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.
parcoordMiss(
x,
delimiter = NULL,
highlight = NULL,
selection = c("any", "all"),
plotvars = NULL,
plotNA = TRUE,
col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),
alpha = NULL,
lty = par("lty"),
xlim = NULL,
ylim = NULL,
main = NULL,
sub = NULL,
xlab = NULL,
ylab = NULL,
labels = TRUE,
xpd = NULL,
interactive = TRUE,
...
)a matrix or data.frame.
a character-vector to distinguish between variables and
imputation-indices for imputed variables (therefore, 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).
a vector giving the variables to be used for highlighting.
If NULL (the default), all variables are used for highlighting.
the selection method for highlighting missing/imputed
values in multiple highlight variables. Possible values are "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).
a vector giving the variables to be plotted. If NULL
(the default), all variables are plotted.
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.
if 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).
a numeric value between 0 and 1 giving the level of
transparency of the colors, or NULL. This can be used to prevent
overplotting.
if plotNA is TRUE, a vector of length four giving
the line types 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 line types for non-highlighted and highlighted
observations (if a single line type is supplied, it is used for both).
axis limits.
main and sub title.
axis labels.
either a logical indicating whether labels should be plotted below each coordinate axis, or a character vector giving the labels.
a logical indicating whether the lines should be allowed to go
outside the plot region. If NULL, it defaults to TRUE unless
axis limits are specified.
a logical indicating whether interactive features should be enabled (see ‘Details’).
for parcoordMiss, further graphical parameters to be
passed down (see graphics::par()). For TKRparcoordMiss,
further arguments to be passed to parcoordMiss.
Andreas Alfons, Matthias Templ, modifications by Bernd Prantner
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.
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/s11634-011-0102-y.
pbox()
Other plotting functions:
aggr(),
barMiss(),
histMiss(),
marginmatrix(),
marginplot(),
matrixplot(),
mosaicMiss(),
pairsVIM(),
pbox(),
scattJitt(),
scattMiss(),
scattmatrixMiss(),
spineMiss()
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"))
Run the code above in your browser using DataLab