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
corside=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
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 siside 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