histMiss {VIM} | R Documentation |
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, ...)
x |
a vector, matrix or |
delimiter |
a character-vector to distinguish between variables
and imputation-indices for imputed variables (therefore, |
pos |
a numeric value giving the index of the variable of
interest. Additional variables in |
selection |
the selection method for highlighting missing/imputed values
in multiple additional variables. Possible values are |
breaks |
either a character string naming an algorithm to compute
the breakpoints (see |
right |
logical; if |
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 recycled. |
border |
the color to be used for the border of the cells.
Use |
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 |
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
|
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.
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. |
Some of the argument names and positions have changed with version 1.3
due to extended functionality and for more consistency with other plot
functions in VIM
. For back compatibility, the arguments
axisnames
and names.miss
can still be supplied to
...
and are handled correctly. Nevertheless, they are
deprecated and no longer documented. Use miss.labels
instead.
Andreas Alfons, Bernd Prantner
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.
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)