Description Usage Arguments Details Note Author(s) References See Also Examples
View source: R/TKRparcoordMiss.R
Parallel coordinate plot with adjustments for missing/imputed values. Missing values in the plotted variables may be represented by a point above the corresponding coordinate axis to prevent disconnected lines. In addition, observations with missing/imputed values in selected variables may be highlighted.
1 2 3 4 
x 
a matrix or 
delimiter 
a charactervector to distinguish between variables and
imputationindices for imputed variables (therefore, 
highlight 
a vector giving the variables to be used for highlighting.
If 
selection 
the selection method for highlighting missing/imputed
values in multiple highlight variables. Possible values are 
plotvars 
a vector giving the variables to be plotted. If 
plotNA 
a logical indicating whether missing values in the plot variables should be represented by a point above the corresponding coordinate axis to prevent disconnected lines. 
col 
if 
alpha 
a numeric value between 0 and 1 giving the level of
transparency of the colors, or 
hscale 
horizontal scale factor for plot to be embedded in a Tcl/Tk window (see ‘Details’). The default value depends on the number of variables. 
vscale 
vertical scale factor for the plot to be embedded in a Tcl/Tk window (see ‘Details’). 
TKRpar 
a list of graphical parameters to be set for the plot to be
embedded in a Tcl/Tk window (see ‘Details’ and

... 
for 
In parallel coordinate plots, the variables are represented by parallel
axes. Each observation of the scaled data is shown as a line. Observations
with missing/imputed values in selected variables may thereby be
highlighted. However, plotting variables with missing values results in
disconnected lines, making it impossible to trace the respective
observations across the graph. As a remedy, missing values may be
represented by a point above the corresponding coordinate axis, which is
separated from the main plot by a small gap and a horizontal line, as
determined by plotNA
. Connected lines can then be drawn for all
observations. Nevertheless, a caveat of this display is that it may draw
attention away from the main relationships between the variables.
If interactive
is TRUE
, it is possible switch between this
display and the standard display without the separate level for missing
values by clicking in the top margin of the plot. In addition, the variables
to be used for highlighting can be selected interactively. Observations
with missing/imputed values in any or in all of the selected variables are
highlighted (as determined by selection
). A variable can be added to
the selection by clicking on a coordinate axis. If a variable is already
selected, clicking on its coordinate axis removes it from the selection.
Clicking anywhere outside the plot region (except the top margin, if
missing/imputed values exist) quits the interactive session.
TKRparcoordMiss
behaves like parcoordMiss
, but uses
tkrplot
to embed the plot in a Tcl/Tk window.
This is useful if the number of variables is large, because scrollbars allow
to move from one part of the plot to another.
Some of the argument names and positions have changed with versions
1.3 and 1.4 due to extended functionality and for more consistency with
other plot functions in VIM
. For back compatibility, the arguments
colcomb
and xaxlabels
can still be supplied to ...{}
and are handled correctly. Nevertheless, they are deprecated and no longer
documented. Use highlight
and labels
instead.
Andreas Alfons, Matthias Templ, modifications by Bernd Prantner
Wegman, E. J. (1990) Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association 85 (411), 664–675.
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/s116340110102y.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 116
1 2 3 4 5 6 7 8 9 10 11 12  data(chorizonDL, package = "VIM")
## for missing values
parcoordMiss(chorizonDL[,c(15,101:110)],
plotvars=2:11, interactive = FALSE)
legend("top", col = c("skyblue", "red"), lwd = c(1,1),
legend = c("observed in Bi", "missing in Bi"))
## for imputed values
parcoordMiss(kNN(chorizonDL[,c(15,101:110)]), delimiter = "_imp" ,
plotvars=2:11, interactive = FALSE)
legend("top", col = c("skyblue", "orange"), lwd = c(1,1),
legend = c("observed in Bi", "imputed in Bi"))

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