SL.ranger: Add Ranger wrapper for SuperLearner
In ehkennedy/npcausal: Nonparametric causal inference methods
Description
Usage
Arguments
Value
References
Description
SL.ranger is a wrapper for SuperLearner that adds the fast random forests method ranger.
Usage
1
Arguments
Y
outcome vector.
X
covariate dataframe for training.
newX
covariate dataframe for predictions.
family
link function (currently only supports "gaussian" identity link).
Value
Predictions and fits from ranger.
References
Wright MN, Ziegler A (2016). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software.
ehkennedy/npcausal documentation built on Feb. 26, 2021, 2:43 a.m.
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Description Usage Arguments Value References
Description
SL.ranger is a wrapper for SuperLearner that adds the fast random forests method ranger.
Usage
1 |
Arguments
Y |
outcome vector. |
X |
covariate dataframe for training. |
newX |
covariate dataframe for predictions. |
family |
link function (currently only supports "gaussian" identity link). |
Value
Predictions and fits from ranger.
References
Wright MN, Ziegler A (2016). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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