scattMiss(x, delimiter = NULL, side = 1, col = c("skyblue", "red",
"orange", "lightgrey"), alpha = NULL, lty = c("dashed", "dotted"),
lwd = par("lwd"), quantiles = c(0.5, 0.975), inEllipse = FALSE,
zeros = FALSE, xlim = NULL, ylim = NULL, main = NULL, sub = NULL,
xlab = NULL, ylab = NULL, interactive = TRUE, ...)matrix or data.frame with two columns.x needs to have
colnames). If given, it is used to determine the corresponding
iside=1, a rug representation and vertical lines are
plotted for the missing/imputed values in the second variable; if
side=2, a rug representation and horizontal lines for the
missing/imputed values in the first variable.NULL. This can be used to prevent
overplotting.NULL to
suppress plotting ellipses (see quantiles is NULL or if there are imputed values.TRUE, only the non-zero observations are used for computing the
tolerance ellipses. If a single logicalside argument can
be changed interactively (see par).side argument. The lines are
thereby drawn at the observed x- or y-value. In case of imputed values, they
will additionally be highlighted in the scatterplot. Supplementary,
percentage coverage ellipses can be drawn to give a clue about the shape of
the bivariate data distribution.
If interactiveis TRUE, clicking in the bottom margin redraws
the plot with information about missing/imputed values in the first variable
and clicking in the left margin redraws the plot with information about
missing/imputed values in the second variable. Clicking anywhere else in
the plot quits the interactive session.marginplotdata(tao, package = "VIM")
## for missing values
scattMiss(tao[,c("Air.Temp", "Humidity")])
## for imputed values
scattMiss(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")Run the code above in your browser using DataLab