TKRaggr(x, ..., delimiter = NULL, hscale = NULL, vscale = NULL, TKRpar = list())
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
).par
).aggr
, a list of class "aggr"
containing the
following components:
data.frame
containing the amount of
missing/imputed values in each variable.
If combined
is FALSE
, two separate plots are drawn for the
missing/imputed values in each variable and the combinations of
missing/imputed and non-missing values. The barplot on the left hand side
shows the amount of missing/imputed values in each variable. In the
aggregation plot on the right hand side, all existing combinations of
missing/imputed and non-missing values in the observations are visualized.
Available, missing and imputed data are color coded as given by col
.
Additionally, there are two possibilities to represent the frequencies of
occurrence of the different combinations. The first option is to visualize
the proportions or frequencies by a small bar plot and/or numbers. The
second option is to let the cell heights be given by the frequencies of the
corresponding combinations. Furthermore, variables may be sorted by the
number of missing/imputed values and combinations by the frequency of
occurrence to give more power to finding the structure of missing/imputed
values.
If combined
is TRUE
, a small version of the barplot showing
the amount of missing/imputed values in each variable is drawn on top of the
aggregation plot.
The graphical parameter oma
will be set unless supplied as an
argument.
TKRaggr
behaves like plot.aggr
, but uses
tkrplot
to embed the plot in a Tcl/Tk window.
This is useful if the number of variables and/or combinations is large,
because scrollbars allow to move from one part of the plot to another.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16
print.aggr
, summary.aggr
data(sleep, package="VIM")
## for missing values
a <- aggr(sleep)
a
summary(a)
## for imputed values
sleep_IMPUTED <- kNN(sleep)
a <- aggr(sleep_IMPUTED, delimiter="_imp")
a
summary(a)
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