VIMGUI (version 0.9.0)

TKRaggr: 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

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 corresponding imputation-index for any imputed variable (a logical-vector indicating which values of the variable have been imputed). If such imputation-indices are found, they are used for highlighting and the colors are adjusted according to the given colors for imputed variables (see col).

...

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. par("oma") will be set appropriately unless supplied (see par).

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.
• combinationsa 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.
• tabcombthe 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)