VIM introduces tools for visualization of missing and imputed values. Forthermore, methods to impute missing values are featured. This vignette will give a brief look at a common imputation scenario and showcase how VIM can be used to both impute the data and also interpret the results visually.
library(VIM) data(sleep) a <- aggr(sleep, plot = FALSE) plot(a, numbers = TRUE, prop = FALSE)
The left plot shows the amount of missings for each column in the dataset
sleep and the right plot shows how often each combination of missings occur.
For example, there are 9 rows wich contain a missing in both
For simplicity, we will only look at the variables
Sleep for the remainer of this vignette. Bivariate datasets can be passed
to special functions that visualize the structure of missings such as
x <- sleep[, c("Dream", "Sleep")] marginplot(x)
The red boxplot on the left shows the distrubution of all values of
Dream contains a missing value. The blue boxplot on the left shows
the distribution of the values of
Dream is observed.
In order to impute missing values,
VIM offers a spectrum of imputation methods
kNN() (k nearest neighbor),
hotdeck() and so forth. Those functions
can be applied to a
data.frame and return another
data.frame where missings
are replaced by imputed values.
x_imputed <- kNN(x)
Note however, that in certain circumstances, there will be missings left in the returned dataset depending on the method used.
The same functions that visualize missing values can also visualize the imputed dataset.
marginplot(x_imputed, delimiter = "_imp")
In this plot three differnt colors are used in the top-right. These colors represent the structure of missings.
Dreamwas missing initially
Sleepwas missing initially
Sleepwere missing initially
kNN() method seemingly preserves the correlation between