VIM (version 3.0.2)

mosaicMiss: 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

x

a 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 i

highlight

a vector giving the variables to be used for highlighting. If NULL (the default), all variables are used for highlighting.

selection

the selection method for highlighting missing/imputed values in multiple highlight variables. Possible values are "any" (highlighting of missing/imputed values in any of the highlight variables) and "all"

plotvars

a vector giving the categorical variables to be plotted. If NULL (the default), all variables are plotted.

col

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

labels

a list of arguments for the labeling function labeling_border.

miss.labels

either a logical indicating whether labels should be plotted for observed and missing/imputed (highlighted) data, or a character vector giving the labels.

...

additional arguments to be passed to mosaic.

Value

• An object of class "structable" is returned invisibly.

Details

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

References

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

spineMiss, mosaic

Examples

data(sleep, package = "VIM")
## for missing values
mosaicMiss(sleep, highlight = 4, 
    plotvars = 8:10, miss.labels = FALSE)

## for imputed values
mosaicMiss(kNN(sleep), highlight = 4, 
    plotvars = 8:10, delimiter = "_imp", miss.labels = FALSE)