marginplot(x, delimiter = NULL, col = c("skyblue", "red", "red4", "orange",
"orange4"), alpha = NULL, pch = c(1, 16), cex = par("cex"),
numbers = TRUE, cex.numbers = par("cex"), zeros = FALSE, xlim = NULL,
ylim = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = NULL,
ann = par("ann"), axes = TRUE, frame.plot = axes, ...)matrix or data.frame with two columns.x needs to have
colnames). If given, it is used to determine the corresponding
impuNULL. This can be used to prevent
overplotting.TRUE, only the non-zero observations are used for drawing the
respective boxplot. If a single logical is smain,
sub, xlab, ylab) should be displayed."xaxt" or "yaxt" to suppress
only one of the axes.par).cex.numbersImputed values in either of the variables are highlighted in the scatterplot.
Furthermore, the frequencies of the missing/imputed values can be displayed by a number (lower left of the plot). The number in the lower left corner is the number of observations that are missing/imputed in both variables.
scattMissdata(tao, package = "VIM")
data(chorizonDL, package = "VIM")
## for missing values
marginplot(tao[,c("Air.Temp", "Humidity")])
marginplot(log10(chorizonDL[,c("CaO", "Bi")]))
## for imputed values
marginplot(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")
marginplot(kNN(log10(chorizonDL[,c("CaO", "Bi")])), delimiter = "_imp")Run the code above in your browser using DataLab