Overview
In addition to imputation methods, VIM provides a number
of functions, which can be used to plot results in sophisticated
ways.
This vignette showcases selected plotting functions, which are very supportive in context with visualizing missing and imputed values.
Data
The following example demonstrates the functionality of the plotting
functions using a subset of sleep. In order to emphasize
the features of the methods, the missing values in the dataset are
imputed via kNN() or regressionImp(). Both are
powerful donor-based imputation methods and also included in the
VIM package. (see vignette("donorImp"))
library(VIM)
dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
dataset$BodyWgt <- log(dataset$BodyWgt)
dataset$Span <- log(dataset$Span)
imp_knn <- kNN(dataset) # dataset with imputed values
#> NonD BodyWgt Span NonD BodyWgt Span
#> 2.1000000 -5.2983174 0.6931472 17.9000000 8.8029735 4.6051702
#> Dream BodyWgt Span Dream BodyWgt Span
#> 0.0000000 -5.2983174 0.6931472 6.6000000 8.8029735 4.6051702
#> Dream NonD BodyWgt Dream NonD BodyWgt
#> 0.000000 2.100000 -5.298317 6.600000 17.900000 8.802973To keep things as simple as possible, the plotting functions in
VIM uses three main colors. Each color represents a
property:
- BLUE observed values are highlighted in blue
- RED missing values are highlighted in red
- ORANGE imputed values are highlighted in orange
Function aggr()
The aggr() function calculates or plots the amount of
missing/imputed values in each variable and the amount of
missing/imputed values in certain combinations of variables.
aggr(dataset) 
aggr(imp_knn, delimiter = "_imp") 
The plots indicate that all missing values in the dataset are imputed
via knn(). (All the previously red bars changed their color
to orange)
Function barMiss()
The barMiss() function provides a barplot with
highlighting of missing/imputed values in other variables by splitting
each bar into two parts. Additionally, information about missing/imputed
values in the variable of interest is shown on the right hand side.
If only.miss=TRUE, the missing/imputed values in the
variable of interest are visualized by one bar on the right hand side.
If additional variables are supplied, this bar is again split into two
parts according to missingness/number of imputed missings in the
additional variables.
# for imputed values
x_IMPUTED <- regressionImp(NonD ~ Sleep, data = x)
#> There still missing values in variable NonD . Probably due to missing values in the regressors.
barMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)
The plot indicates that there are still some missings in NonD. This is
because the regression model could not be applied to observations, where
Sleep is unobserved.
Function scattMiss()
In addition to a standard scatterplot, lines are plotted in
scattMiss() for the missing values in one variable. If
there are imputed values, they will be highlighted.
Information about missing values in one variable is included as
vertical or horizontal lines, as determined by the 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.
In contrast to the other examples, regressionImp() is
used for imputing missing values. This has been done deliberately to
highlight the functionality of scattMiss(). The following
plots makes it easy to indentify missing/imputed values.
# for imputed values
imp_regression <- regressionImp(NonD ~ Sleep, dataset)
#> There still missing values in variable NonD . Probably due to missing values in the regressors.
scattMiss(imp_regression[,-3], delimiter = "_imp")
The plot indicates that there are still some missings in
NonD. This is because the regression model could not be
applied to observations, where Sleep is unobserved.
Function histMiss()
The histMiss() function visualizes data in a histogram
with highlighting the missing/imputed values in other variables by
splitting each bin into two parts. Additionally, information about
missing/imputed values in the variable of interest is shown on the right
hand side.
If only.miss=TRUE, the missing/imputed values in the
variable of interest are visualized by one bar on the right hand side.
If additional variables are supplied, this bar is again split into two
parts according to missingness/number of imputed missings in the
additional variables.
# for imputed values
x_IMPUTED <- regressionImp(NonD ~ Sleep, data = x)
#> There still missing values in variable NonD . Probably due to missing values in the regressors.
histMiss(x_IMPUTED, delimiter = "_imp", only.miss = FALSE)
Function matrixplot()
The matrixplot() function creats a matrix plot, in which
all cells of a data matrix are visualized by rectangles. Available data
is coded according to a continuous color scheme, while missing/imputed
data is visualized by a clearly distinguishable color.
x <- sleep[, c("Dream", "NonD","Sleep", "BodyWgt")]
x$BodyWgt <- log(x$BodyWgt)
# for missing values
matrixplot(x, sortby="BodyWgt")
# for imputed values - multiple variable imputation with regrssionImp()
x_IMPUTED <- regressionImp(NonD + Dream ~ Sleep, data = x)
#> There still missing values in variable NonD . Probably due to missing values in the regressors.
#> There still missing values in variable Dream . Probably due to missing values in the regressors.
matrixplot(x_IMPUTED, delimiter = "_imp", sortby = "BodyWgt")
Function marginplot()
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.
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.
dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")]
dataset$BodyWgt <- log(dataset$BodyWgt)
dataset$Span <- log(dataset$Span)
imp_knn <- kNN(dataset, variable = "NonD")
#> Dream BodyWgt Span Dream BodyWgt Span
#> 0.0000000 -5.2983174 0.6931472 6.6000000 8.8029735 4.6051702
dataset[, c("NonD", "Span")] |>
marginplot()
imp_knn[, c("NonD", "Span", "NonD_imp")] |>
marginplot(delimiter = "_imp")
Function marginmatrix()
The marginmatrix() function creates a scatterplot matrix
with information about missing/imputed values in the plot margins of
each panel.
## for missing values
x <- sleep[, 2:4]
x[, 1] <- log10(x[, 1])
marginmatrix(x)
## for imputed values
x_imp <- irmi(sleep[, 2:4])
#> BrainWgt Dream BrainWgt Dream
#> 0.14 0.00 5712.00 6.60
#> BrainWgt NonD BrainWgt NonD
#> 0.14 2.10 5712.00 17.90
x_imp[,1] <- log10(x_imp[, 1])
marginmatrix(x_imp, delimiter = "_imp")