VIM: growdotMiss – R documentation

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growdotMiss

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 are highlighted.

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

`x`	a vector, matrix or `data.frame`.
`coords`	a matrix or `data.frame` with two columns giving the spatial coordinates of the observations.
`map`	a background map to be passed to `bgmap()`.
`pos`	a numeric value giving the index of the variable determining the dot sizes.
`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`).
`selection`	the selection method for highlighting missing/imputed values in multiple additional variables. Possible values are `"any"` (highlighting of missing/imputed values in any of the additional variables) and `"all"` (highlighting of missing/imputed values in all of the additional variables).
`log`	a logical indicating whether the variable given by `pos` should be log-transformed.
`col`	a vector of length six giving the colors to be used in the plot. If only one color is supplied, it is used for the borders of non-highlighted dots and the surface area of highlighted dots. Else if two colors are supplied, they are recycled.
`border`	a vector of length four giving the colors to be used for the borders of the growing dots. Use `NA` to omit borders.
`alpha`	a numeric value between 0 and 1 giving the level of transparency of the colors, or `NULL`. This can be used to prevent overplotting.
`scale`	scaling factor of the map.
`size`	a vector of length two giving the sizes for the smallest and largest dots.
`exp`	a vector of length three giving the factors that define the shape of the exponential function (see ‘Details’).
`col.map`	the color to be used for the background map.
`legend`	a logical indicating whether a legend should be plotted.
`legtitle`	the title for the legend.
`cex.legtitle`	the character expansion factor to be used for the title of the legend.
`cex.legtext`	the character expansion factor to be used in the legend.
`ncircles`	the number of circles displayed in the legend.
`ndigits`	the number of digits displayed in the legend. Note that \ this is just a suggestion (see `format()`).
`interactive`	a logical indicating whether information about certain observations can be displayed interactively (see ‘Details’).
`...`	for `growdotMiss`, further arguments and graphical parameters to be passed to `bgmap()`. For `bubbleMiss`, the arguments to be passed to `growdotMiss`.

Details

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

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.

Note

The function was renamed to growdotMiss in version 1.3. bubbleMiss is a (deprecated) wrapper for growdotMiss for back compatibility 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 package StatDA.

Author(s)

Andreas Alfons, Matthias Templ, Peter Filzmoser, Bernd Prantner

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.

Examples

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

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

VIM

Visualization and Imputation of Missing Values

v6.1.0

GPL (>= 2)

Authors

Matthias Templ [aut, cre], Alexander Kowarik [aut] (<https://orcid.org/0000-0001-8598-4130>), Andreas Alfons [aut], Gregor de Cillia [aut], Bernd Prantner [ctb], Wolfgang Rannetbauer [aut]

Initial release