Map of observed and missing/imputed values.

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



a vector, matrix or data.frame.


a data.frame or matrix with two columns giving the spatial coordinates of the observations.


a background map to be passed to bgmap().


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).


the selection method for displaying missing/imputed values in the map. Possible values are "any" (display missing/imputed values in any variable) and "all" (display missing/imputed values in all variables).


a vector of length three giving the colors to be used for observed, missing and imputed values. If a single color is supplied, it is used for all values.


a numeric value between 0 and 1 giving the level of transparency of the colors, or NULL. This can be used to prevent overplotting.


a vector of length two giving the plot characters to be used for observed and missing/imputed values. If a single plot character is supplied, it will be used for both.

the color to be used for the background map.


a logical indicating whether a legend should be plotted.


a logical indicating whether information about selected observations can be displayed interactively (see ‘Details’).


further graphical parameters to be passed to bgmap() and graphics::points().


If interactive=TRUE, detailed information for an observation can be printed on the console by clicking on the corresponding point. Clicking in a region that does not contain any points quits the interactive session.


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.


Matthias Templ, Andreas Alfons, modifications by Bernd Prantner


data(chorizonDL, package = "VIM")
data(kola.background, package = "VIM")
coo <- chorizonDL[, c("XCOO", "YCOO")]
## for missing values
x <- chorizonDL[, c("As", "Bi")]
mapMiss(x, coo, kola.background)

#> Click on a point to get more information.
#> To regain use of the VIM GUI and the R console, click in a region that does not contain any points.

## for imputed values
x_imp <- kNN(chorizonDL[, c("As", "Bi")])
mapMiss(x_imp, coo, kola.background, delimiter = "_imp")

#> Click on a point to get more information.
#> To regain use of the VIM GUI and the R console, click in a region that does not contain any points.