Solves an incompatibility with the {formula.tools} package. formula.tools:::as.character.formula() breaks base::as.character() for formulas, which prevented {missRanger} from working, see also
https://github.com/decisionpatterns/formula.tools/issues/11. We have added a workaround in #81.
Fixes a major bug, by which responses would be used as covariates in the random forests. Thanks for reporting @flystar233, see #78. You can expect different and better imputations.
Out-of-sample application is now possible! Thanks to @jeandigitale for pushing the idea in #58.
This means you can run imp <- missRanger(..., keep_forests = TRUE) and then apply its models to new data via predict(imp, newdata). The "missRanger" object can be saved/loaded as binary file, e.g, via saveRDS()/readRDS() for later use.
Note that out-of-sample imputation works best for rows in newdata with only one
missing value (counting only missings in variables used as covariates in random forests). We call this the "easy case". In the "hard case",
even multiple iterations (set by iter) can lead to unsatisfactory results.
The out-of-sample algorithm works as follows:
pmm() is more picky: xtrain and xtest must both be either numeric, logical, or factor (with identical levels).data_raw.visit_seq to to_impute.ranger() arguments are now explicit arguments in missRanger() to improve tab-completion experience:
keep_forests = TRUE, the argument data_only is set to FALSE by default.pmm.k.verbose argument is passed to ranger() as well.data_only = TRUE to control if only the imputed data should be returned (default), or an object of class "missRanger". This object contains the imputed data and infos like OOB prediction errors, fixing #28. The value FALSE will later becoming the default in {missRanger 3.0.0}. This will be announced via deprecation cycle.keep_forests = FALSE. Should the random forests of the best iteration (the one that generated the final imputed data) be added to the "missRanger" object? Note that this will use a lot of memory. Only relevant if data_only = FALSE. This solves #54.missRanger() now works with syntactically wrong variable names like "1bad:variable". This solves an old issue, recently popping up in this new issue.missRanger() now works with any number of features, as long as the formula is left at its default, i.e., . ~ .. This solves this issue.ranger() is now called via the x/y interface, not the formula interface anymore.importFrom to :: code styleMaintenance release,
mtry = function(m) max(1, m %/% 3). Keep in mind that missRanger() might use a growing set of covariables in the first iteration of the process, so passing mtry = 2 might result in an error.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.