Calculate or plot the amount of missing/imputed values in each variable and the amount of missing/imputed values in certain combinations of variables.
Print method for objects of class "aggr".
Summary method for objects of class "aggr".
Print method for objects of class "summary.aggr".
aggr(x, delimiter = NULL, plot = TRUE, ...)# S3 method for aggr
plot(
x,
col = c("skyblue", "red", "orange"),
bars = TRUE,
numbers = FALSE,
prop = TRUE,
combined = FALSE,
varheight = FALSE,
only.miss = FALSE,
border = par("fg"),
sortVars = FALSE,
sortCombs = TRUE,
ylabs = NULL,
axes = TRUE,
labels = axes,
cex.lab = 1.2,
cex.axis = par("cex"),
cex.numbers = par("cex"),
gap = 4,
...
)
# S3 method for aggr
print(x, ..., digits = NULL)
# S3 method for aggr
summary(object, ...)
# S3 method for summary.aggr
print(x, ...)
for aggr, a list of class "aggr" containing the
following components:
x the data used.
combinations a character vector representing the combinations of variables.
count the frequencies of these combinations.
percent the percentage of these combinations.
missings a data.frame containing the amount of
missing/imputed values in each variable.
tabcomb the indicator matrix for the combinations of variables.
a list of class "summary.aggr" containing the following
components:
missings a data.frame containing the amount of missing or
imputed values in each variable.
combinations a data.frame containing a character vector
representing the combinations of variables along with their frequencies and
percentages.
an object of class "summary.aggr".
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 logical indicating whether the results should be plotted (the
default is TRUE).
Further arguments, currently ignored.
a vector of length three giving the colors to be used for observed, missing and imputed data. If only one color is supplied, it is used for missing and imputed data and observed data is transparent. If only two colors are supplied, the first one is used for observed data and the second color is used for missing and imputed data.
a logical indicating whether a small barplot for the frequencies of the different combinations should be drawn.
a logical indicating whether the proportion or frequencies of the different combinations should be represented by numbers.
a logical indicating whether the proportion of missing/imputed values and combinations should be used rather than the total amount.
a logical indicating whether the two plots should be
combined. If FALSE, a separate barplot on the left hand side shows
the amount of missing/imputed values in each variable. If TRUE, a
small version of this barplot is drawn on top of the plot for the
combinations of missing/imputed and non-missing values. See
“Details” for more information.
a logical indicating whether the cell heights are given by the frequencies of occurrence of the corresponding combinations.
a logical indicating whether the small barplot for the
frequencies of the combinations should only be drawn for combinations
including missing/imputed values (if bars is TRUE). This is
useful if most observations are complete, in which case the corresponding
bar would dominate the barplot such that the remaining bars are too
compressed. The proportion or frequency of complete observations (as
determined by prop) is then represented by a number instead of a bar.
the color to be used for the border of the bars and
rectangles. Use border=NA to omit borders.
a logical indicating whether the variables should be sorted by the number of missing/imputed values.
a logical indicating whether the combinations should be sorted by the frequency of occurrence.
if combined is TRUE, a character string giving
the y-axis label of the combined plot, otherwise a character vector of
length two giving the y-axis labels for the two plots.
a logical indicating whether axes should be drawn.
either a logical indicating whether labels should be plotted on the x-axis, or a character vector giving the labels.
the character expansion factor to be used for the axis labels.
the character expansion factor to be used for the axis annotation.
the character expansion factor to be used for the proportion or frequencies of the different combinations
if combined is FALSE, a numeric value giving the
distance between the two plots in margin lines.
the minimum number of significant digits to be used (see
print.default()).
an object of class "aggr".
Andreas Alfons, Matthias Templ, modifications for displaying imputed values by Bernd Prantner
Matthias Templ, modifications by Andreas Alfons and Bernd Prantner
Matthias Templ, modifications by Andreas Alfons
Andreas Alfons, modifications by Bernd Prantner
Often it is of interest how many missing/imputed values are contained in each variable. Even more interesting, there may be certain combinations of variables with a high number of missing/imputed values.
If combined is FALSE, two separate plots are drawn for the
missing/imputed values in each variable and the combinations of
missing/imputed and non-missing values. The barplot on the left hand side
shows the amount of missing/imputed values in each variable. In the
aggregation plot on the right hand side, all existing combinations of
missing/imputed and non-missing values in the observations are visualized.
Available, missing and imputed data are color coded as given by col.
Additionally, there are two possibilities to represent the frequencies of
occurrence of the different combinations. The first option is to visualize
the proportions or frequencies by a small bar plot and/or numbers. The
second option is to let the cell heights be given by the frequencies of the
corresponding combinations. Furthermore, variables may be sorted by the
number of missing/imputed values and combinations by the frequency of
occurrence to give more power to finding the structure of missing/imputed
values.
If combined is TRUE, a small version of the barplot showing
the amount of missing/imputed values in each variable is drawn on top of the
aggregation plot.
The graphical parameter oma will be set unless supplied as an
argument.
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.
print.aggr(), summary.aggr()
aggr()
print.summary.aggr(), aggr()
summary.aggr(), aggr()
Other plotting functions:
barMiss(),
histMiss(),
marginmatrix(),
marginplot(),
matrixplot(),
mosaicMiss(),
pairsVIM(),
parcoordMiss(),
pbox(),
scattJitt(),
scattMiss(),
scattmatrixMiss(),
spineMiss()
data(sleep, package="VIM")
## for missing values
a <- aggr(sleep)
a
summary(a)
## for imputed values
sleep_IMPUTED <- kNN(sleep)
a <- aggr(sleep_IMPUTED, delimiter="_imp")
a
summary(a)
data(sleep, package = "VIM")
a <- aggr(sleep, plot=FALSE)
a
data(sleep, package = "VIM")
summary(aggr(sleep, plot=FALSE))
data(sleep, package = "VIM")
s <- summary(aggr(sleep, plot=FALSE))
s
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