`R/parcoordMiss.R`

`parcoordMiss.Rd`

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,
...
)
```

- x
a matrix or

`data.frame`

.- delimiter
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`

).- highlight
a vector giving the variables to be used for highlighting. If

`NULL`

(the default), all variables are used for highlighting.- selection
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).- plotvars
a vector giving the variables to be plotted. If

`NULL`

(the default), all variables are plotted.- 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

`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).- alpha
a numeric value between 0 and 1 giving the level of transparency of the colors, or

`NULL`

. This can be used to prevent overplotting.- lty
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).- xlim, ylim
axis limits.

- main, sub
main and sub title.

- xlab, ylab
axis labels.

- labels
either a logical indicating whether labels should be plotted below each coordinate axis, or a character vector giving the labels.

- xpd
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.- interactive
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`

.

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.

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.

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.

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"))
```