Miss Ranger Manual

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Package ‘missRanger’
June 14, 2019
Title Fast Imputation of Missing Values
Version 2.0.1
Description Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixedtype data sets by chaining random forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) . Under the hood, it uses the lightning fast random jungle package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 01 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow e.g. to do multiple imputation when repeating the call to missRanger().
Depends R (>= 3.5.0)
License GPL(>= 2)
Encoding UTF-8
LazyData true
Type Package
Date 2019-06-14
Imports stats, FNN (>= 1.1), ranger (>= 0.10)
Author Michael Mayer [aut, cre, cph]
Maintainer Michael Mayer 
RoxygenNote 6.1.1
NeedsCompilation no

R topics documented:
allVarsTwoSided
generateNA . . .
imputeUnivariate
missRanger . . .
pmm . . . . . . .

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2
2
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5
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1

2

generateNA

Extraction of Variable Names from Two-Sided Formula.

allVarsTwoSided

Description
Takes a formula and a data frame and returns all variable names in both the lhs and the rhs. lhs and
rhs are evaluated separately. This is relevant if both sides contain a "." (= all variables).
Usage
allVarsTwoSided(formula, data)
Arguments
formula
data

A two-sided formula object.
A data.frame. Primarily used to deal with "." in the formula.

Value
A list with two character vectors of variable names.
Examples
allVarsTwoSided(Species + Sepal.Width ~ Petal.Width, iris)
allVarsTwoSided(. ~ ., iris)
allVarsTwoSided(.-Species ~ Sepal.Width, iris)
allVarsTwoSided(. ~ Sepal.Width, iris)

Adds Missing Values to a Data Set

generateNA

Description
Takes a data frame and replaces randomly part of the values by missing values.
Usage
generateNA(data, p = 0.1, seed = NULL)
Arguments
data
p
seed

A data.frame.
Proportion of missing values to approximately add to each column of data.
An integer seed.

Value
data with missing values.
Examples
head(generateNA(iris))

imputeUnivariate

imputeUnivariate

3

Univariate Imputation

Description
Fills missing values of a vector of any type by sampling with replacement from the non-missing
values. Requires at least one non-missing value to run.
Usage
imputeUnivariate(x, seed = NULL)
Arguments
x

A vector of any type possibly containing missing values.

seed

An integer seed.

Value
A vector of the same length and type as x but without missing values.
Examples
imputeUnivariate(c(NA, 0, 1, 0, 1))
imputeUnivariate(c("A", "A", NA))
imputeUnivariate(as.factor(c("A", "A", NA)))
# Impute a whole data set univariately
ir <- generateNA(iris)
head(imputed <- do.call(data.frame, lapply(ir, imputeUnivariate)))

missRanger

Fast Imputation of Missing Values by Chained Random Forests

Description
Uses the "ranger" package [1] to do fast missing value imputation by chained random forests, see
[2] and [3]. Between the iterative model fitting, it offers the option of predictive mean matching.
This firstly avoids imputation with values not present in the original data (like a value 0.3334 in a
0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting
conditional distributions to a realistic level and, as such, allows to do multiple imputation when
repeating the call to missRanger(). The iterative chaining stops as soon as maxiter is reached or if
the average out-of-bag estimate of performance stops improving. In the latter case, except for the
first iteration, the second last (i.e. best) imputed data is returned.
Usage
missRanger(data, formula = . ~ ., pmm.k = 0L, maxiter = 10L,
seed = NULL, verbose = 1, returnOOB = FALSE, case.weights = NULL,
...)

4

missRanger

Arguments
data

A data.frame or tibble with missing values to impute.

formula

A two-sided formula specifying variables to be imputed (left hand side) and
variables used to impute (right hand side). Defaults to . ~ ., i.e. use all variables
to impute all variables. If e.g. all variables (with missings) should be imputed
by all variables except variable "ID", use . ~ . - ID. Note that a "." is evaluated
separately for both sides of the formula. Note that variables with missings must
appear in the left hand side if they should be used on the right hand side.

pmm.k

Number of candidate non-missing values to sample from in the predictive mean
matching step. 0 to avoid this step.

maxiter

Maximum number of chaining iterations.

seed

Integer seed to initialize the random generator.

verbose

Controls how much info is printed to screen. 0 to print nothing. 1 (default) to
print a "." per iteration and variable, 2 to print the OOB prediction error per
iteration and variable (1 minus R-squared for regression).

returnOOB

Logical flag. If TRUE, the final average out-of-bag prediction error is added to
the output as attribute "oob".

case.weights

Vector with weight per observation in the data set used in fitting the random
forests.

...

Arguments passed to ranger. If the data set is large, better use less trees (e.g.
num.trees = 100) and/or a low value of sample.fraction. The following arguments are incompatible: data, write.forest, probability, split.select.weights,
dependent.variable.name, and classification.

Value
An imputed data.frame.
References
[1] Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High
Dimensional Data in C++ and R. Journal of Statistical Software, in press. http://arxiv.org/abs/1508.04409.
[2] Stekhoven, D.J. and Buehlmann, P. (2012). ’MissForest - nonparametric missing value imputation for mixed-type data’, Bioinformatics, 28(1) 2012, 112-118. https://doi.org/10.1093/bioinformatics/btr597.
[3] Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained
Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/
Examples
irisWithNA <- generateNA(iris)
irisImputed <- missRanger(irisWithNA, pmm.k = 3, num.trees = 100)
head(irisImputed)
head(irisWithNA)
# With extra trees algorithm
irisImputed_et <- missRanger(irisWithNA, pmm.k = 3, num.trees = 100, splitrule = "extratrees")
head(irisImputed_et)
# Do not impute Species. Note: Since this variable contains missings, it cannot be used
# to impute the other variables as well.

pmm

5

irisImputed <- missRanger(irisWithNA, . - Species ~ ., pmm.k = 3, num.trees = 100)
# Impute univariately only.
irisImputed <- missRanger(irisWithNA, . ~ 1)
# Use Species and Petal.Length to impute Species and Petal.Length.
irisImputed <- missRanger(irisWithNA, Species + Petal.Length ~ Species + Petal.Length,
pmm.k = 3, num.trees = 100)

Predictive Mean Matching

pmm

Description
This function is used internally only but might help others to implement an efficient way of doing
predictive mean matching on top of any prediction based missing value imputation. It works as
follows: For each predicted value of a vector xtest, the closest k predicted values of another vector
xtrain are identified by k-nearest neighbour. Then, one of those neighbours is randomly picked
and its corresponding observed value in ytrain is returned.
Usage
pmm(xtrain, xtest, ytrain, k = 1L, seed = NULL)
Arguments
xtrain

Vector with predicted values in the training data set.

xtest

Vector with predicted values in the test data set.

ytrain

Vector with observed response in the training data set.

k

Number of nearest neighbours to choose from. Set k = 0 if no predictive mean
matching is to be done.

seed

Integer random seed.

Value
Vector with predicted values in the test data set based on predictive mean matching.
Examples
pmm(xtrain
pmm(xtrain
pmm(xtrain
pmm(xtrain

=
=
=
=

c(0.2,
c(0.2,
c("A",
c("A",

0.2,
0.2,
"A",
"A",

0.8),
0.8),
"B"),
"B"),

xtest
xtest
xtest
xtest

=
=
=
=

0.3,
0.3,
"B",
"B",

ytrain
ytrain
ytrain
ytrain

=
=
=
=

c(0, 0, 1),
c(0, 0, 1),
c("B", "A",
c("B", "A",

k = 1) # 0
k = 3) # 0 or 1
"B"), k = 1) # B
"B"), k = 2) # A or B

Index
allVarsTwoSided, 2
generateNA, 2
imputeUnivariate, 3
missRanger, 3
pmm, 5

6



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