a numeric vector

Cd

a numericvector

Ce_INAA

a numeric vector

Cl_IC

anumeric vector

Co

a numeric vector

Co_INAA

anumeric vector

EC

a numeric vector

Cr

anumeric vector

Cr_INAA

a numeric vector

Cs_INAA

a numeric vector

Cu

a numeric vector

Eu_INAA

a numeric vector

F_IC

a numericvector

Fe

a numeric vector

Fe_INAA

a numericvector

Fe2O3

a numeric vector

Hf_INAA

anumeric vector

Hg

a numeric vector

Hg_INAA

anumeric vector

Ir_INAA

a numeric vector

K

anumeric vector

K2O

a numeric vector

La

anumeric vector

La_INAA

a numeric vector

Li

anumeric vector

LOI

a numeric vector

Lu_INAA

a numeric vector

wt_INAA

a numericvector

Mg

a numeric vector

MgO

a numericvector

Mn

a numeric vector

MnO

a numericvector

Mo

a numeric vector

Mo_INAA

a numericvector

Na

a numeric vector

Na_INAA

a numericvector

Na2O

a numeric vector

Nd_INAA

anumeric vector

Ni

a numeric vector

Ni_INAA

anumeric vector

NO3_IC

a numeric vector

P

anumeric vector

P2O5

a numeric vector

Pb

anumeric vector

pH

a numeric vector

PO4_IC

anumeric vector

Rb

a numeric vector

S

anumeric vector

Sb

a numeric vector

Sb_INAA

anumeric vector

Sc

a numeric vector

Sc_INAA

anumeric vector

Se

a numeric vector

Se_INAA

anumeric vector

Si

a numeric vector

SiO2

anumeric vector

Sm_INAA

a numeric vector

Sn_INAA

a numeric vector

SO4_IC

a numericvector

Sr

a numeric vector

Sr_INAA

a numericvector

SUM_XRF

a numeric vector

Ta_INAA

anumeric vector

Tb_INAA

a numeric vector

Te

anumeric vector

Th

a numeric vector

Th_INAA

anumeric vector

Ti

a numeric vector

TiO2

anumeric vector

U_INAA

a numeric vector

V

anumeric vector

W_INAA

a numeric vector

Y

anumeric vector

Yb_INAA

a numeric vector

Zn

anumeric vector

Zn_INAA

a numeric vector

ELEV

a numeric vector

*COUN

a numeric vector

*ASP

a numeric vector

TOPC

a numeric vector

LITO

a numeric vector

Al_XRF

a numericvector

Ca_XRF

a numeric vector

Fe_XRF

anumeric vector

K_XRF

a numeric vector

Mg_XRF

a numeric vector

Mn_XRF

a numericvector

Na_XRF

a numeric vector

P_XRF

anumeric vector

Si_XRF

a numeric vector

Ti_XRF

a numeric vector

Note

For a more detailed description of this data set, see the help filechorizon in packagemvoutlier.

Source

Kola Project (1993-1998)

References

Reimann, C., Filzmoser, P., Garrett, R.G. and Dutter, R. (2008)Statistical Data Analysis Explained: Applied Environmental Statisticswith R. Wiley.

Examples

data(chorizonDL, package = "VIM")summary(chorizonDL)

HCL and RGB color sequences

Description

Compute color sequences by linear interpolation based on a continuous colorscheme between certain start and end colors. Color sequences may thereby becomputed in theHCL orRGB color space.

Usage

colSequence(p, start, end, space = c("hcl", "rgb"), ...)colSequenceRGB(p, start, end, fixup = TRUE, ...)colSequenceHCL(p, start, end, fixup = TRUE, ...)

Arguments

Value

A character vector containing hexadecimal strings of the form"#RRGGBB".

Author(s)

Andreas Alfons

References

Zeileis, A., Hornik, K., Murrell, P. (2009) Escaping RGBland:Selecting colors for statistical graphics.Computational Statistics &Data Analysis,53 (9), 1259–1270.

See Also

colorspace::hex(),colorspace::sequential_hcl()

Examples

p <- c(0, 0.3, 0.55, 0.8, 1)## HCL colorscolSequence(p, c(0, 0, 100), c(0, 100, 50))colSequence(p, polarLUV(L=90, C=30, H=90), c(0, 100, 50))## RGB colorscolSequence(p, c(1, 1, 1), c(1, 0, 0), space="rgb")colSequence(p, RGB(1, 1, 0), "red")

Colic horse data set

Description

This is a modified version of the original training data settaken from the UCI repository, see reference.The modifications are only related to having appropriate levels for factor variables.This data set is about horse diseases where the task is to determine,if the lesion of the horse was surgical or not.

Format

A training data frame with 300 observations on the following 31 variables.

surgery

yes or no

age

1 equals an adult horse, 2 is a horse younger than 6 months

hospitalID

ID

temp_rectal

rectal temperature

pulse

heart rate in beats per minute

respiratory_rate

a normal rate is between 8 and 10

temp_extreme

temperature of extremities

pulse_peripheral

factor with four categories

capillayr_refill_time

a clinical judgement. The longer the refill, the poorer the circulation. Possible values are1 = < 3 seconds and 2 = >= 3 seconds

pain

a subjective judgement of the horse's pain level

peristalsis

an indication of the activity in the horse's gut.As the gut becomes more distended or the horse becomes more toxic, the activity decreases

abdominal_distension

An animal with abdominal distension is likely to be painful and have reduced gut motility.A horse with severe abdominal distension is likely to require surgery just tio relieve the pressure

nasogastric_tube

This refers to any gas coming out of the tube.A large gas cap in the stomach is likely to give the horse discomfort

nasogastric_reflux

posible values are 1 = none, 2 = > 1 liter, 3 = < 1 liter.The greater amount of reflux, the more likelihood that there is someserious obstruction to the fluid passage from the rest of the intestine

nasogastric_reflux_PH

scale is from 0 to 14 with 7 being neutral.Normal values are in the 3 to 4 range

rectal_examination

Rectal examination. Absent feces probably indicates an obstruction

abdomen

abdomen. possible values 1 = normal, 2 = other, 3 = firm feces in the large intestine,4 = distended small intestine, 5 = distended large intestine

cell_volume

packed cell volume. normal range is 30 to 50.The level rises as the circulation becomes compromised or as the animal becomes dehydrated.

protein

total protein. Normal values lie in the 6-7.5 (gms/dL) range. The higher the value the greater the dehydration

abdominocentesis_appearance

Abdominocentesis appearance.A needle is put in the horse's abdomen and fluid is obtained from the abdominal cavity

abdomcentesis_protein

abdomcentesis total protein.The higher the level of protein the more likely it is to have a compromised gut. Values are in gms/dL

outcome

What eventually happened to the horse?

surgical_lesion

retrospectively, was the problem (lesion) surgical?

lesion_type1

type of lesion

lesion_type2

type of lesion

lesion_type3

type of lesion

cp_data

temp_extreme_ordered

temperature of extremities (ordered)

mucous_membranes_col

mucous membranes. A subjective measurement of colour

mucous_membranes_group

different recodings of mucous membrances

Source

https://archive.ics.uci.edu/ml/datasets/Horse+ColicCreators: Mary McLeish & Matt Cecile, Department of Computer Science, University of Guelph,Guelph, Ontario, Canada N1G 2W1Donor: Will Taylor

Examples

data(colic)aggr(colic)

Subset of the collision data

Description

Subset of the collision data from December 20. to December 31. 2018 from NYCD.

Details

Each record represents a collision in NYC by city, borough, precinct and cross street.

Source

https://data.cityofnewyork.us/Public-Safety/NYPD-Motor-Vehicle-Collisions/h9gi-nx95

Examples

data(collisions)aggr(collisions)

Colored map with information about missing/imputed values

Description

Colored map in which the proportion or amount of missing/imputed values ineach region is coded according to a continuous or discrete color scheme.The sequential color palette may thereby be computed in theHCL ortheRGB color space.

Usage

colormapMiss(  x,  region,  map,  imp_index = NULL,  prop = TRUE,  polysRegion = 1:length(x),  range = NULL,  n = NULL,  col = c("red", "orange"),  gamma = 2.2,  fixup = TRUE,  coords = NULL,  numbers = TRUE,  digits = 2,  cex.numbers = 0.8,  col.numbers = par("fg"),  legend = TRUE,  interactive = TRUE,  ...)colormapMissLegend(  xleft,  ybottom,  xright,  ytop,  cmap,  n = 1000,  horizontal = TRUE,  digits = 2,  cex.numbers = 0.8,  col.numbers = par("fg"),  ...)

Arguments

Details

The proportion or amount of missing/imputed values inx of eachregion is coded according to a continuous or discrete color scheme in thecolor range defined bycol. In addition, the proportions or numberscan be shown as labels in the regions.

Ifinteractive isTRUE, clicking in a region displays moredetailed information about missing/imputed values on the console. Clickingoutside the borders quits the interactive session.

Value

colormapMiss returns a list with the following components:

• nmiss a numeric vector containing the number of missing/imputedvalues in each region.

• nobs a numeric vector containing the number of observations ineach region.

• pmiss a numeric vector containing the proportion of missingvalues in each region.

• prop a logical indicating whether the proportion ofmissing/imputed values have been used rather than the total amount.

• range the range of the proportion or amount of missing/imputedvalues corresponding to the color range.

• n either a positive integer giving the number of equally spacedcut-off points for a discretized color scheme, orNULL for acontinuous color scheme.

• start the start color of the color scheme.

• end the end color of the color scheme.

• space a character string giving the color space (either"rgb" for RGB colors or"hcl" for HCL colors).

• gamma numeric; the displaygamma value (seecolorspace::hex()).

• fixup a logical indicating whether the colors have beencorrected to valid RGB values (seecolorspace::hex()).

Note

Some of the argument names and positions have changed with versions1.3 and 1.4 due to extended functionality and for more consistency withother plot functions inVIM. For back compatibility, the argumentscex.text andcol.text can still be supplied to...{}and are handled correctly. Nevertheless, they are deprecated and no longerdocumented. Usecex.numbers andcol.numbers instead.

Author(s)

Andreas Alfons, modifications to show imputed values by BerndPrantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

colSequence(),growdotMiss(),mapMiss()

Count number of infinite or missing values

Description

Count the number of infinite or missing values in a vector.

Usage

countInf(x)

Arguments

Value

countInf returns the number of infinite values inx.countNA returns the number of missing values inx.

Author(s)

Andreas Alfons

Examples

data(sleep, package="VIM")countInf(log(sleep$Dream))countNA(sleep$Dream)

Indian Prime Diabetes Data

Description

The datasets consists of several medical predictor variables andone target variable, Outcome. Predictor variables includes the number of pregnanciesthe patient has had, their BMI, insulin level, age, and so on.

Format

A data frame with 768 observations on the following 9 variables.

Pregnancies

Number of times pregnant

Glucose

Plasma glucose concentration a 2 hours in an oral glucose tolerance test

BloodPressure

Diastolic blood pressure (mm Hg)

SkinThickness

Triceps skin fold thickness (mm)

Insulin

2-Hour serum insulin (mu U/ml)

BMI

Body mass index (weight in kg/(height in m)^2)

DiabetesPedigreeFunction

Diabetes pedigree function

Age

Age in years

Outcome

Diabetes (yes or no)

Details

This dataset is originally from the National Institute of Diabetes andDigestive and Kidney Diseases. The objective of the dataset is todiagnostically predict whether or not a patient has diabetes, basedon certain diagnostic measurements included in the dataset.Several constraints were placed on the selection of these instancesfrom a larger database. In particular, all patients here are femalesat least 21 years old of Pima Indian heritage.

Source

https://www.kaggle.com/uciml/pima-indians-diabetes-database/data

References

Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988).Using the ADAP learning algorithm to forecast the onset of diabetes mellitus.In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261–265). IEEE Computer Society Press.

Examples

data(diabetes)aggr(diabetes)

Error performance measures

Description

Various error measures evaluating the quality of imputations

Usage

evaluation(x, y, m, vartypes = "guess")nrmse(x, y, m)pfc(x, y, m)msecov(x, y)msecor(x, y)

Arguments

Details

This function has been mainly written for procuduresthat evaluate imputation or replacement of rounded zeros. The ni parameter can thus, e.g. beused for expressing the number of rounded zeros.

Value

the error measures value

Author(s)

Matthias Templ

References

M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwiseregression imputation using standard and robust methods.Journal ofComputational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.

Examples

data(iris)iris_orig <- iris_imp <- irisiris_imp$Sepal.Length[sample(1:nrow(iris), 10)] <- NAiris_imp$Sepal.Width[sample(1:nrow(iris), 10)] <- NAiris_imp$Species[sample(1:nrow(iris), 10)] <- NAm <- is.na(iris_imp)iris_imp <- kNN(iris_imp, imp_var = FALSE)evaluation(iris_orig, iris_imp, m = m, vartypes = c(rep("numeric", 4), "factor"))msecov(iris_orig[, 1:4], iris_imp[, 1:4])

Food consumption

Description

The relative consumption of certain food items in European and Scandinavian countries.

Format

A data frame with 16 observations on the following 21 variables.

Details

The numbers represent the percentage of the population consuming that food type.

Source

https://openmv.net/info/food-consumption

Examples

data(food)str(food)aggr(food)

Missing value gap statistics

Description

Computes the average missing value gap of a vector.

Usage

gapMiss(x, what = mean)

Arguments

Details

The length of each sequence of missing values (gap) in a vector is calculated and themean gap is reported

Value

The gap statistics

Author(s)

Matthias Templ based on a suggestion and draft from Huang Tian Yuan.

Examples

v <- rnorm(20)v[3] <- NAv[6:9] <- NAv[13:17] <- NAvgapMiss(v)gapMiss(v, what = median)gapMiss(v, what = function(x) mean(x, trim = 0.1))gapMiss(v, what = var)

Computes the extended Gower distance of two data sets

Description

The function gowerD is used by kNN to compute the distances for numerical,factor ordered and semi-continous variables.

Usage

gowerD(  data.x,  data.y = data.x,  weights = rep(1, ncol(data.x)),  numerical = colnames(data.x),  factors = vector(),  orders = vector(),  mixed = vector(),  levOrders = vector(),  mixed.constant = rep(0, length(mixed)),  returnIndex = FALSE,  nMin = 1L,  returnMin = FALSE,  methodStand = "range")

Arguments

Details

returnIndex=FALSE: a numerical matrix n x m with the computed distancesreturnIndex=TRUE: a named list with "ind" containing the requested indices and "mins" the computed distances

Examples

data(sleep)# all variables used as numericalgowerD(sleep)# split in numerical angowerD(sleep, numerical = c("BodyWgt", "BrainWgt", "NonD", "Dream", "Sleep", "Span", "Gest"),  orders = c("Pred","Exp","Danger"), levOrders = c(5,5,5))# as before but only returning the index of the closest observationgowerD(sleep, numerical = c("BodyWgt", "BrainWgt", "NonD", "Dream", "Sleep", "Span", "Gest"),  orders = c("Pred","Exp","Danger"), levOrders = c(5,5,5), returnIndex = TRUE)

Growing dot map with information about missing/imputed values

Description

Map with dots whose sizes correspond to the values in a certain variable.Observations with missing/imputed values in additional variables arehighlighted.

Usage

growdotMiss(  x,  coords,  map,  pos = 1,  delimiter = NULL,  selection = c("any", "all"),  log = FALSE,  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),  border = par("bg"),  alpha = NULL,  scale = NULL,  size = NULL,  exp = c(0, 0.95, 0.05),  col.map = grey(0.5),  legend = TRUE,  legtitle = "Legend",  cex.legtitle = par("cex"),  cex.legtext = par("cex"),  ncircles = 6,  ndigits = 1,  interactive = TRUE,  ...)

Arguments

Details

The smallest dots correspond to the 10\the 99\defining the shape of the exponential function. Missings/imputed missingsin the variable of interest will be drawn as rectangles.

Ifinteractive=TRUE, detailed information for an observation can beprinted on the console by clicking on the corresponding point. Clicking ina region that does not contain any points quits the interactive session.

Note

The function was renamed togrowdotMiss in version 1.3.bubbleMiss is a (deprecated) wrapper forgrowdotMiss for backcompatibility with older versions. However, due to extended functionality,some of the argument positions have changed.

The code is based on (removed from CRAN) bubbleFIN from packageStatDA.

Author(s)

Andreas Alfons, Matthias Templ, Peter Filzmoser, Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

bgmap(),mapMiss(),colormapMiss()

Examples

data(chorizonDL, package = "VIM")data(kola.background, package = "VIM")coo <- chorizonDL[, c("XCOO", "YCOO")]## for missing valuesx <- chorizonDL[, c("Ca","As", "Bi")]growdotMiss(x, coo, kola.background, border = "white")## for imputed valuesx_imp <- kNN(chorizonDL[,c("Ca","As","Bi" )])growdotMiss(x_imp, coo, kola.background, delimiter = "_imp", border = "white")

Histogram with information about missing/imputed values

Description

Histogram with highlighting of missing/imputed values in other variables bysplitting each bin into two parts. Additionally, information aboutmissing/imputed values in the variable of interest is shown on the righthand side.

Usage

histMiss(  x,  delimiter = NULL,  pos = 1,  selection = c("any", "all"),  breaks = "Sturges",  right = TRUE,  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),  border = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  only.miss = TRUE,  miss.labels = axes,  interactive = TRUE,  ...)

Arguments

Details

If more than one variable is supplied, the bins for the variable of interestwill be split according to missingness/number of imputed missings in theadditional variables.

Ifonly.miss=TRUE, the missing/imputed values in the variable ofinterest are visualized by one bar on the right hand side. If additionalvariables are supplied, this bar is again split into two parts according tomissingness/number of imputed missings in the additional variables.

Otherwise, a small barplot consisting of two bars is drawn on the right handside. The first bar corresponds to observed values in the variable ofinterest and the second bar to missing/imputed values. Since these two barsare not on the same scale as the main barplot, a second y-axis is plotted onthe right (ifaxes=TRUE). Each of the two bars are again split intotwo parts according to missingness/number of imputed missings in theadditional variables. Note that this display does not make sense if onlyone variable is supplied, thereforeonly.miss is ignored in thatcase.

Ifinteractive=TRUE, clicking in the left margin of the plot resultsin switching to the previous variable and clicking in the right marginresults in switching to the next variable. Clicking anywhere else on thegraphics device quits the interactive session. When switching to acategorical variable, a barplot is produced rather than a histogram.

Value

a list with the following components:

• breaks the breakpoints.

• counts the number of observations in each cell.

• missings the number of highlighted observations in each cell.

• mids the cell midpoints.

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentsaxisnames andnames.miss can still be supplied to...{} and are handled correctly. Nevertheless, they are deprecatedand no longer documented. Usemiss.labels instead.

Author(s)

Andreas Alfons, Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

spineMiss(),barMiss()

Other plotting functions:aggr(),barMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(tao, package = "VIM")## for missing valuesx <- tao[, c("Air.Temp", "Humidity")]histMiss(x)histMiss(x, only.miss = FALSE)## for imputed valuesx_IMPUTED <- kNN(tao[, c("Air.Temp", "Humidity")])histMiss(x_IMPUTED, delimiter = "_imp")histMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)

Hot-Deck Imputation

Description

Implementation of the popular Sequential, Random (within a domain) hot-deckalgorithm for imputation.

Usage

hotdeck(  data,  variable = NULL,  ord_var = NULL,  domain_var = NULL,  makeNA = NULL,  NAcond = NULL,  impNA = TRUE,  donorcond = NULL,  imp_var = TRUE,  imp_suffix = "imp")

Arguments

Value

the imputed data set.

Note

If the sequential hotdeck does not lead to a suitable,a random donor in the group will be used.

Author(s)

Alexander Kowarik

References

A. Kowarik, M. Templ (2016) Imputation withR package VIM.Journal ofStatistical Software, 74(7), 1-16.

See Also

Other imputation methods:impPCA(),irmi(),kNN(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(sleep)sleepI <- hotdeck(sleep)sleepI2 <- hotdeck(sleep,ord_var="BodyWgt",domain_var="Pred")# Usage of donorcond in a simple examplesleepI3 <- hotdeck(  sleep,  variable = c("NonD", "Dream", "Sleep", "Span", "Gest"),  ord_var = "BodyWgt", domain_var = "Pred",  donorcond = list(">4", "<17", ">1.5", "%between%c(8,13)", ">5"))set.seed(132)nRows <- 1e3# Generate a data set with nRows rows and several variablesx <- data.frame(  x = rnorm(nRows), y = rnorm(nRows),  z = sample(LETTERS, nRows, replace = TRUE),  d1 = sample(LETTERS[1:3], nRows, replace = TRUE),  d2 = sample(LETTERS[1:2], nRows, replace = TRUE),  o1 = rnorm(nRows), o2 = rnorm(nRows), o3 = rnorm(100))origX <- xx[sample(1:nRows,nRows/10), 1] <- NAx[sample(1:nRows,nRows/10), 2] <- NAx[sample(1:nRows,nRows/10), 3] <- NAx[sample(1:nRows,nRows/10), 4] <- NAxImp <- hotdeck(x,ord_var = c("o1", "o2", "o3"), domain_var = "d2")

Iterative EM PCA imputation

Description

Greedy algorithm for EM-PCA including robust methods

Usage

impPCA(  x,  method = "classical",  m = 1,  eps = 0.5,  k = ncol(x) - 1,  maxit = 100,  boot = FALSE,  verbose = TRUE)

Arguments

Value

the imputed data set. Ifboot = FALSE this is a data.frame.Ifboot = TRUE this is a list where each list element contains a data.frame.

Author(s)

Matthias Templ

References

Serneels, Sven and Verdonck, Tim (2008).Principal component analysis for data containing outliers and missing elements.Computational Statistics and Data Analysis, Elsevier, vol. 52(3), pages 1712-1727

See Also

Other imputation methods:hotdeck(),irmi(),kNN(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(Animals, package = "MASS")Animals$brain[19] <- Animals$brain[19] + 0.01Animals <- log(Animals)colnames(Animals) <- c("log(body)", "log(brain)")Animals_na <- Animalsprobs <- abs(Animals$`log(body)`^2)probs <- rep(0.5, nrow(Animals))probs[c(6,16,26)] <- 0set.seed(1234)Animals_na[sample(1:nrow(Animals), 10, prob = probs), "log(brain)"] <- NAw <- is.na(Animals_na$`log(brain)`)impPCA(Animals_na)impPCA(Animals_na, method = "mcd")impPCA(Animals_na, boot = TRUE, m = 10)impPCA(Animals_na, method = "mcd", boot = TRUE)[[1]]plot(`log(brain)` ~ `log(body)`, data = Animals, type = "n", ylab = "", xlab="")mtext(text = "impPCA robust", side = 3)points(Animals$`log(body)`[!w], Animals$`log(brain)`[!w])points(Animals$`log(body)`[w], Animals$`log(brain)`[w], col = "grey", pch = 17)imputed <- impPCA(Animals_na, method = "mcd", boot = TRUE)[[1]]colnames(imputed) <- c("log(body)", "log(brain)")points(imputed$`log(body)`[w], imputed$`log(brain)`[w], col = "red", pch = 20, cex = 1.4)segments(x0 = Animals$`log(body)`[w], x1 = imputed$`log(body)`[w], y0 = Animals$`log(brain)`[w],y1 = imputed$`log(brain)`[w], lty = 2, col = "grey")legend("topleft", legend = c("non-missings", "set to missing", "imputed values"),pch = c(1,17,20), col = c("black","grey","red"), cex = 0.7)mape <- round(100* 1/sum(is.na(Animals_na$`log(brain)`)) * sum(abs((Animals$`log(brain)` -imputed$`log(brain)`) / Animals$`log(brain)`)), 2)s2 <- var(Animals$`log(brain)`)nrmse <- round(sqrt(1/sum(is.na(Animals_na$`log(brain)`)) * sum(abs((Animals$`log(brain)` -imputed$`log(brain)`) / s2))), 2)text(x = 8, y = 1.5, labels = paste("MAPE =", mape))text(x = 8, y = 0.5, labels = paste("NRMSE =", nrmse))

Robust imputation

Description

Multiple imputation using classical and robust methodsaccounting for model and imputation uncertainty.

Usage

imputeRobust(  form,  data,  boot = TRUE,  robustboot = "stratified",  method = "MM",  takeAll = TRUE,  alpha = 0.75,  uncert = "pmm",  family = "Gaussian",  value_back = "all")

Arguments

Details

Complex formulas can be provided for each variable inyour data set.

Value

Imputed data set.

See Also

initialiselmrobgampdist

Examples

## Not run: if(interactive()){ #EXAMPLE1 }## End(Not run)

FUNCTION_TITLE

Description

FUNCTION_DESCRIPTION

Usage

imputeRobustChain(  formulas = vector("list", ncol(data)),  data,  boot = TRUE,  robustboot = TRUE,  method = "lts",  multinom.method = "multinom",  takeAll = TRUE,  eps = 0.5,  maxit = 4,  alpha = 0.5,  uncert = "pmm",  familiy = "Gaussian",  value_back = "matrix",  trace = FALSE)

Arguments

Details

DETAILS

Value

OUTPUT_DESCRIPTION

See Also

initialiseoutCoDa

Examples

## Not run: if(interactive()){ #EXAMPLE1 }## End(Not run)

Initialization of missing values

Description

Rough estimation of missing values in a vector according to its type.

Usage

initialise(x, mixed, method = "kNN", mixed.constant = NULL)

Arguments

Details

Missing values are imputed with the mean for vectors of class"numeric", with the median for vectors of class"integer", andwith the mode for vectors of class"factor". Hence,x shouldbe prepared in the following way: assign class"numeric" to numericvectors, assign class"integer" to ordinal vectors, and assign class"factor" to nominal or binary vectors.

Value

the initialized vector.

Note

The function is used internally by some imputation algorithms.

Author(s)

Matthias Templ, modifications by Andreas Alfons

Iterative robust model-based imputation (IRMI)

Description

In each step of the iteration, one variable is used as a response variableand the remaining variables serve as the regressors.

Usage

irmi(  x,  eps = 5,  maxit = 100,  mixed = NULL,  mixed.constant = NULL,  count = NULL,  step = FALSE,  robust = FALSE,  takeAll = TRUE,  noise = TRUE,  noise.factor = 1,  force = FALSE,  robMethod = "lmrob",  force.mixed = TRUE,  mi = 1,  addMixedFactors = FALSE,  trace = FALSE,  init.method = "kNN",  modelFormulas = NULL,  multinom.method = "multinom",  imp_var = TRUE,  imp_suffix = "imp")

Arguments

Details

The method works sequentially and iterative. The method can deal with amixture of continuous, semi-continuous, ordinal and nominal variablesincluding outliers.

A full description of the method can be found in the mentioned reference.

Value

the imputed data set.

Author(s)

Matthias Templ, Alexander Kowarik

References

M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwiseregression imputation using standard and robust methods.Journal ofComputational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.

A. Kowarik, M. Templ (2016) Imputation withR package VIM.Journal ofStatistical Software, 74(7), 1-16.

See Also

Other imputation methods:hotdeck(),impPCA(),kNN(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(sleep)irmi(sleep)data(testdata)imp_testdata1 <- irmi(testdata$wna, mixed = testdata$mixed)# mixed.constant != 0 (-10)testdata$wna$m1[testdata$wna$m1 == 0] <- -10testdata$wna$m2 <- log(testdata$wna$m2 + 0.001)imp_testdata2 <- irmi(  testdata$wna,  mixed = testdata$mixed,  mixed.constant = c(-10,log(0.001)))imp_testdata2$m2 <- exp(imp_testdata2$m2) - 0.001#example with fixed formulas for the variables with missingform = list(  NonD  = c("BodyWgt", "BrainWgt"),  Dream = c("BodyWgt", "BrainWgt"),  Sleep = c("BrainWgt"           ),  Span  = c("BodyWgt"            ),  Gest  = c("BodyWgt", "BrainWgt"))irmi(sleep, modelFormulas = form, trace = TRUE)# Example with ordered variabletd <- testdata$wnatd$c1 <- as.ordered(td$c1)irmi(td)

k-Nearest Neighbour Imputation

Description

k-Nearest Neighbour Imputation based on a variation of the Gower Distancefor numerical, categorical, ordered and semi-continous variables.

Usage

kNN(  data,  variable = colnames(data),  k = 5,  dist_var = colnames(data),  weights = NULL,  numFun = median,  catFun = maxCat,  makeNA = NULL,  NAcond = NULL,  impNA = TRUE,  donorcond = NULL,  mixed = vector(),  mixed.constant = NULL,  trace = FALSE,  imp_var = TRUE,  imp_suffix = "imp",  addRF = FALSE,  onlyRF = FALSE,  addRandom = FALSE,  useImputedDist = TRUE,  weightDist = FALSE,  methodStand = "range",  ordFun = medianSamp)

Arguments

Value

the imputed data set.

Author(s)

Alexander Kowarik, Statistik Austria

References

A. Kowarik, M. Templ (2016) Imputation withR package VIM.Journal ofStatistical Software, 74(7), 1-16.

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(sleep)kNN(sleep)library(laeken)kNN(sleep, numFun = weightedMean, weightDist=TRUE)

Background map for the Kola project data

Description

Coordinates of the Kola background map.

Source

Kola Project (1993-1998)

References

Reimann, C., Filzmoser, P., Garrett, R.G. and Dutter, R. (2008)Statistical Data Analysis Explained: Applied Environmental Statisticswith R. Wiley, 2008.

Examples

data(kola.background, package = "VIM")bgmap(kola.background)

Map with information about missing/imputed values

Description

Map of observed and missing/imputed values.

Usage

mapMiss(  x,  coords,  map,  delimiter = NULL,  selection = c("any", "all"),  col = c("skyblue", "red", "orange"),  alpha = NULL,  pch = c(19, 15),  col.map = grey(0.5),  legend = TRUE,  interactive = TRUE,  ...)

Arguments

Details

Ifinteractive=TRUE, detailed information for an observation can beprinted on the console by clicking on the corresponding point. Clicking ina region that does not contain any points quits the interactive session.

Author(s)

Matthias Templ, Andreas Alfons, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

bgmap(),bubbleMiss(),colormapMiss()

Examples

data(chorizonDL, package = "VIM")data(kola.background, package = "VIM")coo <- chorizonDL[, c("XCOO", "YCOO")]## for missing valuesx <- chorizonDL[, c("As", "Bi")]mapMiss(x, coo, kola.background)## for imputed valuesx_imp <- kNN(chorizonDL[, c("As", "Bi")])mapMiss(x_imp, coo, kola.background, delimiter = "_imp")

Marginplot Matrix

Description

Create a scatterplot matrix with information about missing/imputed values inthe plot margins of each panel.

Usage

marginmatrix(  x,  delimiter = NULL,  col = c("skyblue", "red", "red4", "orange", "orange4"),  alpha = NULL,  ...)

Arguments

Details

marginmatrix usespairsVIM() with a panel function basedonmarginplot().

The graphical parameteroma will be set unless supplied as anargument.

Author(s)

Andreas Alfons, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

marginplot(),pairsVIM(),scattmatrixMiss()

Other plotting functions:aggr(),barMiss(),histMiss(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package = "VIM")## for missing valuesx <- sleep[, 1:5]x[,c(1,2,4)] <- log10(x[,c(1,2,4)])marginmatrix(x)## for imputed valuesx_imp <- kNN(sleep[, 1:5])x_imp[,c(1,2,4)] <- log10(x_imp[,c(1,2,4)])marginmatrix(x_imp, delimiter = "_imp")

Scatterplot with additional information in the margins

Description

In addition to a standard scatterplot, information about missing/imputedvalues is shown in the plot margins. Furthermore, imputed values arehighlighted in the scatterplot.

Usage

marginplot(  x,  delimiter = NULL,  col = c("skyblue", "red", "red4", "orange", "orange4"),  alpha = NULL,  pch = c(1, 16),  cex = par("cex"),  numbers = TRUE,  cex.numbers = par("cex"),  zeros = FALSE,  xlim = NULL,  ylim = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  ann = par("ann"),  axes = TRUE,  frame.plot = axes,  ...)

Arguments

Details

Boxplots for available and missing/imputed data, as well as univariatescatterplots for missing/imputed values in one variable are shown in theplot margins.

Imputed values in either of the variables are highlighted in thescatterplot.

Furthermore, the frequencies of the missing/imputed values can be displayedby a number (lower left of the plot). The number in the lower left corner isthe number of observations that are missing/imputed in both variables.

Note

Some of the argument names and positions have changed with versions1.3 and 1.4 due to extended functionality and for more consistency withother plot functions inVIM. For back compatibility, the argumentcex.text can still be supplied to...{} and is handledcorrectly. Nevertheless, it is deprecated and no longer documented. Usecex.numbers instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

scattMiss()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(tao, package = "VIM")data(chorizonDL, package = "VIM")## for missing valuesmarginplot(tao[,c("Air.Temp", "Humidity")])marginplot(log10(chorizonDL[,c("CaO", "Bi")]))## for imputed valuesmarginplot(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")marginplot(kNN(log10(chorizonDL[,c("CaO", "Bi")])), delimiter = "_imp")

Fast matching/imputation based on categorical variable

Description

Suitable donors are searched based on matching of the categorical variables.The variables are dropped in reversed order, so that the last element of'match_var' is dropped first and the first element of the vector is dropped last.

Usage

matchImpute(  data,  variable = colnames(data)[!colnames(data) %in% match_var],  match_var,  imp_var = TRUE,  imp_suffix = "imp")

Arguments

Details

The method works by sampling values from the suitable donors.

Value

the imputed data set.

Author(s)

Johannes Gussenbauer, Alexander Kowarik

See Also

hotdeck()

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),medianSamp(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(sleep,package="VIM")imp_data <- matchImpute(sleep,variable=c("NonD","Dream","Sleep","Span","Gest"),  match_var=c("Exp","Danger"))data(testdata,package="VIM")imp_testdata1 <- matchImpute(testdata$wna,match_var=c("c1","c2","b1","b2"))dt <- data.table::data.table(testdata$wna)imp_testdata2 <- matchImpute(dt,match_var=c("c1","c2","b1","b2"))

Matrix plot

Description

Create a matrix plot, in which all cells of a data matrix are visualized byrectangles. Available data is coded according to a continuous color scheme,while missing/imputed data is visualized by a clearly distinguishable color.

Usage

matrixplot(  x,  delimiter = NULL,  sortby = NULL,  col = c("red", "orange"),  fixup = TRUE,  xlim = NULL,  ylim = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  labels = axes,  xpd = NULL,  interactive = TRUE,  ...)

Arguments

Details

In amatrix plot, all cells of a data matrix are visualized byrectangles. Available data is coded according to a continuous color scheme.To compute the colors via interpolation, the variables are first scaled tothe interval between 0 and 1. Missing/imputed values can then bevisualized by a clearly distinguishable color. It is thereby possible to usecolors in theHCL orRGB color space. A simple way ofvisualizing the magnitude of the available data is to apply a greyscale,which has the advantage that missing/imputed values can easily bedistinguished by using a color such as red/orange. Note that-InfandInf are always assigned the begin and end color, respectively, ofthe continuous color scheme.

Additionally, the observations can be sorted by the magnitude of a selectedvariable. Ifinteractive isTRUE, clicking in a columnredraws the plot with observations sorted by the corresponding variable.Clicking anywhere outside the plot region quits the interactive session.

Note

This is a much more powerful extension to the functionimagmissin the former CRAN packagedprep.

iimagMiss is deprecated and may be omitted in future versions ofVIM. Usematrixplot instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package = "VIM")## for missing valuesx <- sleep[, -(8:10)]x[,c(1,2,4,6,7)] <- log10(x[,c(1,2,4,6,7)])matrixplot(x, sortby = "BrainWgt")## for imputed valuesx_imp <- kNN(sleep[, -(8:10)])x_imp[,c(1,2,4,6,7)] <- log10(x_imp[,c(1,2,4,6,7)])matrixplot(x_imp, delimiter = "_imp", sortby = "BrainWgt")

Aggregation function for a factor variable

Description

The function maxCat chooses the levelwith the most occurrences and random if the maximum is not unique.

Usage

maxCat(x, weights = NULL)

Arguments

Aggregation function for a ordinal variable

Description

The function medianSamp chooses the level as the median or randomly betweentwo levels.

Usage

medianSamp(x, weights = NULL)

Arguments

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),matchImpute(),rangerImpute(),regressionImp(),sampleCat(),xgboostImpute()

Mosaic plot with information about missing/imputed values

Description

Create a mosaic plot with information about missing/imputed values.

Usage

mosaicMiss(  x,  delimiter = NULL,  highlight = NULL,  selection = c("any", "all"),  plotvars = NULL,  col = c("skyblue", "red", "orange"),  labels = NULL,  miss.labels = TRUE,  ...)

Arguments

Details

Mosaic plots are graphical representations of multi-way contingency tables.The frequencies of the different cells are visualized by area-proportionalrectangles (tiles). Additional tiles are be used to display the frequenciesof missing/imputed values. Furthermore, missing/imputed values in a certainvariable or combination of variables can be highlighted in order to exploretheir structure.

Value

An object of class"structable" is returned invisibly.

Note

This function uses the highly flexiblestrucplot framework ofpackagevcd.

Author(s)

Andreas Alfons, modifications by Bernd Prantner

References

Meyer, D., Zeileis, A. and Hornik, K. (2006) Thestrucplot framework: Visualizing multi-way contingency tables withvcd.Journal of Statistical Software,17 (3), 1–48.

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete data usingvisualization tools.Journal of Advances in Data Analysis andClassification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

spineMiss(),vcd::mosaic()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package = "VIM")## for missing valuesmosaicMiss(sleep, highlight = 4,     plotvars = 8:10, miss.labels = FALSE)## for imputed valuesmosaicMiss(kNN(sleep), highlight = 4,     plotvars = 8:10, delimiter = "_imp", miss.labels = FALSE)

Scatterplot Matrices

Description

Create a scatterplot matrix.

Usage

pairsVIM(  x,  ...,  delimiter = NULL,  main = NULL,  sub = NULL,  panel = points,  lower = panel,  upper = panel,  diagonal = NULL,  labels = TRUE,  pos.labels = NULL,  cex.labels = NULL,  font.labels = par("font"),  layout = c("matrix", "graph"),  gap = 1)

Arguments

Details

This function is the workhorse formarginmatrix() andscattmatrixMiss().

The graphical parameteroma will be set unless supplied as anargument.

A panel function should not attempt to start a new plot, since thecoordinate system for each panel is set up bypairsVIM.

Note

The code is based ongraphics::pairs(). Starting withversion 1.4, infinite values are no longer removed before passing thex andy vectors to the panel functions.

Author(s)

Andreas Alfons, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

marginmatrix(),scattmatrixMiss()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(sleep, package = "VIM")x <- sleep[, -(8:10)]x[,c(1,2,4,6,7)] <- log10(x[,c(1,2,4,6,7)])pairsVIM(x)

Parallel coordinate plot with information about missing/imputed values

Description

Parallel coordinate plot with adjustments for missing/imputed values.Missing values in the plotted variables may be represented by a point abovethe corresponding coordinate axis to prevent disconnected lines. Inaddition, observations with missing/imputed values in selected variables maybe highlighted.

Usage

parcoordMiss(  x,  delimiter = NULL,  highlight = NULL,  selection = c("any", "all"),  plotvars = NULL,  plotNA = TRUE,  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),  alpha = NULL,  lty = par("lty"),  xlim = NULL,  ylim = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  labels = TRUE,  xpd = NULL,  interactive = TRUE,  ...)

Arguments

Details

In parallel coordinate plots, the variables are represented by parallelaxes. Each observation of the scaled data is shown as a line. Observationswith missing/imputed values in selected variables may thereby behighlighted. However, plotting variables with missing values results indisconnected lines, making it impossible to trace the respectiveobservations across the graph. As a remedy, missing values may berepresented by a point above the corresponding coordinate axis, which isseparated from the main plot by a small gap and a horizontal line, asdetermined byplotNA. Connected lines can then be drawn for allobservations. Nevertheless, a caveat of this display is that it may drawattention away from the main relationships between the variables.

Ifinteractive isTRUE, it is possible switch between thisdisplay and the standard display without the separate level for missingvalues by clicking in the top margin of the plot. In addition, the variablesto be used for highlighting can be selected interactively. Observationswith missing/imputed values in any or in all of the selected variables arehighlighted (as determined byselection). A variable can be added tothe selection by clicking on a coordinate axis. If a variable is alreadyselected, clicking on its coordinate axis removes it from the selection.Clicking anywhere outside the plot region (except the top margin, ifmissing/imputed values exist) quits the interactive session.

Note

Some of the argument names and positions have changed with versions1.3 and 1.4 due to extended functionality and for more consistency withother plot functions inVIM. For back compatibility, the argumentscolcomb andxaxlabels can still be supplied to...{}and are handled correctly. Nevertheless, they are deprecated and no longerdocumented. Usehighlight andlabels instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

Wegman, E. J. (1990) Hyperdimensional data analysis usingparallel coordinates.Journal of the American Statistical Association85 (411), 664–675.

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete data usingvisualization tools.Journal of Advances in Data Analysis andClassification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

pbox()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(chorizonDL, package = "VIM")## for missing valuesparcoordMiss(chorizonDL[,c(15,101:110)],     plotvars=2:11, interactive = FALSE)legend("top", col = c("skyblue", "red"), lwd = c(1,1),     legend = c("observed in Bi", "missing in Bi"))## for imputed valuesparcoordMiss(kNN(chorizonDL[,c(15,101:110)]), delimiter = "_imp" ,    plotvars=2:11, interactive = FALSE)legend("top", col = c("skyblue", "orange"), lwd = c(1,1),     legend = c("observed in Bi", "imputed in Bi"))

Parallel boxplots with information about missing/imputed values

Description

Boxplot of one variable of interest plus information about missing/imputedvalues in other variables.

Usage

pbox(  x,  delimiter = NULL,  pos = 1,  selection = c("none", "any", "all"),  col = c("skyblue", "red", "red4", "orange", "orange4"),  numbers = TRUE,  cex.numbers = par("cex"),  xlim = NULL,  ylim = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  frame.plot = axes,  labels = axes,  interactive = TRUE,  ...)

Arguments

Details

This plot consists of several boxplots. First, a standard boxplot of thevariable of interest is produced. Second, boxplots grouped by observed andmissing/imputed values according toselection are produced for thevariable of interest.

Additionally, the frequencies of the missing/imputed values can berepresented by numbers. If so, the first line corresponds to the observedvalues of the variable of interest and their distribution in the differentgroups, the second line to the missing/imputed values.

Ifinteractive=TRUE, clicking in the left margin of the plot resultsin switching to the previous variable and clicking in the right marginresults in switching to the next variable. Clicking anywhere else on thegraphics device quits the interactive session.

Value

a list as returned bygraphics::boxplot().

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentsnamesandcex.text can still be supplied to...{} and are handledcorrectly. Nevertheless, they are deprecated and no longer documented. Uselabels andcex.numbers instead.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

parcoordMiss()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),scattJitt(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(chorizonDL, package = "VIM")## for missing valuespbox(log(chorizonDL[, c(4,5,8,10,11,16:17,19,25,29,37,38,40)]))## for imputed valuespbox(kNN(log(chorizonDL[, c(4,8,10,11,17,19,25,29,37,38,40)])),     delimiter = "_imp")

Transformation and standardization

Description

This function is used by theVIM GUI for transformation andstandardization of the data.

Usage

prepare(  x,  scaling = c("none", "classical", "MCD", "robust", "onestep"),  transformation = c("none", "minus", "reciprocal", "logarithm", "exponential", "boxcox",    "clr", "ilr", "alr"),  alpha = NULL,  powers = NULL,  start = 0,  alrVar)

Arguments

Details

Transformation:

"none": no transformation is used.

"logarithm": compute the the logarithm (to the base 10).

"boxcox": apply a Box-Cox transformation. Powers may be specified orcalculated with the functioncar::powerTransform().

Standardization:

"none": no standardization is used.

"classical": apply az-Transformation on each variable byusing functionscale().

"robust": apply a robustifiedz-Transformation by using medianand MAD.

Value

Transformed and standardized data.

Author(s)

Matthias Templ, modifications by Andreas Alfons

See Also

scale(),car::powerTransform()

Examples

data(sleep, package = "VIM")x <- sleep[, c("BodyWgt", "BrainWgt")]prepare(x, scaling = "robust", transformation = "logarithm")

Pulp lignin content

Description

Pulp quality by lignin content remaining

Format

A data frame with 301 observations on the following 23 variables.

Details

Pulp quality is measured by the lignin content remaining in the pulp:the Kappa number. This data set is used to understand which variablesin the process influence the Kappa number, and if it can be predictedaccurately enough for an inferential sensor application.Variables with a number at the end have been lagged by thatnumber of hours to line up the data.

Source

https://openmv.net/info/kamyr-digester

References

K. Walkush and R.R. Gustafson. Application of feedforward neural networks and partial leastsquares regression for modelling Kappa number in a continuous Kamyr digester",Pulp and Paper Canada, 95, 1994, p T7-T13.

Examples

data(pulplignin)str(pulplignin)aggr(pulplignin)

Random Forest Imputation

Description

Impute missing values based on a random forest model usingranger::ranger()

Usage

rangerImpute(  formula,  data,  imp_var = TRUE,  imp_suffix = "imp",  ...,  verbose = FALSE,  median = FALSE)

Arguments

Value

the imputed data set.

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),matchImpute(),medianSamp(),regressionImp(),sampleCat(),xgboostImpute()

Examples

data(sleep)rangerImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)

Regression Imputation

Description

Impute missing values based on a regression model.

Usage

regressionImp(  formula,  data,  family = "AUTO",  robust = FALSE,  imp_var = TRUE,  imp_suffix = "imp",  mod_cat = FALSE)

Arguments

Details

lm() is used for family "normal" andglm() for all other families.Ifrobust = TRUE,lmrob() is used for family "normal"andglmrob() for all other families.

Value

the imputed data set.

Author(s)

Alexander Kowarik

References

A. Kowarik, M. Templ (2016) Imputation withR package VIM.Journal ofStatistical Software, 74(7), 1-16.

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),matchImpute(),medianSamp(),rangerImpute(),sampleCat(),xgboostImpute()

Examples

data(sleep)sleepImp1 <- regressionImp(Dream+NonD~BodyWgt+BrainWgt,data=sleep)sleepImp2 <- regressionImp(Sleep+Gest+Span+Dream+NonD~BodyWgt+BrainWgt,data=sleep)data(testdata)imp_testdata1 <- regressionImp(b1+b2~x1+x2,data=testdata$wna)imp_testdata3 <- regressionImp(x1~x2,data=testdata$wna,robust=TRUE)

Rug representation of missing/imputed values

Description

Add a rug representation of missing/imputed values in only one of thevariables to scatterplots.

Usage

rugNA(  x,  y,  ticksize = NULL,  side = 1,  col = "red",  alpha = NULL,  miss = NULL,  lwd = 0.5,  ...)

Arguments

Details

Ifside is 1 or 3, the rug representation consists of valuesavailable inx but missing/imputed iny. Else ifsideis 2 or 4, it consists of values available iny but missing/imputedinx.

Author(s)

Andreas Alfons, modifications by Bernd Prantner

Examples

data(tao, package = "VIM")## for missing valuesx <- tao[, "Air.Temp"]y <- tao[, "Humidity"]plot(x, y)rugNA(x, y, side = 1)rugNA(x, y, side = 2)## for imputed valuesx_imp <- kNN(tao[, c("Air.Temp","Humidity")])x <- x_imp[, "Air.Temp"]y <- x_imp[, "Humidity"]miss <- x_imp[, c("Air.Temp_imp","Humidity_imp")]plot(x, y)rugNA(x, y, side = 1, col = "orange", miss = miss)rugNA(x, y, side = 2, col = "orange", miss = miss)

Random aggregation function for a factor variable

Description

The function sampleCat samples with probabilites corresponding to theoccurrence of the level in the NNs.

Usage

sampleCat(x, weights = NULL)

Arguments

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),xgboostImpute()

Bivariate jitter plot

Description

Create a bivariate jitter plot.

Usage

scattJitt(  x,  delimiter = NULL,  col = c("skyblue", "red", "red4", "orange", "orange4"),  alpha = NULL,  cex = par("cex"),  col.line = "lightgrey",  lty = "dashed",  lwd = par("lwd"),  numbers = TRUE,  cex.numbers = par("cex"),  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  frame.plot = axes,  labels = c("observed", "missing", "imputed"),  ...)

Arguments

Details

The amount of observed and missing/imputed values is visualized by jitteredpoints. Thereby the plot region is divided into up to four regionsaccording to the existence of missing/imputed values in one or bothvariables. In addition, the amount of observed and missing/imputed valuescan be represented by a number.

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentcex.text can still be supplied to...{} and is handledcorrectly. Nevertheless, it is deprecated and no longer documented. Usecex.numbers instead.

Author(s)

Matthias Templ, modifications by Andreas Alfons and Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattMiss(),scattmatrixMiss(),spineMiss()

Examples

data(tao, package = "VIM")## for missing valuesscattJitt(tao[, c("Air.Temp", "Humidity")])## for imputed valuesscattJitt(kNN(tao[, c("Air.Temp", "Humidity")]), delimiter = "_imp")

Scatterplot with information about missing/imputed values

Description

In addition to a standard scatterplot, lines are plotted for the missingvalues in one variable. If there are imputed values, they will behighlighted.

Usage

scattMiss(  x,  delimiter = NULL,  side = 1,  col = c("skyblue", "red", "orange", "lightgrey"),  alpha = NULL,  lty = c("dashed", "dotted"),  lwd = par("lwd"),  quantiles = c(0.5, 0.975),  inEllipse = FALSE,  zeros = FALSE,  xlim = NULL,  ylim = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  interactive = TRUE,  ...)

Arguments

Details

Information about missing values in one variable is included as vertical orhorizontal lines, as determined by theside argument. The lines arethereby drawn at the observed x- or y-value. In case of imputed values, theywill additionally be highlighted in the scatterplot. Supplementary,percentage coverage ellipses can be drawn to give a clue about the shape ofthe bivariate data distribution.

IfinteractiveisTRUE, clicking in the bottom margin redrawsthe plot with information about missing/imputed values in the first variableand clicking in the left margin redraws the plot with information aboutmissing/imputed values in the second variable. Clicking anywhere else inthe plot quits the interactive session.

Note

The argumentzeros has been introduced in version 1.4. As aresult, some of the argument positions have changed.

Author(s)

Andreas Alfons, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

marginplot()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattmatrixMiss(),spineMiss()

Examples

data(tao, package = "VIM")## for missing valuesscattMiss(tao[,c("Air.Temp", "Humidity")])## for imputed valuesscattMiss(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")

Scatterplot matrix with information about missing/imputed values

Description

Scatterplot matrix in which observations with missing/imputed values incertain variables are highlighted.

Usage

scattmatrixMiss(  x,  delimiter = NULL,  highlight = NULL,  selection = c("any", "all"),  plotvars = NULL,  col = c("skyblue", "red", "orange"),  alpha = NULL,  pch = c(1, 3),  lty = par("lty"),  diagonal = c("density", "none"),  interactive = TRUE,  ...)

Arguments

Details

scattmatrixMiss usespairsVIM() with a panel functionthat allows highlighting of missing/imputed values.

Ifinteractive=TRUE, the variables to be used for highlighting can beselected interactively. Observations with missing/imputed values in any orin all of the selected variables are highlighted (as determined byselection). A variable can be added to the selection by clicking ina diagonal panel. If a variable is already selected, clicking on thecorresponding diagonal panel removes it from the selection. Clickinganywhere else quits the interactive session.

The graphical parameteroma will be set unless supplied as anargument.

TKRscattmatrixMiss behaves likescattmatrixMiss, but usestkrplot to embed the plot in aTcl/Tk window.This is useful if the number of variables is large, because scrollbars allowto move from one part of the plot to another.

Note

Some of the argument names and positions have changed with version 1.3due to a re-implementation and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentcolcomb can still be supplied to...{} and is handledcorrectly. Nevertheless, it is deprecated and no longer documented. Usehighlight instead. The argumentssmooth,reg.line andlegend.plot are no longer used and ignored if supplied.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

pairsVIM(),marginmatrix()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),spineMiss()

Examples

data(sleep, package = "VIM")## for missing valuesx <- sleep[, 1:5]x[,c(1,2,4)] <- log10(x[,c(1,2,4)])scattmatrixMiss(x, highlight = "Dream")## for imputed valuesx_imp <- kNN(sleep[, 1:5])x_imp[,c(1,2,4)] <- log10(x_imp[,c(1,2,4)])scattmatrixMiss(x_imp, delimiter = "_imp", highlight = "Dream")

Mammal sleep data

Description

Sleep data with missing values.

Format

A data frame with 62 observations on the following 10 variables.

BodyWgt

a numeric vector

BrainWgt

a numeric vector

NonD

a numericvector

Dream

a numeric vector

Sleep

anumeric vector

Span

a numeric vector

Gest

anumeric vector

Pred

a numeric vector

Exp

anumeric vector

Danger

a numeric vector

Source

Allison, T. and Chichetti, D. (1976) Sleep in mammals: ecologicaland constitutional correlates.Science194 (4266), 732–734.

The data set was imported fromGGobi.

Examples

data(sleep, package = "VIM")summary(sleep)aggr(sleep)

Spineplot with information about missing/imputed values

Description

Spineplot or spinogram with highlighting of missing/imputed values in othervariables by splitting each cell into two parts. Additionally, informationabout missing/imputed values in the variable of interest is shown on theright hand side.

Usage

spineMiss(  x,  delimiter = NULL,  pos = 1,  selection = c("any", "all"),  breaks = "Sturges",  right = TRUE,  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),  border = NULL,  main = NULL,  sub = NULL,  xlab = NULL,  ylab = NULL,  axes = TRUE,  labels = axes,  only.miss = TRUE,  miss.labels = axes,  interactive = TRUE,  ...)

Arguments

Details

A spineplot is created if the variable of interest is categorial and aspinogram if it is numerical. The horizontal axis is scaled according torelative frequencies of the categories/classes. If more than one variableis supplied, the cells are split according to missingness/number of imputedvalues in the additional variables. Thus the proportion of highlightedobservations in each category/class is displayed on the vertical axis. Sincethe height of each cell corresponds to the proportion of highlightedobservations, it is now possible to compare the proportions ofmissing/imputed values among the different categories/classes.

Ifonly.miss=TRUE, the missing/imputed values in the variable ofinterest are also visualized by a cell in the spine plot or spinogram. Ifadditional variables are supplied, this cell is again split into two partsaccording to missingness/number if imputed values in the additionalvariables.

Otherwise, a small spineplot that visualizes missing/imputed values in thevariable of interest is drawn on the right hand side. The first cellcorresponds to observed values and the second cell to missing/imputedvalues. Each of the two cells is again split into two parts according tomissingness/number of imputed values in the additional variables. Note thatthis display does not make sense if only one variable is supplied, thereforeonly.miss is ignored in that case.

Ifinteractive=TRUE, clicking in the left margin of the plot resultsin switching to the previous variable and clicking in the right marginresults in switching to the next variable. Clicking anywhere else on thegraphics device quits the interactive session.

Value

a table containing the frequencies corresponding to the cells.

Note

Some of the argument names and positions have changed with version 1.3due to extended functionality and for more consistency with other plotfunctions inVIM. For back compatibility, the argumentsxaxlabels andmissaxlabels can still be supplied to...{} and are handled correctly. Nevertheless, they are deprecatedand no longer documented. Uselabels andmiss.labels instead.

The code is based on the functiongraphics::spineplot() by AchimZeileis.

Author(s)

Andreas Alfons, Matthias Templ, modifications by Bernd Prantner

References

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incompletedata using visualization tools.Journal of Advances in Data Analysisand Classification, Online first. DOI: 10.1007/s11634-011-0102-y.

See Also

histMiss(),barMiss(),mosaicMiss()

Other plotting functions:aggr(),barMiss(),histMiss(),marginmatrix(),marginplot(),matrixplot(),mosaicMiss(),pairsVIM(),parcoordMiss(),pbox(),scattJitt(),scattMiss(),scattmatrixMiss()

Examples

data(tao, package = "VIM")data(sleep, package = "VIM")## for missing valuesspineMiss(tao[, c("Air.Temp", "Humidity")])spineMiss(sleep[, c("Exp", "Sleep")])## for imputed valuesspineMiss(kNN(tao[, c("Air.Temp", "Humidity")]), delimiter = "_imp")spineMiss(kNN(sleep[, c("Exp", "Sleep")]), delimiter = "_imp")

create table with highlighted missings/imputations

Description

Create areactable table that highlights missing values and imputed valueswith the same colors ashistMiss()

Usage

tableMiss(x, delimiter = "_imp")

Arguments

Examples

data(tao)x_IMPUTED <- kNN(tao[, c("Air.Temp", "Humidity")])tableMiss(x_IMPUTED[105:114, ])x_IMPUTED[106, 2] <- NAx_IMPUTED[105, 1] <- NAx_IMPUTED[107, "Humidity_imp"] <- TRUEtableMiss(x_IMPUTED[105:114, ])

Tropical Atmosphere Ocean (TAO) project data

Description

A small subsample of the Tropical Atmosphere Ocean (TAO) project data,derived from theGGOBI project.

Format

A data frame with 736 observations on the following 8 variables.

Year

a numeric vector

Latitude

anumeric vector

Longitude

a numeric vector

Sea.Surface.Temp

a numeric vector

Air.Temp

anumeric vector

Humidity

a numeric vector

UWind

zonal wind, i.e. latitude-parallel wind

VWind

meridional wind, i.e. longitude-parallel wind

Details

All cases recorded for five locations and two time periods.

Source

http://www.pmel.noaa.gov/tao/

Examples

data(tao, package = "VIM")summary(tao)aggr(tao)

Simulated data set for testing purpose

Description

2 numeric, 2 binary, 2 nominal and 2 mixed (semi-continous) variables

Format

The format is: List of 4

• ⁠$wna⁠ : adata.frame with 500 obs. of 8 variables:

  • x1: numeric 10.87 9.53 7.83 8.53 8.67 ...

  • x2: numeric 10.9 9.32 7.68 8.2 8.41 ... ..

  • c1: Factor w/ 4 levels "a","b","c","d": 3 2 2 1 2 2 1 3 3 2 ...

  • c2: Factor w/ 4 levels "a","b","c","d": 2 3 2 2 2 2 2 4 2 2 ...

  • b1: Factor w/ 2 levels "0","1": 2 2 1 2 1 2 1 2 1 1 ...

  • b2: Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 2 2 2 ...

  • m1: numeric 0 8.29 9.08 0 0 ...

  • m2: numeric 10.66 9.39 7.8 8.11 7.33 ...

• ⁠$wona⁠ : a 'data.frame“ with 500 obs. of 8 variables:

  • x1: numeric 10.87 9.53 7.83 8.53 8.67 ...

  • x2: numeric 10.9 9.32 7.68 8.2 8.41 ...

  • c1: Factor w/ 4 levels "a","b","c","d": 3 2 2 1 2 2 1 3 3 2 ...

  • c2: Factor w/ 4 levels "a","b","c","d": 2 3 2 2 2 2 2 4 2 2 ...

  • b1: Factor w/ 2 levels "0","1": 2 2 1 2 1 2 1 2 1 1 ...

  • b2: Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 2 2 2 ...

  • m1: numeric 0 8.29 9.08 0 0 ...

  • m2: numeric 10.66 9.39 7.8 8.11 7.33 ...

• ⁠$mixed⁠:c("m1", "m2")

• ⁠$outlierInd⁠: 'NULL“

Examples

data(testdata)

Simulated toy data set for examples

Description

A 2-dimensional data set with additional information.

Format

data frame with 100 observations and 12 variables. The first twovariables represent the fully observed data.

Examples

data(toydataMiss)

Wine tasting and price

Description

Wine reviews from France, Switzerland, Austria and Germany.

Format

A data frame with 9627 observations on the following 9 variables.

country

country of origin

points

the number of points WineEnthusiast rated the wine on a scale of 1-100(though they say they only post reviews for wines that score >=80)

price

the cost for a bottle of the wine

province

the province or state that the wine is from

taster_name

name of the person who tasted and reviewed the wine

taster_twitter_handle

Twitter handle for the person who tasted ane reviewed the wine

variety

the type of grapes used to make the wine (ie pinot noir)

winery

the winery that made the wine

variety_main

broader category as variety

Details

The data was scraped from WineEnthusiast during the week of Nov 22th, 2017.The code for the scraper can be found at https://github.com/zackthoutt/wine-deep-learningThis data set is slightly modified, i.e. only four countries are selected andbroader categories on the variety have been added.

Source

https://www.kaggle.com/zynicide/wine-reviews

Examples

data(wine)str(wine)aggr(wine)

Xgboost Imputation

Description

Impute missing values based on a random forest model usingxgboost::xgboost()

Usage

xgboostImpute(  formula,  data,  imp_var = TRUE,  imp_suffix = "imp",  verbose = FALSE,  nrounds = 100,  objective = NULL,  ...)

Arguments

Value

the imputed data set.

See Also

Other imputation methods:hotdeck(),impPCA(),irmi(),kNN(),matchImpute(),medianSamp(),rangerImpute(),regressionImp(),sampleCat()

Examples

data(sleep)xgboostImpute(Dream~BodyWgt+BrainWgt,data=sleep)xgboostImpute(Dream+NonD~BodyWgt+BrainWgt,data=sleep)xgboostImpute(Dream+NonD+Gest~BodyWgt+BrainWgt,data=sleep)sleepx <- sleepsleepx$Pred <- as.factor(LETTERS[sleepx$Pred])sleepx$Pred[1] <- NAxgboostImpute(Pred~BodyWgt+BrainWgt,data=sleepx)