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.
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,
...
)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.
a vector, matrix or data.frame.
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).
a numeric value giving the index of the variable of interest.
Additional variables in x are used for highlighting.
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).
either a character string naming an algorithm to compute the
breakpoints (see hist()), or a numeric value giving the number
of cells.
logical; if TRUE, the histogram cells are right-closed
(left-open) intervals.
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 recycled.
the color to be used for the border of the cells. Use
border=NA to omit borders.
main and sub title.
axis labels.
a logical indicating whether axes should be drawn on the plot.
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’).
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’).
a logical indicating whether the variables can be switched interactively (see ‘Details’).
further graphical parameters to be passed to
graphics::title() and graphics::axis().
Andreas Alfons, Bernd Prantner
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.
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.
spineMiss(), barMiss()
Other plotting functions:
aggr(),
barMiss(),
marginmatrix(),
marginplot(),
matrixplot(),
mosaicMiss(),
pairsVIM(),
parcoordMiss(),
pbox(),
scattJitt(),
scattMiss(),
scattmatrixMiss(),
spineMiss()
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)
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