barMiss(x, delimiter = NULL, pos = 1, selection = c("any", "all"), col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"), border = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, axes = TRUE, labels = axes, only.miss = TRUE, miss.labels = axes, interactive = TRUE, ...)
data.frame
.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
).x
are used for highlighting."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).border=NA
to omit borders.TRUE
, the missing/imputed values in the
variable of interest are visualized by a single bar. Otherwise, a small
barplot is drawn on the right hand side (see Details).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
continuous variable, a histogram is plotted rather than a barplot.
spineMiss
, histMiss
data(sleep, package = "VIM")
## for missing values
x <- sleep[, c("Exp", "Sleep")]
barMiss(x)
barMiss(x, only.miss = FALSE)
## for imputed values
x_IMPUTED <- kNN(sleep[, c("Exp", "Sleep")])
barMiss(x_IMPUTED, delimiter = "_imp")
barMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)
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