TKRparcoordMiss(x, delimiter = NULL, highlight = NULL, selection = c("any", "all"), plotvars = NULL, plotNA = TRUE, col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"), alpha = NULL, hscale = NULL, vscale = 1, 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
).NULL
(the default), all variables are used for highlighting."any"
(highlighting of missing/imputed values in any of the highlight
variables) and "all"
(highlighting of missing/imputed values in
all of the highlight variables).NULL
(the default), all variables are plotted.plotNA
is TRUE
, a vector of length six giving
the colors to be used for observations with different combinations of
observed and missing/imputed values in the plot variables and highlight
variables (vectors of length one or two are recycled). Otherwise, a vector
of length two giving the colors for non-highlighted and highlighted
observations (if a single color is supplied, it is used for both).NULL
. This can be used to prevent
overplotting.par
).parcoordMiss
, further graphical parameters to be
passed down (see par
). For TKRparcoordMiss
,
further arguments to be passed to parcoordMiss
.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.
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.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16
pbox
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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