`R/scattmatrixMiss.R`

`scattmatrixMiss.Rd`

Scatterplot matrix in which observations with missing/imputed values in certain variables are highlighted.

- 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.- col
a vector of length three giving the colors to be used in the plot. The second/third color will be used for highlighting missing/imputed values.

- 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.- pch
a vector of length two giving the plot characters. The second plot character will be used for the highlighted observations.

- lty
a vector of length two giving the line types for the density plots in the diagonal panels (if

`diagonal="density"`

). The second line type is used for the highlighted observations. If a single value is supplied, it is used for both non-highlighted and highlighted observations.- diagonal
a character string specifying the plot to be drawn in the diagonal panels. Possible values are

`"density"`

(density plots for non-highlighted and highlighted observations) and`"none"`

.- interactive
a logical indicating whether the variables to be used for highlighting can be selected interactively (see ‘Details’).

- ...
for

`scattmatrixMiss`

, further arguments and graphical parameters to be passed to`pairsVIM()`

.`par("oma")`

will be set appropriately unless supplied (see`graphics::par()`

). For`TKRscattmatrixMiss`

, further arguments to be passed to`scattmatrixMiss`

.

`scattmatrixMiss`

uses `pairsVIM()`

with a panel function
that allows highlighting of missing/imputed values.

If `interactive=TRUE`

, 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 in
a diagonal panel. If a variable is already selected, clicking on the
corresponding diagonal panel removes it from the selection. Clicking
anywhere else quits the interactive session.

The graphical parameter `oma`

will be set unless supplied as an
argument.

`TKRscattmatrixMiss`

behaves like `scattmatrixMiss`

, 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 version 1.3
due to a re-implementation and for more consistency with other plot
functions in `VIM`

. For back compatibility, the argument
`colcomb`

can still be supplied to `...{}`

and is handled
correctly. Nevertheless, it is deprecated and no longer documented. Use
`highlight`

instead. The arguments `smooth`

, `reg.line`

and
`legend.plot`

are no longer used and ignored if supplied.

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()`

,
`parcoordMiss()`

,
`pbox()`

,
`scattJitt()`

,
`scattMiss()`

,
`spineMiss()`

```
data(sleep, package = "VIM")
## for missing values
x <- sleep[, 1:5]
x[,c(1,2,4)] <- log10(x[,c(1,2,4)])
scattmatrixMiss(x, highlight = "Dream")
#> Warning: variable 'Dream' contains infinite values
## for imputed values
x_imp <- kNN(sleep[, 1:5])
x_imp[,c(1,2,4)] <- log10(x_imp[,c(1,2,4)])
scattmatrixMiss(x_imp, delimiter = "_imp", highlight = "Dream")
#> Warning: variable 'Dream' contains infinite values
```