TKRscattmatrixMiss(x, delimiter = NULL, highlight = NULL, selection = c("any", "all"), plotvars = NULL, col = c("skyblue", "red", "orange"), alpha = NULL, hscale = NULL, vscale = NULL, 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.NULL
. This can be used to prevent
overplotting.par
).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.
A. Kowarik, M. Templ (2016) Imputation with R package VIM. Journal of Statistical Software, 74(7), 1-16
pairsVIM
, marginmatrix
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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