This is a summary of all changes since version 1.x.x.
missRanger now also imputes and uses logical variables, character variables and further variables of mode numeric like dates and times.
Added formula interface to specify which variables to impute (those on the left hand side) and those used to do so (those on the right hand side). Here some (pseudo) examples:
. ~ . (default): Use all variables to impute all variables. Note that only those with missing values will be imputed. Variables without missings will only be used to impute others.
. ~ . - ID: Use all variables except ID to impute all missing values.
Species ~ Sepal.Width: Use Sepal.Width to impute Species. Only works if Sepal.Width does not contain missing values. (Add it to the right hand side if it does.)
Species + Sepal.Length ~ Species + Petal.Length: Use Species and Petal.Length to impute Species and Sepal.Length. Only works if Petal.Length does not contain missing values because it does not appear on the left hand side and is therefore not imputed itself.
. ~ 1: Univariate imputation for all relevant columns (as nothing is selected on the right hand side).
The first argument of generateNA is called x instead of data in consistency with imputeUnivariate.
imputeUnivariate now also works for data frames and matrices.
In PMM mode, missRanger relies on OOB predictions. The smaller the value of num.trees, the higher the risk of missing OOB predictions, which caused an error in PMM. Now, pmm allows for missing values in xtrain or ytrain. Thus, the algorithm will even work with num.trees = 1. This will be useful to impute large data sets with PMM.
The function imputeUnivariate has received a seed argument.
The function imputeUnivariate has received a v argument, specifying columns to impute.
The function generateNA offers now the possibility to use different proportions of missings for each column.
If verbose is not 0, then missRanger will show which variables will be imputed in which order and which variables will be used for imputation.
returnOOB is now effectively controlling if out-of-bag errors are attached as attribute “oob” to the resulting data frame or not. So far, it was always attached.