This vignette showcases the functions
rangerImpute(), which can both be used to generate
imputations for several variables in a dataset using a formula
For data, a subset of
sleep is used. The columns have
been selected deliberately to include some interactions between the
library(VIM) library(magrittr) <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] dataset $BodyWgt <- log(dataset$BodyWgt) dataset$Span <- log(dataset$Span) datasetaggr(dataset)
str(dataset) #> 'data.frame': 62 obs. of 4 variables: #> $ Dream : num NA 2 NA NA 1.8 0.7 3.9 1 3.6 1.4 ... #> $ NonD : num NA 6.3 NA NA 2.1 9.1 15.8 5.2 10.9 8.3 ... #> $ BodyWgt: num 8.803 0 1.2194 -0.0834 7.8427 ... #> $ Span : num 3.65 1.5 2.64 NA 4.23 ...
In order to invoke the imputation methods, a formula is used to
specify which variables are to be estimated and which variables should
be used as regressors. We will start by imputing
<- regressionImp(NonD ~ BodyWgt + Span, dataset) imp_regression #> There still missing values in variable NonD . Probably due to missing values in the regressors. <- rangerImpute(NonD ~ BodyWgt + Span, dataset) imp_ranger aggr(imp_regression, delimiter = "_imp")
We can see that for
regrssionImp() there are still
NonD for all observations where
Span is unobserved. This is because the regression model
could not be applied to those observations. The same is true for the
values imputed via
As we can see in the next two plots, the correlation structure of
BodyWgt is preserved by both
imputation methods. In the case of
imputed values almost follow a straight line. This suggests that the
Span had little to no effect on the model.
c("NonD", "BodyWgt", "NonD_imp")] %>% imp_regression[, marginplot(delimiter = "_imp")
rangerImpute() on the other hand,
played an important role in the generation of the imputed values.
c("NonD", "BodyWgt", "NonD_imp")] %>% imp_ranger[, marginplot(delimiter = "_imp")
c("NonD", "Span", "NonD_imp")] %>% imp_ranger[, marginplot(delimiter = "_imp")
To impute several variables at once, the formula in
regressionImp() can be
specified with more than one column name in the left hand side.
<- regressionImp(Dream + NonD ~ BodyWgt + Span, dataset) imp_regression #> There still missing values in variable Dream . Probably due to missing values in the regressors. #> There still missing values in variable NonD . Probably due to missing values in the regressors. <- rangerImpute(Dream + NonD ~ BodyWgt + Span, dataset) imp_ranger aggr(imp_regression, delimiter = "_imp")
Again, there are missings left for both
In order to validate the performance of
iris dataset is used. Firstly, some values are randomly
library(reactable) data(iris) <- iris df colnames(df) <- c("S.Length","S.Width","P.Length","P.Width","Species") # randomly produce some missing values in the data set.seed(1) <- 50 nbr_missing <- data.frame(row=sample(nrow(iris),size = nbr_missing,replace = T), y col=sample(ncol(iris)-1,size = nbr_missing,replace = T)) <-y[!duplicated(y),] yas.matrix(y)]<-NA df[ aggr(df)
sapply(df, function(x)sum(is.na(x))) #> S.Length S.Width P.Length P.Width Species #> 12 10 13 12 0
We can see that there are missings in all variables and some
observations reveal missing values on several points. In the next step
we perform a multiple variable imputation and
serves as a regressor.
<- regressionImp(S.Length + S.Width + P.Length + P.Width ~ Species, df) imp_regression aggr(imp_regression, delimiter = "imp")
The plot indicates that all missing values have been imputed by the
regressionImp() algorithm. The following table displays the
rounded first five results of the imputation for all variables.