Ranger is a fast implementation of random forests (Breiman 2001) or recursive partitioning, particularly suited for high dimensional data. Classification, regression, and survival forests are supported. Classification and regression forests are implemented as in the original Random Forest (Breiman 2001), survival forests as in Random Survival Forests (Ishwaran et al. 2008). Includes implementations of extremely randomized trees (Geurts et al. 2006) and quantile regression forests (Meinshausen 2006).

```
ranger(
formula = NULL,
data = NULL,
num.trees = 500,
mtry = NULL,
importance = "none",
write.forest = TRUE,
probability = FALSE,
min.node.size = NULL,
min.bucket = NULL,
max.depth = NULL,
replace = TRUE,
sample.fraction = ifelse(replace, 1, 0.632),
case.weights = NULL,
class.weights = NULL,
splitrule = NULL,
num.random.splits = 1,
alpha = 0.5,
minprop = 0.1,
split.select.weights = NULL,
always.split.variables = NULL,
respect.unordered.factors = NULL,
scale.permutation.importance = FALSE,
local.importance = FALSE,
regularization.factor = 1,
regularization.usedepth = FALSE,
keep.inbag = FALSE,
inbag = NULL,
holdout = FALSE,
quantreg = FALSE,
time.interest = NULL,
oob.error = TRUE,
num.threads = NULL,
save.memory = FALSE,
verbose = TRUE,
node.stats = FALSE,
seed = NULL,
dependent.variable.name = NULL,
status.variable.name = NULL,
classification = NULL,
x = NULL,
y = NULL,
...
)
```

Object of class `ranger`

with elements

`forest`

Saved forest (If write.forest set to TRUE). Note that the variable IDs in the

`split.varIDs`

object do not necessarily represent the column number in R.`predictions`

Predicted classes/values, based on out-of-bag samples (classification and regression only).

`variable.importance`

Variable importance for each independent variable.

`variable.importance.local`

Variable importance for each independent variable and each sample, if

`local.importance`

is set to TRUE and`importance`

is set to 'permutation'.`prediction.error`

Overall out-of-bag prediction error. For classification this is accuracy (proportion of misclassified observations), for probability estimation the Brier score, for regression the mean squared error and for survival one minus Harrell's C-index.

`r.squared`

R squared. Also called explained variance or coefficient of determination (regression only). Computed on out-of-bag data.

`confusion.matrix`

Contingency table for classes and predictions based on out-of-bag samples (classification only).

`unique.death.times`

Unique death times (survival only).

`chf`

Estimated cumulative hazard function for each sample (survival only).

`survival`

Estimated survival function for each sample (survival only).

`call`

Function call.

`num.trees`

Number of trees.

`num.independent.variables`

Number of independent variables.

`mtry`

Value of mtry used.

`min.node.size`

Value of minimal node size used.

`treetype`

Type of forest/tree. classification, regression or survival.

`importance.mode`

Importance mode used.

`num.samples`

Number of samples.

`inbag.counts`

Number of times the observations are in-bag in the trees.

`dependent.variable.name`

Name of the dependent variable. This is NULL when x/y interface is used.

`status.variable.name`

Name of the status variable (survival only). This is NULL when x/y interface is used.

- formula
Object of class

`formula`

or`character`

describing the model to fit. Interaction terms supported only for numerical variables.- data
Training data of class

`data.frame`

,`matrix`

,`dgCMatrix`

(Matrix) or`gwaa.data`

(GenABEL).- num.trees
Number of trees.

- mtry
Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables. Alternatively, a single argument function returning an integer, given the number of independent variables.

- importance
Variable importance mode, one of 'none', 'impurity', 'impurity_corrected', 'permutation'. The 'impurity' measure is the Gini index for classification, the variance of the responses for regression and the sum of test statistics (see

`splitrule`

) for survival.- write.forest
Save

`ranger.forest`

object, required for prediction. Set to`FALSE`

to reduce memory usage if no prediction intended.- probability
Grow a probability forest as in Malley et al. (2012).

- min.node.size
Minimal node size to split at. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.

- min.bucket
Minimal terminal node size. No nodes smaller than this value can occur. Default 3 for survival and 1 for all other tree types.

- max.depth
Maximal tree depth. A value of NULL or 0 (the default) corresponds to unlimited depth, 1 to tree stumps (1 split per tree).

- replace
Sample with replacement.

- sample.fraction
Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement. For classification, this can be a vector of class-specific values.

- case.weights
Weights for sampling of training observations. Observations with larger weights will be selected with higher probability in the bootstrap (or subsampled) samples for the trees.

- class.weights
Weights for the outcome classes (in order of the factor levels) in the splitting rule (cost sensitive learning). Classification and probability prediction only. For classification the weights are also applied in the majority vote in terminal nodes.

- splitrule
Splitting rule. For classification and probability estimation "gini", "extratrees" or "hellinger" with default "gini". For regression "variance", "extratrees", "maxstat" or "beta" with default "variance". For survival "logrank", "extratrees", "C" or "maxstat" with default "logrank".

- num.random.splits
For "extratrees" splitrule.: Number of random splits to consider for each candidate splitting variable.

- alpha
For "maxstat" splitrule: Significance threshold to allow splitting.

- minprop
For "maxstat" splitrule: Lower quantile of covariate distribution to be considered for splitting.

- split.select.weights
Numeric vector with weights between 0 and 1, used to calculate the probability to select variables for splitting. Alternatively, a list of size num.trees, containing split select weight vectors for each tree can be used.

- always.split.variables
Character vector with variable names to be always selected in addition to the

`mtry`

variables tried for splitting.- respect.unordered.factors
Handling of unordered factor covariates. One of 'ignore', 'order' and 'partition'. For the "extratrees" splitrule the default is "partition" for all other splitrules 'ignore'. Alternatively TRUE (='order') or FALSE (='ignore') can be used. See below for details.

- scale.permutation.importance
Scale permutation importance by standard error as in (Breiman 2001). Only applicable if permutation variable importance mode selected.

- local.importance
Calculate and return local importance values as in (Breiman 2001). Only applicable if

`importance`

is set to 'permutation'.- regularization.factor
Regularization factor (gain penalization), either a vector of length p or one value for all variables.

- regularization.usedepth
Consider the depth in regularization.

- keep.inbag
Save how often observations are in-bag in each tree.

- inbag
Manually set observations per tree. List of size num.trees, containing inbag counts for each observation. Can be used for stratified sampling.

- holdout
Hold-out mode. Hold-out all samples with case weight 0 and use these for variable importance and prediction error.

- quantreg
Prepare quantile prediction as in quantile regression forests (Meinshausen 2006). Regression only. Set

`keep.inbag = TRUE`

to prepare out-of-bag quantile prediction.- time.interest
Time points of interest (survival only). Can be

`NULL`

(default, use all observed time points), a vector of time points or a single number to use as many time points (grid over observed time points).- oob.error
Compute OOB prediction error. Set to

`FALSE`

to save computation time, e.g. for large survival forests.- num.threads
Number of threads. Default is number of CPUs available.

- save.memory
Use memory saving (but slower) splitting mode. No effect for survival and GWAS data. Warning: This option slows down the tree growing, use only if you encounter memory problems.

- verbose
Show computation status and estimated runtime.

- node.stats
Save node statistics. Set to

`TRUE`

to save prediction, number of observations and split statistics for each node.- seed
Random seed. Default is

`NULL`

, which generates the seed from`R`

. Set to`0`

to ignore the`R`

seed.- dependent.variable.name
Name of dependent variable, needed if no formula given. For survival forests this is the time variable.

- status.variable.name
Name of status variable, only applicable to survival data and needed if no formula given. Use 1 for event and 0 for censoring.

- classification
Set to

`TRUE`

to grow a classification forest. Only needed if the data is a matrix or the response numeric.- x
Predictor data (independent variables), alternative interface to data with formula or dependent.variable.name.

- y
Response vector (dependent variable), alternative interface to data with formula or dependent.variable.name. For survival use a

`Surv()`

object or a matrix with time and status.- ...
Further arguments passed to or from other methods (currently ignored).

Marvin N. Wright

The tree type is determined by the type of the dependent variable. For factors classification trees are grown, for numeric values regression trees and for survival objects survival trees. The Gini index is used as default splitting rule for classification. For regression, the estimated response variances or maximally selected rank statistics (Wright et al. 2016) can be used. For Survival the log-rank test, a C-index based splitting rule (Schmid et al. 2015) and maximally selected rank statistics (Wright et al. 2016) are available. For all tree types, forests of extremely randomized trees (Geurts et al. 2006) can be grown.

With the `probability`

option and factor dependent variable a probability forest is grown.
Here, the node impurity is used for splitting, as in classification forests.
Predictions are class probabilities for each sample.
In contrast to other implementations, each tree returns a probability estimate and these estimates are averaged for the forest probability estimate.
For details see Malley et al. (2012).

Note that nodes with size smaller than `min.node.size`

can occur because `min.node.size`

is the minimal node size *to split at*, as in original Random Forests.
To restrict the size of terminal nodes, set `min.bucket`

.
Variables selected with `always.split.variables`

are tried additionally to the mtry variables randomly selected.
In `split.select.weights`

, weights do not need to sum up to 1, they will be normalized later.
The weights are assigned to the variables in the order they appear in the formula or in the data if no formula is used.
Names of the `split.select.weights`

vector are ignored.
Weights assigned by `split.select.weights`

to variables in `always.split.variables`

are ignored.
The usage of `split.select.weights`

can increase the computation times for large forests.

Unordered factor covariates can be handled in 3 different ways by using `respect.unordered.factors`

:
For 'ignore' all factors are regarded ordered, for 'partition' all possible 2-partitions are considered for splitting.
For 'order' and 2-class classification the factor levels are ordered by their proportion falling in the second class, for regression by their mean response, as described in Hastie et al. (2009), chapter 9.2.4.
For multiclass classification the factor levels are ordered by the first principal component of the weighted covariance matrix of the contingency table (Coppersmith et al. 1999), for survival by the median survival (or the largest available quantile if the median is not available).
The use of 'order' is recommended, as it computationally fast and can handle an unlimited number of factor levels.
Note that the factors are only reordered once and not again in each split.

The 'impurity_corrected' importance measure is unbiased in terms of the number of categories and category frequencies and is almost as fast as the standard impurity importance.
It is a modified version of the method by Sandri & Zuccolotto (2008), which is faster and more memory efficient. See Nembrini et al. (2018) for details.
This importance measure can be combined with the methods to estimate p-values in `importance_pvalues`

.
We recommend not to use the 'impurity_corrected' importance when making predictions since the feature permutation step might reduce predictive performance (a warning is raised when predicting on new data).

Note that ranger has different default values than other packages.
For example, our default for `mtry`

is the square root of the number of variables for all tree types, whereas other packages use different values for regression.
Also, changing one hyperparameter does not change other hyperparameters (where possible).
For example, `splitrule="extratrees"`

uses randomized splitting but does not disable bagging as in Geurts et al. (2006).
To disable bagging, use `replace = FALSE, sample.fraction = 1`

.
This can also be used to grow a single decision tree without bagging and feature subsetting: `ranger(..., num.trees = 1, mtry = p, replace = FALSE, sample.fraction = 1)`

, where p is the number of independent variables.

While random forests are known for their robustness, default hyperparameters not always work well.
For example, for high dimensional data, increasing the `mtry`

value and the number of trees `num.trees`

is recommended.
For more details and recommendations, see Probst et al. (2019).
To find the best hyperparameters, consider hyperparameter tuning with the `tuneRanger`

or `mlr3`

packages.

Out-of-bag prediction error is calculated as accuracy (proportion of misclassified observations) for classification, as Brier score for probability estimation, as mean squared error (MSE) for regression and as one minus Harrell's C-index for survival.
Harrell's C-index is calculated based on the sum of the cumulative hazard function (CHF) over all timepoints, i.e., `rowSums(chf)`

, where `chf`

is the the out-of-bag CHF; for details, see Ishwaran et al. (2008).
Calculation of the out-of-bag prediction error can be turned off with `oob.error = FALSE`

.

Regularization works by penalizing new variables by multiplying the splitting criterion by a factor, see Deng & Runger (2012) for details.
If `regularization.usedepth=TRUE`

, \(f^d\) is used, where *f* is the regularization factor and *d* the depth of the node.
If regularization is used, multithreading is deactivated because all trees need access to the list of variables that are already included in the model.

For a large number of variables and data frames as input data the formula interface can be slow or impossible to use.
Alternatively `dependent.variable.name`

(and `status.variable.name`

for survival) or `x`

and `y`

can be used.
Use `x`

and `y`

with a matrix for `x`

to avoid conversions and save memory.
Consider setting `save.memory = TRUE`

if you encounter memory problems for very large datasets, but be aware that this option slows down the tree growing.

For GWAS data consider combining `ranger`

with the `GenABEL`

package.
See the Examples section below for a demonstration using `Plink`

data.
All SNPs in the `GenABEL`

object will be used for splitting.
To use only the SNPs without sex or other covariates from the phenotype file, use `0`

on the right hand side of the formula.
Note that missing values are treated as an extra category while splitting.

See https://github.com/imbs-hl/ranger for the development version.

With recent R versions, multithreading on Windows platforms should just work. If you compile yourself, the new RTools toolchain is required.

Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. tools:::Rd_expr_doi("10.18637/jss.v077.i01").

Schmid, M., Wright, M. N. & Ziegler, A. (2016). On the use of Harrell's C for clinical risk prediction via random survival forests. Expert Syst Appl 63:450-459. tools:::Rd_expr_doi("10.1016/j.eswa.2016.07.018").

Wright, M. N., Dankowski, T. & Ziegler, A. (2017). Unbiased split variable selection for random survival forests using maximally selected rank statistics. Stat Med 36:1272-1284. tools:::Rd_expr_doi("10.1002/sim.7212").

Nembrini, S., Koenig, I. R. & Wright, M. N. (2018). The revival of the Gini Importance? Bioinformatics. tools:::Rd_expr_doi("10.1093/bioinformatics/bty373").

Breiman, L. (2001). Random forests. Mach Learn, 45:5-32. tools:::Rd_expr_doi("10.1023/A:1010933404324").

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. Ann Appl Stat 2:841-860. tools:::Rd_expr_doi("10.1097/JTO.0b013e318233d835").

Malley, J. D., Kruppa, J., Dasgupta, A., Malley, K. G., & Ziegler, A. (2012). Probability machines: consistent probability estimation using nonparametric learning machines. Methods Inf Med 51:74-81. tools:::Rd_expr_doi("10.3414/ME00-01-0052").

Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning. Springer, New York. 2nd edition.

Geurts, P., Ernst, D., Wehenkel, L. (2006). Extremely randomized trees. Mach Learn 63:3-42. tools:::Rd_expr_doi("10.1007/s10994-006-6226-1").

Meinshausen (2006). Quantile Regression Forests. J Mach Learn Res 7:983-999. https://www.jmlr.org/papers/v7/meinshausen06a.html.

Sandri, M. & Zuccolotto, P. (2008). A bias correction algorithm for the Gini variable importance measure in classification trees. J Comput Graph Stat, 17:611-628. tools:::Rd_expr_doi("10.1198/106186008X344522").

Coppersmith D., Hong S. J., Hosking J. R. (1999). Partitioning nominal attributes in decision trees. Data Min Knowl Discov 3:197-217. tools:::Rd_expr_doi("10.1023/A:1009869804967").

Deng & Runger (2012). Feature selection via regularized trees. The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia. tools:::Rd_expr_doi("10.1109/IJCNN.2012.6252640").

Probst, P., Wright, M. N. & Boulesteix, A-L. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining Knowl Discov 9:e1301.tools:::Rd_expr_doi("10.1002/widm.1301").

`predict.ranger`

```
## Classification forest with default settings
ranger(Species ~ ., data = iris)
## Prediction
train.idx <- sample(nrow(iris), 2/3 * nrow(iris))
iris.train <- iris[train.idx, ]
iris.test <- iris[-train.idx, ]
rg.iris <- ranger(Species ~ ., data = iris.train)
pred.iris <- predict(rg.iris, data = iris.test)
table(iris.test$Species, pred.iris$predictions)
## Quantile regression forest
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles")
pred$predictions
## Variable importance
rg.iris <- ranger(Species ~ ., data = iris, importance = "impurity")
rg.iris$variable.importance
## Survival forest
require(survival)
rg.veteran <- ranger(Surv(time, status) ~ ., data = veteran)
plot(rg.veteran$unique.death.times, rg.veteran$survival[1,])
## Alternative interfaces (same results)
ranger(dependent.variable.name = "Species", data = iris)
ranger(y = iris[, 5], x = iris[, -5])
if (FALSE) {
## Use GenABEL interface to read Plink data into R and grow a classification forest
## The ped and map files are not included
library(GenABEL)
convert.snp.ped("data.ped", "data.map", "data.raw")
dat.gwaa <- load.gwaa.data("data.pheno", "data.raw")
phdata(dat.gwaa)$trait <- factor(phdata(dat.gwaa)$trait)
ranger(trait ~ ., data = dat.gwaa)
}
```

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