Scatterplot matrix in which observations with missing/imputed values in certain variables are highlighted.
scattmatrixMiss(
x,
delimiter = NULL,
highlight = NULL,
selection = c("any", "all"),
plotvars = NULL,
col = c("skyblue", "red", "orange"),
alpha = NULL,
pch = c(1, 3),
lty = par("lty"),
diagonal = c("density", "none"),
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 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.
a numeric value between 0 and 1 giving the level of
transparency of the colors, or NULL
. This can be used to prevent
overplotting.
a vector of length two giving the plot characters. The second plot character will be used for the highlighted observations.
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.
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"
.
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
.
Andreas Alfons, Matthias Templ, modifications by Bernd Prantner
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.
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.
pairsVIM()
, marginmatrix()
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")
## 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")
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