Package 'missRanger'

Adds Missing Values

Description

Takes a vector, matrix or data.frame and replaces some values by NA.

Usage

generateNA(x, p = 0.1, seed = NULL)

Arguments

x	A vector, matrix or data.frame.
p	Proportion of missing values to add to x. In case x is a data.frame, p can also be a vector of probabilities per column or a named vector.
seed	An integer seed.

Value

x with missing values.

Examples

generateNA(1:10, p = 0.5)
head(generateNA(iris, p = 0.2))

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Univariate Imputation

Description

Fills missing values of a vector, matrix or data frame by sampling with replacement from the non-missing values. For data frames, this sampling is done within column.

Usage

imputeUnivariate(x, v = NULL, seed = NULL)

Arguments

x	A vector, matrix or data frame.
v	A character vector of column names to impute (only relevant if x is a data frame). The default NULL imputes all columns.
seed	An integer seed.

Value

x with imputed values.

Examples

imputeUnivariate(c(NA, 0, 1, 0, 1))
head(imputeUnivariate(generateNA(iris)))

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Fast Imputation of Missing Values by Chained Random Forests

Description

Uses the "ranger" package (Wright & Ziegler) to do fast missing value imputation by chained random forests, see Stekhoven & Buehlmann and Van Buuren & Groothuis-Oudshoorn. 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. This allows to do multiple imputation when repeating the call to missRanger().

Usage

missRanger(
  data,
  formula = . ~ .,
  pmm.k = 0L,
  num.trees = 500,
  mtry = NULL,
  min.node.size = NULL,
  min.bucket = NULL,
  max.depth = NULL,
  replace = TRUE,
  sample.fraction = if (replace) 1 else 0.632,
  case.weights = NULL,
  num.threads = NULL,
  save.memory = FALSE,
  maxiter = 10L,
  seed = NULL,
  verbose = 1,
  returnOOB = FALSE,
  data_only = !keep_forests,
  keep_forests = FALSE,
  ...
)

Arguments

data	A data.frame 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. For instance, if all variables (with missings) should be imputed by all variables except variable "ID", use . ~ . - ID. Note that a "." is evaluated separately for each side of the formula. Further 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 steps. 0 to avoid this step.
num.trees	Number of trees passed to ranger::ranger().
mtry	Number of covariates considered per split. The default NULL equals the rounded down root of the number of features. Can be a function, e.g., function(p) trunc(p/3). Passed to ranger::ranger(). Note that during the first iteration, the number of features is growing. Thus, a fixed value can lead to an error. Using a function like function(p) min(p, 2) will fix such problem.
min.node.size	Minimal node size passed to ranger::ranger(). By default 1 for classification and 5 for regression.
min.bucket	Minimal terminal node size passed to ranger::ranger(). The default NULL means 1.
max.depth	Maximal tree depth passed to ranger::ranger(). NULL means unlimited depth. 1 means single split trees.
replace	Sample with replacement passed to ranger::ranger().
sample.fraction	Fraction of rows per tree passed to ranger::ranger(). The default: use all rows when replace = TRUE and 0.632 otherwise.
case.weights	Optional case weights passed to ranger::ranger().
num.threads	Number of threads passed to ranger::ranger(). The default NULL uses all threads.
save.memory	Slow but memory saving mode of ranger::ranger().
maxiter	Maximum number of iterations.
seed	Integer seed.
verbose	A value in 0, 1, 2 controlling the verbosity.
returnOOB	Should the final average OOB prediction errors be added as data attribute "oob"? Only relevant when data_only = TRUE.
data_only	If TRUE (default), only the imputed data is returned. Otherwise, a "missRanger" object with additional information is returned.
keep_forests	Should the random forests of the last relevant iteration be returned? The default is FALSE. Setting this option will use a lot of memory. Only relevant when data_only = TRUE.
...	Additional arguments passed to ranger::ranger(). Not all make sense.

Details

The iterative chaining stops as soon as maxiter is reached or if the average out-of-bag (OOB) prediction errors stop reducing. In the latter case, except for the first iteration, the second last (= best) imputed data is returned.

OOB prediction errors are quantified as 1 - R^2 for numeric variables, and as classification error otherwise. If a variable has been imputed only univariately, the value is 1.

Value

If data_only = TRUE an imputed data.frame. Otherwise, a "missRanger" object with the following elements:

• data: The imputed data.

• data_raw: The original data provided.

• forests: When keep_forests = TRUE, a list of "ranger" models used to generate the imputed data. NULL otherwise.

• to_impute: Variables to be imputed (in this order).

• impute_by: Variables used for imputation.

• best_iter: Best iteration.

• pred_errors: Per-iteration OOB prediction errors (1 - R^2 for regression, classification error otherwise).

• mean_pred_errors: Per-iteration averages of OOB prediction errors.

• pmm.k: Same as input pmm.k.

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. <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

iris2 <- generateNA(iris, seed = 1)

imp1 <- missRanger(iris2, pmm.k = 5, num.trees = 50, seed = 1)
head(imp1)

# Extended output
imp2 <- missRanger(iris2, pmm.k = 5, num.trees = 50, data_only = FALSE, seed = 1)
summary(imp2)

all.equal(imp1, imp2$data)

# Formula interface: Univariate imputation of Species and Sepal.Width
imp3 <- missRanger(iris2, Species + Sepal.Width ~ 1)

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Predictive Mean Matching

Description

For each value in the prediction vector xtest, one of the closest k values in the prediction vector xtrain is randomly chosen and its observed value in ytrain is returned. Note that xtrain and xtest must be both either numeric, logical, or factor-valued. ytest can be of any type.

Usage

pmm(xtrain, xtest, ytrain, k = 1L, seed = NULL)

Arguments

xtrain	Vector with predicted values in the training data. Must be numeric, logical, or factor-valued.
xtest	Vector as xtrain with predicted values in the test data. Missing values are not allowed.
ytrain	Vector of the observed values in the training data. Must be of same length as xtrain.
k	Number of nearest neighbours (donors) to sample from.
seed	Integer random seed.

Value

Vector of the same length as xtest with values from xtrain.

Examples

pmm(xtrain = c(0.2, 0.3, 0.8), xtest = c(0.7, 0.2), ytrain = 1:3, k = 1)  # c(3, 1)

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Predict Method

Description

Impute missing values on newdata based on an object of class "missRanger".

For multivariate imputation, use missRanger(..., keep_forests = TRUE). For univariate imputation, no forests are required. This can be enforced by predict(..., iter = 0) or via missRanger(. ~ 1, ...).

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.

Usage

## S3 method for class 'missRanger'
predict(
  object,
  newdata,
  pmm.k = object$pmm.k,
  iter = 4L,
  num.threads = NULL,
  seed = NULL,
  verbose = 1L,
  ...
)

Arguments

object	'missRanger' object.
newdata	A data.frame with missing values to impute.
pmm.k	Number of candidate predictions of the original dataset for predictive mean matching (PMM). By default the same value as during fitting.
iter	Number of iterations for "hard case" rows. 0 for univariate imputation.
num.threads	Number of threads used by ranger's predict function. The default NULL uses all threads.
seed	Integer seed used for initial univariate imputation and PMM.
verbose	Should info be printed? (1 = yes/default, 0 for no).
...	Passed to the predict function of ranger.

Details

The out-of-sample algorithm works as follows:

1. Impute univariately all relevant columns by randomly drawing values from the original unimputed data. This step will only impact "hard case" rows.

2. Replace univariate imputations by predictions of random forests. This is done sequentially over variables, where the variables are sorted to minimize the impact of univariate imputations. Optionally, this is followed by predictive mean matching (PMM).

3. Repeat Step 2 for "hard case" rows multiple times.

Examples

iris2 <- generateNA(iris, seed = 20, p = c(Sepal.Length = 0.2, Species = 0.1))
imp <- missRanger(iris2, pmm.k = 5, num.trees = 100, keep_forests = TRUE, seed = 2)
predict(imp, head(iris2), seed = 3)

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Print Method

Description

Print method for an object of class "missRanger".

Usage

## S3 method for class 'missRanger'
print(x, ...)

Arguments

x	An object of class "missRanger".
...	Further arguments passed from other methods.

Value

Invisibly, the input is returned.

Examples

CO2_ <- generateNA(CO2, seed = 1)
imp <- missRanger(CO2_, pmm.k = 5, data_only = FALSE, num.threads = 1)
imp

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Summary Method

Description

Summary method for an object of class "missRanger".

Usage

## S3 method for class 'missRanger'
summary(object, ...)

Arguments

object	An object of class "missRanger".
...	Further arguments passed from other methods.

Value

Invisibly, the input is returned.

Examples

CO2_ <- generateNA(CO2, seed = 1)
imp <- missRanger(CO2_, pmm.k = 5, data_only = FALSE, num.threads = 1)
summary(imp)

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