In addition to a standard scatterplot, information about missing/imputed values is shown in the plot margins. Furthermore, imputed values are highlighted in the scatterplot.
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,
...
)a matrix or data.frame with two columns.
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 of length five giving the colors to be used in the plot. The first color is used for the scatterplot and the boxplots for the available data. In case of missing values, the second color is taken for the univariate scatterplots and boxplots for missing values in one variable and the third for the frequency of missing/imputed values in both variables (see ‘Details’). Otherwise, in case of imputed values, the fourth color is used for the highlighting, the frequency, the univariate scatterplot and the boxplots of mputed values in the first variable and the fifth color for the same applied to the second variable. A black color is used for the highlighting and the frequency of imputed values in both variables instead. If only one color is supplied, it is used for the bivariate and univariate scatterplots and the boxplots for missing/imputed values in one variable, whereas the boxplots for the available data are transparent. Else if two colors are supplied, the second one is recycled.
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 symbols to be used for the scatterplot and the univariate scatterplots. If a single plot character is supplied, it is used for the scatterplot and the default value will be used for the univariate scatterplots (see ‘Details’).
the character expansion factor to be used for the bivariate and univariate scatterplots.
a logical indicating whether the frequencies of missing/imputed values should be displayed in the lower left of the plot (see ‘Details’).
the character expansion factor to be used for the frequencies of the missing/imputed values.
a logical vector of length two indicating whether the variables
are semi-continuous, i.e., contain a considerable amount of zeros. If
TRUE, only the non-zero observations are used for drawing the
respective boxplot. If a single logical is supplied, it is recycled.
axis limits.
main and sub title.
axis labels.
a logical indicating whether plot annotation (main,
sub, xlab, ylab) should be displayed.
a logical indicating whether both axes should be drawn on the
plot. Use graphical parameter "xaxt" or "yaxt" to suppress
only one of the axes.
a logical indicating whether a box should be drawn around the plot.
further graphical parameters to be passed down (see
graphics::par()).
Boxplots for available and missing/imputed data, as well as univariate scatterplots for missing/imputed values in one variable are shown in the plot margins.
Imputed 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.
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.
Other plotting functions:
aggr(),
barMiss(),
histMiss(),
marginmatrix(),
matrixplot(),
mosaicMiss(),
pairsVIM(),
parcoordMiss(),
pbox(),
scattJitt(),
scattMiss(),
scattmatrixMiss(),
spineMiss()
# NOT RUN {
data(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")
# }
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