VIM (version 3.0.2)

histMiss: Histogram with information about missing/imputed values

Description

Histogram with highlighting of missing/imputed values in other variables by splitting each bin into two parts. Additionally, information about missing/imputed values in the variable of interest is shown on the right hand side.

Usage

histMiss(x, delimiter = NULL, pos = 1, selection = c("any","all"),
    breaks = "Sturges", right = TRUE, col = c("skyblue","red",
    "skyblue4","red4","orange","orange4"), border = NULL,
    main = NULL, sub = NULL, xlab = NULL, ylab = NULL, axes = TRUE,
    only.miss = TRUE, miss.labels = axes, interactive = TRUE, ...)

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 cor

pos

a numeric value giving the index of the variable of interest. Additional variables in x are used for highlighting.

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

breaks

either a character string naming an algorithm to compute the breakpoints (see hist), or a numeric value giving the number of cells.

right

logical; if TRUE, the histogram cells are right-closed (left-open) intervals.

col

a vector of length six giving the colors to be used. If only one color is supplied, the bars are transparent and the supplied color is used for highlighting missing/imputed values. Else if two colors are supplied, they are recycl

border

the color to be used for the border of the cells. Use border=NA to omit borders.

main, sub

main and sub title.

xlab, ylab

axis labels.

axes

a logical indicating whether axes should be drawn on the plot.

only.miss

logical; if TRUE, the missing/imputed values in the first variable are visualized by a single bar. Otherwise, a small barplot is drawn on the right hand side (see Details).

miss.labels

either a logical indicating whether label(s) should be plotted below the bar(s) on the right hand side, or a character string or vector giving the label(s) (see Details).

interactive

a logical indicating whether the variables can be switched interactively (see Details).

...

further graphical parameters to be passed to title and axis.

Value

• a list with the following components:
• breaks
  the breakpoints.
• counts
  the number of observations in each cell.
• missings
  the number of highlighted observations in each cell.
• mids
  the cell midpoints.

Details

If more than one variable is supplied, the bins for the variable of interest will be split according to missingness/number of imputed missings in the additional variables. If only.miss=TRUE, the missing/imputed values in the variable of interest are visualized by one bar on the right hand side. If additional variables are supplied, this bar is again split into two parts according to missingness/number of imputed missings in the additional variables. Otherwise, a small barplot consisting of two bars is drawn on the right hand side. The first bar corresponds to observed values in the variable of interest and the second bar to missing/imputed values. Since these two bars are not on the same scale as the main barplot, a second y-axis is plotted on the right (if axes=TRUE). Each of the two bars are again split into two parts according to missingness/number of imputed missings in the additional variables. Note that this display does not make sense if only one variable is supplied, therefore only.miss is ignored in that case. If interactive=TRUE, clicking in the left margin of the plot results in switching to the previous variable and clicking in the right margin results in switching to the next variable. Clicking anywhere else on the graphics device quits the interactive session. When switching to a categorical variable, a barplot is produced rather than a histogram.

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

spineMiss, barMiss

Examples

data(tao, package = "VIM")
## for missing values
x <- tao[, c("Air.Temp", "Humidity")]
histMiss(x)
histMiss(x, only.miss = FALSE)

## for imputed values
x_IMPUTED <- kNN(tao[, c("Air.Temp", "Humidity")])
histMiss(x_IMPUTED, delimiter = "_imp")
histMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)