VIM (version 3.0.2)

aggr: Aggregations for missing/imputed values

Description

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

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, ...)
TKRaggr(x, ..., delimiter = NULL, hscale = NULL, vscale = NULL,
    TKRpar = list())

Arguments

x

a vector, matrix or data.frame.

delimiter

a character-vector to distinguish between variables and imputation-indices for imputed variables (therefore, x needs to have colnames). If given, it is used to determine the correspo

plot

a logical indicating whether the results should be plotted (the default is TRUE).

col

a vector of length three giving the colors to be used for observed, missing and imputed data. If only one color is supplied, it is used for missing and imputed data and observed data is transparent. If only two colors are supplied, the firs

bars

a logical indicating whether a small barplot for the frequencies of the different combinations should be drawn.

numbers

a logical indicating whether the proportion or frequencies of the different combinations should be represented by numbers.

prop

a logical indicating whether the proportion of missing/imputed values and combinations should be used rather than the total amount.

combined

a logical indicating whether the two plots should be combined. If FALSE, a separate barplot on the left hand side shows the amount of missing/imputed values in each variable. If TRUE, a small version of this barp

varheight

a logical indicating whether the cell heights are given by the frequencies of occurrence of the corresponding combinations.

only.miss

a logical indicating whether the small barplot for the frequencies of the combinations should only be drawn for combinations including missing/imputed values (if bars is TRUE). This is useful if most observations

border

the color to be used for the border of the bars and rectangles. Use border=NA to omit borders.

sortVars

a logical indicating whether the variables should be sorted by the number of missing/imputed values.

sortCombs

a logical indicating whether the combinations should be sorted by the frequency of occurrence.

ylabs

if combined is TRUE, a character string giving the y-axis label of the combined plot, otherwise a character vector of length two giving the y-axis labels for the two plots.

axes

a logical indicating whether axes should be drawn.

labels

either a logical indicating whether labels should be plotted on the x-axis, or a character vector giving the labels.

cex.lab

the character expansion factor to be used for the axis labels.

cex.axis

the character expansion factor to be used for the axis annotation.

cex.numbers

the character expansion factor to be used for the proportion or frequencies of the different combinations

gap

if combined is FALSE, a numeric value giving the distance between the two plots in margin lines.

...

for aggr and TKRaggr, further arguments and graphical parameters to be passed to plot.aggr. For plot.aggr, further graphical parameters to be passed down.

hscale

horizontal scale factor for plot to be embedded in a Tcl/Tk window (see Details). The default value depends on the number of variables.

vscale

vertical scale factor for the plot to be embedded in a Tcl/Tk window (see Details). The default value depends on the number of combinations.

TKRpar

a list of graphical parameters to be set for the plot to be embedded in a Tcl/Tk window (see Details and par).

Value

• for aggr, a list of class "aggr" containing the following components:
• x
  the data used.
• combinations
  a character vector representing the combinations of variables.
• count
  the frequencies of these combinations.
• percent
  the percentage of these combinations.
• missings
  a data.frame containing the amount of missing/imputed values in each variable.
• tabcomb
  the indicator matrix for the combinations of variables.

Details

Often it is of interest how many missing/imputed values are contained in each variable. Even more interesting, there may be certain combinations of variables with a high number of missing/imputed values. If combined is FALSE, two separate plots are drawn for the missing/imputed values in each variable and the combinations of missing/imputed and non-missing values. The barplot on the left hand side shows the amount of missing/imputed values in each variable. In the aggregation plot on the right hand side, all existing combinations of missing/imputed and non-missing values in the observations are visualized. Available, missing and imputed data are color coded as given by col. Additionally, there are two possibilities to represent the frequencies of occurrence of the different combinations. The first option is to visualize the proportions or frequencies by a small bar plot and/or numbers. The second option is to let the cell heights be given by the frequencies of the corresponding combinations. Furthermore, variables may be sorted by the number of missing/imputed values and combinations by the frequency of occurrence to give more power to finding the structure of missing/imputed values. If combined is TRUE, a small version of the barplot showing the amount of missing/imputed values in each variable is drawn on top of the aggregation plot. The graphical parameter oma will be set unless supplied as an argument. TKRaggr behaves like plot.aggr, but uses tkrplot to embed the plot in a Tcl/Tk window. This is useful if the number of variables and/or combinations is large, because scrollbars allow to move from one part of the plot to another.

References

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

See Also

print.aggr, summary.aggr

Examples

data(sleep, package="VIM")
## for missing values
a <- aggr(sleep)
a
summary(a)

## for imputed values
sleep_IMPUTED <- kNN(sleep)
a <- aggr(sleep_IMPUTED, delimiter="_imp")
a
summary(a)